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Article

Exoplanets Catalogue Analysis: The Distribution of Exoplanets at FGK Stars by Mass and Orbital Period Accounting for the Observational Selection in the Radial Velocity Method

1
Space Research Institute (IKI), Russian Academy of Sciences, Moscow 117997, Russia
2
LATMOS/IPSL, Université de Versailles Saint-Quentin, 11, Boulevard d’Alembert, F-78280 Guyancourt, France
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(2), 353; https://doi.org/10.3390/atmos14020353
Submission received: 21 December 2022 / Revised: 1 February 2023 / Accepted: 3 February 2023 / Published: 10 February 2023
(This article belongs to the Special Issue Planetary Atmospheres: From Solar System to Exoplanets)

Abstract

:
When studying the statistics of exoplanets, it is necessary to take into account the effects of observational selection and the inhomogeneity of the data in the exoplanets databases. When considering exoplanets discovered by the radial velocity technique (RV), we propose an algorithm to account for major inhomogeneities. We show that the de-biased mass distribution of the RV exoplanets approximately follows to a piecewise power law with the breaks at ~0.14 and ~1.7 MJ. FGK host stars planets group shows an additional break at 0.02 MJ. The distribution of RV planets follows the power laws of: dN/dm α m−3 (masses of 0.011–0.087 MJ), dN/dm α m−0.8…−1 (0.21–1.7 MJ), dN/dmm−1.7…−2 (0.087–0.21 MJ). There is a minimum of exoplanets in the range of 0.087–0.21 MJ. Overall, the corrected RV distribution of the planets over the minimum masses is in good agreement with the predictions of population fusion theory in the range (0.14–13 MJ) and the new population fusion theory in the range (0.02–0.14 MJ). The distributions of planets of small masses (0.011–0.14 MJ), medium masses (0.14–1.7 MJ), and large masses (1.7–13 MJ) versus orbital period indicate a preferential structure of planetary systems, in which the most massive planets are in wide orbits, as analogous to the Solar system.

1. Introduction

The statistics of existing exoplanets are modified by observational biases, and differ from the statistics of detected exoplanets, e.g., as it is directly obtained from exoplanet catalogue [1] (other active catalogs such as http://exoplanet.eu/ (accessed on 30 June 2022) include the same confirmed planets, with a few exceptions. We chose the NASA Exoplanet Archive, and other catalogs can be used. We hope some differences in data content will be irrelevant for the presented analysis, but the verification is left for future work). The detection capability of a particular survey does not have the same response for all planet types and for all host star types. The observational bias factor for a certain type of planet depends mainly on the characteristics of the instrument dedicated to a given survey and on the duration of the survey. When making and studying the statistics of exoplanets, one should take into account the inhomogeneity of the data of the various surveys in the published archives (open databases). For exoplanets discovered with the radial velocity (RV) technique, the data inhomogeneity is mainly caused by differences in the sensitivity of spectrographs, the activity level of host stars, the duration of observations, the number of RV measurements (coverage of the RV orbital phase), the efficiency of applied data processing technique, and planets’ multiplicity (detectability in multi-planet systems is significantly harder than in single-planet systems).
We start our de-biasing principle at the upper level: we accept the exoplanet detection event as it is listed in the catalogue [1]. We understand a possible discussion about whether the catalogue detection fact by most RV surveys could provide the necessary input needed to support the completeness of statistical analysis de-biasing. Logically, the proposed de-biasing on a catalogue level cannot be complete, perhaps it cannot resolve important fine details, but surely it can recover important statistical inhomogeneities at the top level and drastically improve the raw catalogue statistics. To validate our approach, we shall refer the reader to compare both the biased and the obtained de-biased dependencies with alternative (i) cosmogony models of, e.g., the population synthesis [2,3] and (ii) with the transit exoplanets statistics, e.g., [4]. We intend to demonstrate here a reasonably good correspondence (with both (i) and (ii)) for the obtained de-biased mass distribution, while the raw (biased) distribution falls short of the number of light mass planet more than in two orders.
Operating with the catalogue data, we have to account for the overall statistical distribution of planetary masses and periods which are dependent on the mass of host star. This concerns both overall planet abundance and the architecture of planetary systems (e.g., [5,6,7]). Therefore, we select the group of FGK host stars for additional analysis.
Second, we do not want to mix the host stars that were targeted in blind RV surveys with those that were chosen only because of follow-up of transiting planets, e.g., WASP-8, Kepler-56, Kepler-94, and Kepler-424. These host stars and planets were excluded from the analysis to improve such an inhomogeneity for the de-biased statistics.
Transiting planets whose masses have been measured by the radial velocity technique are subject to other observational selection biases (in particular, (i) the probability of a transiting configuration is reciprocally proportional to the distance between the planet and the star, (ii) transiting planets discovered by ground surveys are mostly giant planets because the Earth’s atmosphere makes shallow transits invisible). Obviously, the transiting planets with measured mass should be considered separately.
Consequently, within some observational surveys, planets with certain properties (e.g., the orbital period and the minimum mass (M·sin i, where M is the true mass of a planet and i is the angle between the perpendicular to the orbital plane of a planet and the line of sight) can be detected, while the other surveys fail to find them. For example, a low-mass planet orbiting a low-active star can only be detected with a high-precision spectrograph rather than with a less sensitive instrument. At the same time, a low-mass planet orbiting a quickly rotating active star cannot be detected even with a high-precision spectrograph. Finally, to detect long-period planets, the radial velocity of a host star should be measured over a long period of time, sufficient for the planet to cover a large part of its orbit. Massive planets on close orbits around their host stars may be detected within almost any observational survey. At the same time considerable efforts are required to detect planets with low masses or large orbital periods, these planets can be observed only by a few surveys, while the other surveys will miss them. As a consequence, the real (unbiased) joint statistical distribution of RV planets over minimum masses m = M·sin i and orbital periods P (i.e., on the m−P plane) will differ from the observed (biased) distribution.
The purpose of our study is to propose and study a method to simultaneously homogenize several published surveys, in order to retrieve (or approach as much as possible) the true de-biased mass/period distribution of exoplanets.
We derived the observed and regularized exoplanet mass distribution from a sample of known planets detected through the RV method from a variety of different surveys, which are considered initially sufficiently inhomogeneous. The methodology adopted to compute the regularizing detectability-window matrix (W) is inherently simplistic, taking into account only the total time of observations and the scatter of the RV measurements (Equations (5a) and (5b)). The proposed methodology remains affected by numerous factors, which impacts the dependences in fine detail, but these do not affect the conclusions. To analyze such a large sample of data, we used a simplified approach based on the planet detectability event recorded in the exoplanet database. While specific methods reflecting planetary signals in the detection pipelines techniques, such as using Lomb-Scargle periodogram, to measure RVs in time series, remain superfluous for us, planned for future analysis, so currently they are not captured in detail.
Within a certain degree of precision, the mass distribution of the transiting planets does not depend on the spectral class of their parent stars if one considers planets in stars of spectral classes F, G, and K. [8], so we combine a group of FGK host stars RV planets for an additional study.
In some studies of exoplanetary statistics, the inhomogeneity of observational data was ignored. For example, Butler et al. (2006) [9] constructed the projective-mass distribution of 167 exoplanets known at that time and approximated it with a power law dN/dmm−1.1 but did not take into account the difference in detectability between the various surveys. Marchi, 2007 [10] studied the taxonomy of 183 planets ignoring any observation selection. Tabachnik and Tremaine (2002) [11] analyzed 72 planets, looking for a solution in the form of a power law using the maximum likelihood method. They found that the distribution of planets by masses and orbital periods follows dN = Cmα Pβ(dm/m)(dP/P), with α = 0.11 ± 0.10, β = −0.27 ± 0.06, but poorly describes the distribution of massive planets and brown dwarfs. Marcy et al. (2005) [12] attempted to solve this problem by considering only the planets which were detected at the Lick and the Keck observatories with the spectrographs of nearly equal instrumental errors in single RV-measurements (about 3 m/s); as a result, they eventually examined 104 planets out of 152 known at that time. Marcy et al. (2005) [12] found that the distribution follows a power law dN/dmm−1. When considering the distribution of planets with orbital periods P from 2 to 2000 days and minimum masses m from 0.3 to 10 Jupiter masses ( MJ), Cumming et al. (2008) [13] introduced the survey-completeness factor and found that the joint distribution of 182 RV planets by masses and orbital periods obeys a power law dN = C1 × m−0.31±0.2 × P0.26±0.1dln(m)dln(P) (where C1 is a constant), which corresponds to a projective-mass distribution dN/dmm−1.31±0.2. To analyze the mass distribution of planets orbiting 166 Sun-like stars observed at the Keck observatory with the High Resolution Echelle Spectrometer (HIRES), Howard et al. (2010) [14] introduced the completeness function C(m, P) as a fraction of stars that for sure do not have nearby planets with specified values of the period and the minimum mass. They found that the projective-mass distribution of planets with periods shorter than 50 days can be approximated by the power laws dN/dlog(m) ∝ m−0.48+0.12/−0.14 or dN/dmm−1.48+0.12/−0.14. Jiang et al., 2010 [15] mentioned the need to correct for the observational selection associated with the detection limit of the different surveys. They resumed the coupled mass-period exoplanets’ distribution as a power law dNm0.099±0.055 × P0.13±0.04dm/m dP/P.
We study the de-biasing method against observational selection of RV exoplanets in mass and orbital period statistics. The de-biasing method based on exoplanet catalogue data accounts for major essential selection factors in RV exoplanet detection. Alternatively, a more logical and straightforward de-biasing method is based on raw data analysis from host star observation used to determine Keplerian residuals (taken not from a catalogue but from spectroscopic data). These residuals can be used to determine an “overall” de-biased exoplanet occurrence rate. Thus, from the raw data, one claims the completeness function of a star and, therefore, a detection fact of an exoplanet in a star system. For example, this procedure can be implemented using a Lomb-Scargle periodogram math algorithm, as constructed on RV data by adding and subtracting a real or a dummy planet with a given minimum mass m and period P.
We have tested this approach and summarized a difficulty with reliable exoplanet non-detection criterion. Not on the level of mathematics but on the level of raw data. The uncertainty is caused by the following: (i) Only a limited number of radial velocity measurements is available from various spectrographs. (ii) Need to filter star activity (rotation accounting). (iii) Need to filter observing biases factors, such as an inducement of possible planets and unique observational samplings. It is not uncommon for planets discovered by one research group not to be confirmed by another one (alfa Cen B b, Glise 581 d, g, f, HD 41248 b, c, etc.).
Indeed, the detection of exoplanets remains a “piecemeal” product. Often it is derived from an original unique hypothesis, often from a non-unified combination of several random factors. Therefore, the de-biasing and homogenization of datasets proposed here is based on a much simplified, surely imprecise model: we analyze exoplanet detection event on catalogue basis. One of the aims why we generalise analysis within {K/σ(O-C), P/T} parametric space is not only to exclude random factors but to account for systematic factors (where K is reflex motion, σ(O-C) is residual “observation minus calculation”, P is orbital period, T is time span.) We extrapolate a detection event over other planets with generic characteristics {K/σ(O-C), P/T}, and using that, we state whether the planet can or cannot be detected.
Let us consider a possible criticism of how to account for the number of RV observations in a given periodogram. One of the parameters of the proposed model (γ, see Section 2.1.1, Formula (5b)) is actually the threshold value of K/σ(O-C) at which a planet can still be detected in a given set of radial velocity measurements. We have relied on the formula for identifying a periodic signal in noise S/N = sqrt(n)· K/ΔV, where S/N is the signal-to-noise ratio, which must be 10 or higher for reliable detection, n is the number of measurements, ΔV is the error of a single measurement. Sources of noise are also the star activity and the possible presence of other planets, so instead of ΔV value we use σ(O-C), which is a measure of the total noise. Hence the threshold value K/σ(O-C) = γ = 10/sqrt(n), which does depend on the number of radial velocity measurements. However, in most cases the number of radial velocity measurements leading to the discovery of a low mass planet (<0.14 Jupiter masses) is in the range 100–400, which corresponds to the value of γ = 0.5…1.0, on average 0.75, which is accepted in our model. On the other hand, massive planets correspond to a large value of K (tens and hundreds of meters per second), so 20–30 measurements of radial velocity are sufficient to detect such planets. For N = 25 γ = 2, which is accepted in the model for massive planets. It is possible to calculate an individual threshold value K/σ(O-C) = γ for each star. However, at this stage we believe that determining the exact value of γ for each star is redundant. The fact of a publication presenting a new RV planet also depends on random factors. In addition, authors may take their time to publish a reliable RV signal, seeking to make sure of its planetary nature, or, on the contrary, rush to publish it and present an unreliable planet, which will not be confirmed later.
Therefore, the number of RV observations is encoded in catalogue data if one considers detection event. Several random factors remain averaged on overall statistics. Additional signals in the RV data from stellar activity, inducement of possible planets, observational sampling are similarly encoded.
The de-biasing based only on stars with planets is evidently incomplete. To account for stars without planets, we use the mathematic approach in Section 2.1.1. We analyzed the observed stars without planets considering the ratio of the sum of stars in which a planet with a given mass and orbital period {m, P} can be detected to the total sum of all observable stars.
From Tuomi et al. (2019) [16] (published online in arxiv.org), we borrowed the method of combining single “windows” (completeness functions of each star) into a common “window” (matrices W and V, Section 2.1.1). Hence, Tuomi et al. (2019) [16] applied this method to the actual radial velocity data of stars and carefully considered other factors, including stellar activity indicator data, which we apply in our proposed method.
We borrowed from Petigura et al. (2013a) [17] the methodology for calculating the true number of planets with a known observed number of planets and the completeness function. The detailed calculation of the completeness function in Petigura et al. (2013) [17] and this paper differ because Petigura et al. (2013) [17] considers the distribution by radii and orbital periods of the transiting Kepler planets but not the distribution by the minimum masses of the RV planets (Section 2.1.1).

2. Materials and Methods

2.1. Method for Considering Several Surveys

2.1.1. The Concept of a Detectability Window Algorithm

Among the approaches used to regularize the inhomogeneous data on RV exoplanets from the NASA Exoplanet Archive [1,18], there is one method which we called the “detectability window” regularization algorithm. Presently, we are developing this approach, which was proposed by Tuomi et al. (2019) [16] to study the occurrence rate of planets of different types around M-dwarf stars.
Tuomi et al. (2019) [16] analyzed 23,473 individual measurements of the radial velocity of 426 M dwarfs, which were performed with the High Accuracy Radial velocity Planet Searcher (HARPS), HIRES, Planet Finder Spectrograph (PFS), Ultraviolet and Visual Echelle Spectrograph (UVES), and other instruments.
To account for the differences between the surveys in duration and sensitivity, Tuomi et al. (2019) [16] have introduced the detection probability function pim, ΔP) for each of the data sets (in fact, for each observed star). This function takes discrete values of 1 or 0 depending on whether the obtained data would allow a planet with the minimum mass and the orbital period in a range of (Δm, ΔP) to be detected near a specified host star or not, respectively. This calculation must consider the host star’s mass and the accuracy (in m/s) achieved by the survey. The non-detection of a planet occurs either because the amplitude of the reflex motion K (in m/s) is below the accuracy of the survey, or because the duration of the monitoring survey is too short w.r.t. the period. The overall planet detection probability function fpm, ΔP) was finally determined by summing up all pim, ΔP) over the observed stars (N = 426) and dividing the latter by N (In [16], the probability function of detection was determined as f p Δ m ,   Δ P = 1 1 N i = 1 N p i Δ m ,   Δ P . This is because they have used the reverse definition of pi: pi = 0 for detection, pi = 1 for non-detection.)
f p Δ m ,     Δ P = 1 N i = 1 N p i Δ m ,     Δ P .
The ranges of the minimum masses and the orbital periods (Δm, ΔP) were represented within a grid of, e.g., 8 × 8, where they cover the intervals m = 1–103 Earth masses (ME) and P = 1–104 days, respectively.
Tuomi et al. (2019) [16] were focused on determining the occurrence rate of exoplanets at M dwarfs rather than analyzing their distribution over masses or orbital periods. However, we have modified their proposed method the study of the joint distribution over masses and orbital periods for RV planets orbiting stars of all types as well as in the selection of FGK host-star group.
For the explanation and, therefore, some generalization of Tuomi et al. (2019) [16] methodology, we refer the reader to Appendix A.
The amplitude K of the sinusoidal variation of RV (in the case of circular orbits) is a function of Mstar, the mass of the planet Mplanet, and its period Pplanet:
K m / s = 203.25   M planet M star 2 / 3 P planet 1 / 3 sin i ,
where the numerical constant 203.25 is needed when the Mplanet is in units of Jupiter mass MJ and Mstar is in units of solar mass, and K is needed in m/s to be compared to the accuracy (or threshold detection limit) of a particular spectrometer and survey. The angle inclination of the system is angle i with the line of sight. For RV surveys, the product Mplanet·sin i, the minimum mass, is determined. This equation shows that for a given planet with a given period, the amplitude K of the reflex motion is smaller for a heavier star. Therefore, the same planet (mass, period and inclination) may be detected by a particular survey around a light host star, and escape detection around a heavier host star with the same instrument and survey. This introduces a bias that we are able to estimate (see below) and, therefore, correct for it (de-biasing). This bias depends on the particular sample of stars monitored by a survey (the mass distribution of host stars in the survey) and RV performances of this survey. Our procedure also allows accounting for different performances of the different surveys (homogenization). It relies on the assumption that the true mass distribution of planets is independent of the host star’s mass.

2.2. Method to Construct a Detectability Window

2.2.1. The Concept of a Detectability Window Algorithm

To take into account the data inhomogeneity in the archives of RV-exoplanets, we introduce the notion of a “detectability window”. The detectability window is a matrix of dimension (n × n) in the mP plane, the elements of which represent the probability of detecting a planet with the desired values of the minimum mass and the orbital period W(m, P). The W matrix dimension (n × n) can be chosen by Sturges’ rule [19] to compute the histogram bins number. In other words, the matrix W describes for one given survey (for observed star) or for the merged ensemble of surveys, the fraction of existing planets that are actually detected with given values depending on m and P. The observed (biased) distribution of planets in the mP plane (a two-dimensional histogram) N0(m, P) can be obtained by element-by-element scalar multiplication of the real (unbiased) distribution N(m, P) by the detectability window W(m, P):
N0(mi, Pj) = N(mi, Pj) × W(mi, Pj),
where i and j run from 1 to n.
Consequently, the real (unbiased) distribution can be obtained from the observed (biased) distribution by dividing each element of the observed two-dimensional histogram by the corresponding element of the detectability window, if the latter is not zero:
N(mi, Pj) = N0(mi, Pj) × (1/W(mi, Pj)), if W(mi, Pj) ≠ 0.
In other words, to construct a distribution less distorted by the observational selection, we take each of the actually detected RV planets in the mP plane with a statistical weight inverse to the corresponding value of the planet detection probability, i.e., the matrix element W(m, P) of the detectability window. This method of correcting the observations from a known bias factor is similar to the approach of Petigura et al. (2013a) [17] for the size distribution of transiting planets, the similar de-biasing of RV planets was used in [20]. In the case of transiting planets, for a circular orbit of radius a, it is simply the angle R*/a at which the host star with radius R* is seen from the orbit. Hence, one detected planet may be counted as a number larger than several tens. (Note the similarity to our problem: for transiting planets, the radius of the star R* is taken into consideration; for RV planets, it is the mass of the star which is necessary to be known).
It may not be very obvious that the results of several surveys with different sensitivities may be merged rather simply. The demonstration is given in Appendix A and Appendix B.
To construct the detectability window matrix W(m, P), we consider RV planets with orbital periods and minimum masses ranging from 1 to 104 days and from 0.011 MJ to 13 MJ, respectively (The window’s boundaries may be set arbitrarily. In the following, we use the windows with the other boundaries as well).
We divided each of the domains into twelve bins, equal widths when expressed in logarithms so that the resulting mP plane was split into 144 cells. In the middle of each of these cells, we place an artificial (dummy) planet, i.e., 144 artificial planets are assumed. For each cell with the dummy planet we compute the probability of its detection, the method for calculating which is given below.
To estimate whether each of the artificial planets could have been detected by the considered surveys we need two additional characteristics—total observation time T and average deviation from the best Keplerian curve σ(O − C) in m/s—which are absent in the NASA Exoplanet Archive [1], but would give an estimate of the actual accuracy of the instrument/survey (in m/s). For each of the real RV planets or planetary systems (in the case of multiplanetary systems), we take as a basis the source (published study) where the time T of observations is longest while the average deviation from the best Keplerian curve σ(O − C) is smallest. All sources are listed in Table A2. For each of these sources, we estimate whether it could detect each of the 144 artificial planets. We assume that an artificial planet will definitely be detected if two conditions are fulfilled simultaneously:
  P δ   T K γ   σ O C 5 a 5 b
These conditions mean the following. According to inequality (5a), the orbital period P of an artificial planet should be less than the product δ × T, where T is the total time of observations and δ is a numerical multiplier of the order of unity, which will be defined in Section 2.2. According to inequality (5b), the semi-amplitude K of the reflex motion (in m/s) induced by an artificial planet in the radial velocity of a host star should be greater than the product γ × σ(O − C), where γ is another numerical factor of the order of unity, which will also be defined in Section 2.2.2.
In [20], the detectability-window matrix W was constructed for 547 stars, each of them has at least one RV planet. For each star we calculated the reflex motion K by Equation (2) that each artificial exoplanet could induce on the star. If the artificial exoplanet satisfies both conditions (5a) and (5b), the value in the corresponding cell of the matrix W(m, P) is increased by one, and the algorithm proceeds to the next host star of the surveys (out of 547 stars in total). Once the examination of observations of all host stars has been accomplished, the resulting matrix was normalized by the number of stars (547), and the values in cells of the matrix W take values between 0.0 to 1.0, corresponding to the detection probabilities, where zero or unity means an absolutely opaque window or an absolutely transparent one, respectively (i.e., the planet cannot be detected, or would be certainly detected by the surveys, respectively).
Figure 1 shows an example of the detectability window W in the form of a map with cells of different brightness corresponding to the planet detection probability wij. The probability values wij and the numbers of planets from the NASA Exoplanet Archive [1], which occur in a specified cell Wm, ΔP), are indicated in each of the cells by the lower and upper numbers, respectively. In addition, the positions of these detected planets in the mP plane are shown by red dots. In Figure 1, the detectability window was constructed for the coefficient values δ = 2 and γ = 0.8 in (5a) and (5b), respectively, i.e., by assuming that an artificial planet will be detected if its half of orbital period is less than the total time of observations T and the semi-amplitude of the induced reflex motion in the radial velocity is greater than eight-tenths of the average deviation from the best Keplerian curve σ(O − C). For a more accurate δ and γ choice, we refer the reader to Section 2.2.2.
However, the detectability window proposed above by the W matrix is not constructed for all observed stars but only for stars with planets. Therefore, the correction by W is neither complete nor accurate, as it does not account for possible low-mass planets orbiting stars by which no planets have been detected.
Without loss of generality, we can relax this inconsistency by the following algebra.
We consider L- number of observation programs (surveys), where: The 1-st one can only detect the heaviest of the artificial planets with minimum mass m1; The 2-nd survey detects planets with masses: m1 and m2 (m1 > m2); etc.; Finally, the L-th survey is able to detect planets of all masses: m1, m2, …, mL. Assume, the 1-st survey observes N*1 stars, the 2-nd—N*2 stars, etc., the L-th survey—N*L stars. The corresponding occurrence rates of planets with masses m1, m2, …, mL are denoted by f1, f2, …, fL.
Logically, the 1-st survey finds f1N*1 planets with mass m1, the 2-nd survey finds f1N*2 planets with mass m1 and f2N*2 planets with mass m2, and so on, until the L-th survey detects f1N*L planets with mass m1, f2N*L planets with mass m2, …, fLN*L with mass mL.
Counting the number of detected planets results in: The heaviest planets with mass m1 will be detected f1N*1 + f1N*2 + … + f1N*L = f1∙(N*1 + N*2 + … + N*L) times. The number of planets with m2 mass is f2∙(N*2 + … + N*L). Finally, the number of lightest planets with mL mass is fLN*L.
However, it is important that in reality, the quantity of planets with m1 mass will be f1∙(N*1 + N*2 + … + N*L), the quantity of planets with m2 mass will be f2∙ (N*1 +N*2 + … + N*L), and so on, and the quantity of mL mass planets will be fL∙ (N*1 +N*2 + … + N*L).
To convert the observed numbers of planets into their real numbers, the detection efficiency values (elements of the detectability window matrix V(←W)) shall be following:
v1 = 1, (for planets with m1 mass);
v 2   =   f 2 ·   ( N 2   +     +   N L / ( f 2 ·   ( N 1   +   N 2   +     +   N L ) ) = = ( N 2 + + N L ) /   N ,   ( for   planets   with   m 2   mass ) ;
v L = N L /   N ,   for   planets   with   m L   mass .
In other words, each coefficient of the detectability window matrix V is the ratio of the sum of stars in which a planet with a given minimum mass and orbital period can be detected to the total sum of all observed stars.
Directly from the NASA Exoplanet Archive [1], we know neither the number of observed stars in each artificial survey N*i nor the occurrence rates of planets fi in the mass domain between mi−1mi+1. However, instead, we do know the number of stars with detected planets of masses m1, m2, …, mL. The number of stars that have the planets detected in the i-st survey (denoted as) Si:
S1 = d1·f1N*1, (detected by the 1-st survey);
S2 = d2 · (f1N*2 + f2N*2) =
= d2·N*2 (f1 + f2), (detected by the 2-nd survey);
SL = dL·N*L…(f1 + f2 + … + fL), (detected in the L-th survey).
By definition, the coefficient di is the ratio of the number of stars to the number of observed planets orbiting these stars. For small f, the coefficient d is close to 1 (as a rule, a star has only one known planet), but as f increases, d decreases, and it tends to the value inverse to the average number of planets per star. For giant planets of 2–13 Jupiter masses considered in this paper, d = 0.931 (248 planets in 231 stars), and for planets with masses less than 0.1 Jupiter masses d = 0.676 (145 planets in 98 stars). To exclude the additional factor d, when constructing the detectability window matrix for a multiplanet system W , ˜ we further consider the star as many times as it has known planets. In this case, Equation (7) can be re-written as:
S ˜ 1 = f 1 · N 1 ,   detected   by   the   1 - st   survey ;
S ˜ 2 = f 1 · N 2 + f 2 · N 2 = = N 2 · f 1 + f 2 ,   detected   by   the   2 - nd   survey ;
S ˜ L = N L ·   f 1 + f 2 + + f L ,   detected   in   the   L - th   survey .
It follows from the statements above, that is possible to re-write the detectability window matrix W ˜ (see below (9a)–(9c)) that accounts only for detected planets, into the detectability window matrix V, which takes into account all the observable stars (6a)–(6c). One can realize that W ˜ had the matrix elements w ˜ 1…L along the minimum mass m direction, which are:
w ˜ 1 = 1 ,   for   planets   with   m 1 mass ;
w ˜ 2   =   ( S ˜ 2 +     +   S ˜ L ) / ( S ˜ 1   +   S ˜ 2   +     +   S ˜ L ) = ( S ˜ 2   +   S ˜ L ) / Σ   S ˜ ,   for   planets   with   m 2 mass ;
w ˜   =   S ˜ L /   S ˜ ,   for   planets   with   m L mass .
The corresponding Formulas (6a)–(6c) and (9a)–(9c) for vi and w ˜ i are structurally identical, but they have the different N*i and S ˜ i metrics, where N*i counts observed stars, while S ˜ i counts detected planets.
We further express S ˜ i through the matrix elements w ˜ i (from (8b));:
S ˜ 1 =   ( 1   w ˜ 2 ) · S ˜ ,
S ˜ 2   =   ( w ˜ 2 w ˜ 3 ) · S ˜ ;
S ˜ i   =   ( w ˜ i     w ˜ i + 1 )   · Σ S ˜ ,
S ˜ L   =   w ˜ L   · Σ S ˜ , from   7 b ,   at   boundaries   we   assume   w 1   =   1   and   w L + 1 = 0 .
From (8a)–(8c), we express the number of observed stars N*i through the number of the planets detected in the i-st survey S ˜ i:
N i = S ˜ i / k = 1 i f k .
Finally, vi is found via w ˜ i:
v 1 = w ˜ 2 = 1 ,   ( for planets with   m 1   mass )
v 1 = 1 1 / ( 1 +   N 2 / N 1   +     +   N L / N 1 ) ,   for planets with   m 2   mass ) = 1 1 / ( 1 +   ( w ˜ 2     w ˜ 3 ) / ( 1     w ˜ 2 ) f 1 / f 1 + f 2 + + w ˜ L / ( 1   w ˜ 2 ) · f 1 / f ) ;
v i + 1 = v i N i / N = v i 1 / j = 1 L N j N i , ( where N j / N i = S ˜ j / k = 1 j f k / S ˜ i k = 1 i f k , N j / N i = S ˜ j / S ˜ i · k = 1 i f k / k = 1 j f k = w ˜ j w ˜ j + 1 / w ˜ i w ˜ i + 1 · k = 1 i f k / k = 1 j f k )
v L + 1 =   w ˜ L + 1   =   0
For example, if fi = constant (that makes “flat” distribution on a logarithmic scale d N d l o g m , corresponding to the mass distribution d N d m ∝ m−1), then k = 1 i f k / k = 1 j f k = i/j.
If dN/dlog mm−α (which corresponds to the mass distribution of d N d m m−α−1), fi = f1·mstepi−1,
k = 1 i f k / k = 1 j f k = ( m step i + 1 1 ) / ( m step j + 1 1 ) ,  
where mstep is (mi/mi+1)α.
Without knowing N*i (which defines the total number of observed stars in each survey, including those without planets), we cannot determine the fi occurrence rate. However, since fi enters expressions for vi only as relations of the form k = 1 i f k / k = 1 j f k , we can compute vi by restoring the distribution of planets by mass w ˜ i, by a f(m) guess, as a function of planetary mass in a fixed orbital period domain.
Examples of detectability windows W ˜ (for multiplanet systems) and V (for stars with and without planets) are shown in Figure 2a,b, respectively.
In aid of understanding, a toy model simplified with L = 2 (with two types of planets observed by two surveys) is discussed and illustrated in Appendix B.
The approach above of Equations (6)–(12) is applicable if all the surveys can be arranged in a monotonic sequence according to increasing (or decreasing) detection efficiency of exoplanets. It is possible if the detection efficiency depends monotonically on only one parameter, e.g., the planet mass m (determined by single condition). However, in the general case, the detection efficiency of exoplanets is a function of several variables, hence, in the present paper, we stretch them to the two major parameters (m, P) in (5). We note that for some set of regions on the plane (m, P), one of the conditions accounting for the survey arranged either along planet mass m or orbital period P as the only parameter is always fulfilled, to make the approach described above (6–12) is applicable. Let us comment that (6)–(12) approach is also applicable by replacing m to P, where it is required. Suitable here one more comment is that all the notations, such as those used by forming the detectability windows V(m, P) (for stars with and without planets, Equation (6)), W(m, P) (accounting for a single planet in systems, Equation (8)) and W ˜ (m, P) (for multiplanet systems, Equation (8)) can be similarly determined as well for the discrete (centered) mi and Pi values as for their sets collected in intervals Δm = [mimi+1] and ΔP = [PiPi+1].
To ensure that the constructed various detectability windows W(m, P), W ˜ (m, P) (8) and V(m, P) (6) do actually reflect the current ability of the RV technique to detect exoplanets, we should specify the parameters γ and δ (5) more accurately in each mass- and orbital period domain.

2.2.2. Parameters of the Detectability Window Regularization Algorithm

The values initially assumed for the coefficients in (5a) and (5b) (and illustrated in Figure 1 and Figure 2) γ = 0.8 and δ = 2.0, do characterize the majority of discovered planets, the orbital periods of which are longer than the full time of their observations T (e.g., HD 181234 b [21]). Moreover, some planets induce sinusoidal fluctuations in the radial velocity of a host star with a semi-amplitude K smaller than σ(O − C) (e.g., GJ 433 d [22] and HD 26965 b [23]), which implies γ < 1.0.
To determine the coefficient δ, we plot the distribution of RV planets over the ratio P/T in the form of a histogram (see Figure 3).
According to Figure 3, P/T < 1.5 for the majority of RV-planets (97.7%), and P/T < 2.5 for 99.1% of them. When choosing a value for the coefficient δ, we should take into account that those planets which have passed only a part of their orbit around host stars during the time of observations may also be detected. The smaller the fraction of the orbit the planet has passed, the less reliably its minimum mass and orbital period can be determined. If this fraction of the orbit is small, the Kepler curve degenerates into a linear or quadratic drift of the star’s radial velocity, which indicates the presence of long-periodic bodies in the system, but does not allow their period and mass to be determined. For example, planets with P/T > 2.5 (HD 221420 b, Pr0211 c, HAT-P-17 c, HR 5183 b, HD 190984 b, and HD 133131 B b) are in highly eccentric orbits. During the observational period, they have already passed through pericenters, when the orbital velocity changes rapidly. If the same planets had been observed during their apocenter passages, they would have been apparently missed as a poorly defined source of a linear drift in the radial velocity of their host stars. For most planets with P/T > 2.5, the orbital periods and the semi-major axes of orbits are poorly determined.
It is also worth noting that the variation of the coefficient δ mostly influences the detection probability of planets with the longest periods, while the influence of coefficient δ on the detection probability of short- or medium-period planets is very weak. We will further set δ = 2.0; i.e., we accept the condition that a planet can be detected if it has completed at least a half revolution on the orbit around its host star for the entire period of observations.
Next, let us consider whether it is possible to choose a universal value for the coefficient γ that would be valid for detecting most RV planets so that γ < σ O C K .
The distribution of RV planets over the ratio K/σ(O − C) in the form of a histogram is shown in Figure 4.
For the majority of planets (95.1%), K/σ(O − C) > 1.0, i.e., the semi-amplitude K of fluctuations in the radial velocity of a host star, which are caused by the gravitational influence of the planet, is greater than the average deviation σ(O − C) from the best Keplerian curve. However, for 34 planets out of 695 (4.9%), 0.5 < K/σ(O − C) < 1.0. As a universal approximation, we may set γ = 0.8; although, in Section 3, for each of the intervals of the minimum masses m, optimal values of γ will be chosen.
Nevertheless, the detectability window matrix W (in Figure 1) contains zero probability fp = 0 in the following eight elements (or the map cells with numbers i and j along the horizontal and vertical axes, respectively): W11, W21, W31, W12, W22, W13, W14, and W15. These “degenerate” cells correspond to planets of small masses and large orbital periods. We call this degenerate region “a blind spot”. It is impossible to detect planets from the blind spots (with the corresponding parameters Δmi and ΔPj) even with state-of-the-art tools, and unfortunately, their number remains unknown for constructing statistical patterns.
Note that since for wij = 1 w ˜ ij = 1 and vij = 1, and for wij = 0 w ˜ ij = 0 and vij = 0, the blind spot area does not change when moving from the imprecise matrix W to the refined matrix V (see Figure 2).

3. Results

3.1. De-Biased Histograms of the Projective-Mass Distributions of RV Planets

3.1.1. The Technique for Constructing the Projective-Mass Distributions of RV Planets and Their Histograms

We analyze the projective-mass distribution of RV planets N(m) = dN/dm with the example of the detection probability matrices—the detectability windows W, W ˜ , and V —shown in Figure 1 and Figure 2a,b, correspondingly.
First, using the detectability windows W (accounting for stars with single planets), we write the numbers of planets N in the map cells (the upper numbers shown in the cells) as the matrix N0(12 × 12) and use Equation (4) to regularize the data and correct the observational selection. To pass from the two-dimensional non-corrected (biased) distribution N0m, ΔP) to the corrected mass distribution of RV planets in the form of a histogram N(m) = dN/dm, we sum up the elements of the matrix N0 × (1/W) (see (4)) by columns, i.e., by orbital periods:
N ( m ) = N ( Δ m ) = j = 1 12 N 0 Δ m , Δ j P   ×   ( 1 / W ( Δ m , Δ j P ) ) .
However, in the blind spot (cells W11, W21, W31, W12, W22, W13, W14, and W15), the elements of the matrix N cannot be defined due to the division by zero. Because of this it is impossible to construct a mass distribution for the whole mP plane, i.e., for i and j both running from 1 to 12. There are two ways to overcome this problem:
(A). We cover the planets of all masses, but limit ourselves to those with short orbital periods, i.e., i = 1–12 and j = 7–12:
N A ( m ) = N ( Δ i = 1 12 m ) = j = 7 12 N Δ i m ,   Δ j P   ×   ( 1 / W ( Δ i m ,   Δ j P ) ) .
(B). We cover planets with all periods, but limit ourselves to more massive planets, i.e., i = 4–12 and j = 1–12:
N B ( m ) = N ( Δ i = 4 12 m ) = j = 1 12 N Δ i m ,   Δ j P   ×   ( 1 / W ( Δ i m ,   Δ j P ) ) .
In Figure 5a,c we show the distribution of planets as a histogram NA(m) for all considered masses with the orbital periods Δ j = 7 12 P ranging from 1 to 100 days. This distribution was obtained according to Equation (13b) for several values of γ and δ (γ = 0.65, 0.80, 0.95; δ = 1.5, 2.0, 2.5). Figure 5d show the distribution of planets NB(m) for all considered orbital periods and the masses exceeding 0.065 MJ (21ME); it was calculated according to Equation (13c).
When passing from the integer numbers of planets N0 in Equation (6a) (see the upper numbers in the map cells in Figure 1) to the fractional numbers of planets N in Equations (6b) and (6c), we took into account the error in determining planetary masses by the kernel density estimation (KDE). In this procedure, we used a Gaussian profile or a skewed normal distribution, depending on whether the upper and lower errors are equal (the smoothing technique was described in [24] and presented at length by [25]) or differ in magnitude [26], respectively.
As a first approximation, the mass distribution of RV planets in Figure 5 follows a piecewise continuous power law with breakpoints approximately located at 0.14 MJ and 1.7 MJ (see Figure 5b,d for more precise positions). It is important that the breakpoint positions are independent of selected values of the coefficients γ and δ. The breakpoint positions are determined within an accuracy of the bin width in the histogram.
It should be noted that the projective-mass distribution for planets with periods of 1–100 days significantly differs from that for planets with periods of 1–104 days even in the projective-mass domain, which is common for the both distributions (i.e., (0.063–13) MJ). While the positions of minima (0.14 MJ) coincide in the both distributions, the positions of the maxima are different (~0.5 MJ and ~1.7 MJ for the short-periods planets and the planets with all periods observed, respectively). As it will be shown in Section 4, this suggests that the most massive planets are on wide orbits with periods exceeding 100 days.
As can be seen in Figure 5c, the distribution of planets with periods of 1–100 days does not depend on the choice of the value for δ either (the distributions are the same for δ = 1.5, 2.0, and 2.5). This is due to the fact that, for short-period planets, the whole time of observations always exceeds their orbital periods, i.e., P/T < 1.
Let us state that the breakpoint positions at 0.14 MJ and 1.7 MJ do not depend on the values of the coefficients γ and δ. The slopes of the mass distributions of planets in three mass intervals slightly depend on γ choice. Therefore, we better determine the parameters γ and the power indices α (in N(m) ∝ m−α approximation) in each of the mass intervals. We have analyzed these intervals separately.
The power indices of de-biased mass dependencies within optimal parameters for the intervals between the breakpoint positions are summarized in the Table 1.

3.1.2. The Composite Projective-Mass Distribution of Planets—Comparison to the Mass Distributions from Population Synthesis Theory

In Figure 4, the projective-mass distribution of RV planets corrected with the detectability window regularization algorithm rather accurately follows a power law piecewise. In a domain of (0.011–0.087) MJ (or (3.5–28)ME), the exponent is −3, i.e., dN/dmm−3. In a domain of (0.087–0.21) MJ (or (28–67)ME), the distribution exhibits a minimum, which is deepest in a range of (0.12–0.16) MJ (or (37–50)ME), where the number of planets is 7.7 times smaller than that predicted by the power law with an exponent of −3. In a mass domain of (0.21–2.2) MJ, the distribution follows a power law with an exponent ranging from −0.8 to −1, i.e., dN/dmm−0.8…−1. In a mass domain of (2.2–13) MJ, the distribution may be approximated by a power law with an exponent ranging from −1.7 to −2.0, i.e., dN/dmm−1.7…−2.0.
Due to the presence of the blind spot (zeroed W11, W21, W31, W12, W22, W13, W14, and W15), it is impossible to plot the mass distribution of RV exoplanets in the entire mass range of 0.011–13 Jupiter masses and orbital periods of 1–104 days. However, we obtained a composite mass distribution of the RV exoplanets by putting on one plot the distribution of light planets (0.011–0.21 Jupiter masses) with orbital periods of 1–100 days and the distribution of medium and heavy planets (0.21–13 Jupiter masses) with orbital periods of 1–3981 days. For greater uniformity, we considered only systems with a noise level σ(O − C) < 15 m/s (598 planets). When constructing the distribution of medium and large masses planets, we considered only systems whose total observational time T exceeded 1990.5 days.
In Figure 6a, we superimpose the overlapping parts for the host stars in the mass domain of the distributions in a range of (0.156–0.378) MJ (the right end of the blue curve and the left end of the green one) by 3.75. The coefficient 3.75 was chosen as the ratio of the number of planets in the mass interval 0.156–0.378 of the Jupiter mass with orbital periods of 1–3981 days and 1–100 days.
With the planetary population synthesis, Mordasini (2018) [2] established a theoretical model of the formation and evolution of planets; consequently, the theoretically derived mass distribution of planets may be compared to observations: therefore, both the biased and the de-biased distributions. The predicted mass distribution of planets displayed (by [2] in Figure 10 (top left panel)) is reproduced here in Figure 6 by a dashed black line. In a mass domain of (1–30)ME (or (0.003–0.1) MJ), the distribution follows a power law with an exponent of −2, i.e., dN/dmm−2. In a mass domain of (0.1–5) MJ, the predicted mass distribution follows a power law with an exponent of −1, i.e., dN/dmm−1 (which results in a plateau or even a slight increase along the mass bins expressed in logarithm), and in the domain above 5 MJ the power exponent approaches −2 again. The projective-mass distribution of RV planets corrected with the detectability window regularization algorithm is well consistent with the population synthesis theory [2] in the range of 0.21–13 Jupiter masses. However, in the region of masses less than 0.21 Jupiter masses, the corrected distribution does not agree with the distribution predicted in [2]. However, a new generation of population synthesis models (e.g., [3]) predicts a dN/dmm−3 distribution for planets with masses 5–50 Earth masses. In the mass range 0.087–0.21 MJ there is a deficit of observed planets w.r.t. the theory (the hot Neptunes desert).
In Figure 6b, we analyzed the similar graph as in Figure 6a, but only for FGK host star group in stellar mass domain of 1.00 ± 0.25 Msol. Both graphs show a similarity, but the graph corresponding to solar-like (FGK) host stars shows a decreasing planet number in (0.011–0.02) MJ mass domain. Additionally, the mass distribution slope of planets orbiting FGK host stars fits to m−2.64 ± 0.28 in comparison to m−3 for all planets (orbiting host stars with the masses (0.123–10.8) Msol).

3.1.3. The Minimum in the (0.087–0.21) MJ Domain of Masses

The de-biased projective-mass distribution of RV planets exhibits a contrasting minimum in the mass range (0.087–0.21) MJ. In this range, planets are robustly detected: for planets of lower masses, γ = 0.75, while more massive planets require γ = 1.6. We find reasonable to check whether the observed minimum may be explained by the incorrectly estimated detectability efficiency of planets with masses of (0.087–0.21) MJ, i.e., the incorrectly estimated coefficient γ (the transition from γ = 0.75 to γ = 1.6).
We construct the distribution of RV planets over the ratio K/σ(O − C), i.e., the ratio of the half-amplitude K of the radial-velocity oscillations of a host star to the mean deviation from the best Keplerian curve σ(O − C), for a mass range of (0.087–0.38) MJ (58 planets).
Though it is clear that γ = 0.75 in this domain, we modeled the influence of changes in the coefficient γ on the minimum depth in the projective-mass distribution of RV planets, for which the corrections with γ = 0.75 and 1.6 were used. We considered planets with periods of 1–100 days from systems with a noise level σ(O − C) < 8 m/s. The result is presented in Figure 7a.
As can be seen from Figure 7a, changes in the coefficient γ do not basically change the distribution pattern. In the minimum domain ((0.108–0.135) MJ or (34–43)ME), the number of planets predicted by the power law fitting the range of small masses is seven times larger than the number of planets corrected by the detectability window with γ = 0.75. Moreover, the number of planets in the same domain corrected by the detectability window with γ = 1.6 is in 3.6 times smaller than the number of planets predicted by the power law. Consequently, we conclude that the minimum in the (0.087–0.21) MJ range cannot be explained only by a jump in the γ value.
In Figure 7b, we examined the dependence of the distribution of planets by mass, depending on the orbital period coverage: 10–1000 days, 4.64–464 days, 2.15–215 days, and 1–100 days (we used period ranges equal in logarithmic scale and equal to 100). We can see that the minimum becomes deeper as the orbital periods decrease, suggesting that the mass values in the minimum range correspond to the so-called “desert of hot-Neptunes” (e.g., [27,28]).
It seems possible that if planets of all orbital periods were detected, including periods longer than 1000 days, this minimum would completely disappear and the de-biased mass distribution of RV planets would agree with the population synthesis theory. On one hand, we note that adding de-biased distributions 1–10 days and 10–1000 days of Figure 7b (therefore, complete 1–1000 days) will certainly still show a deficit in this mass range. On the other hand, Neptune and Uranus in the Solar system still could not be detected from outside, because of their large orbit, long period, small induced K reflex motion and very low transit probability. Therefore, this issue deserves a more rigorous separate analysis, deleted to future for the time being.

3.1.4. The Maximum in the (6–9) MJ Domain of Masses

The projective-mass distribution of planets with masses in a domain of (1.7–13) MJ can be represented as a sum of the power law and the maximum (bump) in a range of (6–9) MJ (Figure 6a,b). This peculiarity can also be seen in the composite de-biased distribution, the de-biased distribution of planets from low-noise systems (σ(O − C) < 15 m/s) and the biased distribution (the dotted magenta line in Figure 6), because in this domain there is just no-correction, matrix elements equal to 1.
This peculiarity may be explained by the contribution of planets formed due to the gravitational instability in the protoplanetary cloud [29], while the other giants were formed due to the nucleus accretion [30,31]. However, a lengthy discussion of this issue is beyond the scope of this paper.

3.2. Histograms of the Orbital-Period Distributions of RV Planets

In Section 2, we have described how to construct and translate the detectability window matrixes W(m, P), W ˜ (m, P), and V(m, P), which made it possible to correct the observed two-dimensional histogram N0(mi, Pj) for the distribution of RV planets over the orbital periods and minimum masses and obtain the corrected histogram N(mi, Pj).
To derive the mass distribution of RV planets, we summed the matrix elements over orbital periods. The summation can also be made over masses, which will result in the distribution over orbital periods N(P).
Due to the blind spot (zero elements) in the W, W ˜ , and V matrixes, this summation cannot be made over the entire mP plane (see Section 3.1). For example, it is possible to construct the distribution of planets NA(P) covering all masses and the orbital periods P = 1–100 days analogously to Equation (13b) or to construct the distribution of planets NB(P) covering all orbital periods and the masses larger than 0.12 MJ (37 ME) analogously to Equation (13c).
N A ( P ) = N ( Δ j = 1 6 P ) = i = 2 12 N Δ i m ,   Δ j P   ×   ( 1 / V ( Δ i m ,   Δ j P ) ) .
To derive the distribution NB(P) we consider the planets with minimum masses m = (0.011–13) MJ and orbital periods P = 1–104 days, also divide each of these domains into 12 bins and sum up the matrix elements over minimum masses starting from the fifth column (i.e., sum up the planets with m > 0. 12 MJ).
N B ( P ) = N ( Δ j = 1 12 P ) = i = 5 12 N Δ i m ,   Δ j P   ×   ( 1 / V ( Δ i m ,   Δ j P ) ) .
To describe the distribution NA(P) more accurately we consider the planets with minimum masses m = (0.011–13) MJ and orbital periods P = 1–104 days, divide each of these domains into 12 bins, and sum up the first 6 the matrix elements over minimum masses, (Figure 8a).
We excluded the lightest planets with the masses of 0.011–0.02 masses of Jupiter, because the V matrix values for them are very small (e.g., v(1,6) = 4.9·10−4), while the statistical errors are high. The small number of real planets after the correction becomes very large due to small values of the V matrix elements. In addition, in the region of masses less than five Earth masses (less than 0.016 Jupiter masses), the distribution of planets by masses may not follow the power law with an exponent of degree −3 [3], so the calculation of the V matrix elements made under this assumption becomes incorrect.
For planets with masses m > 0.12 MJ, the planet detection efficiency depends both on the noise level σ(O − C) (affects masses m (5b)) and on the observation duration T (affects periods P (5a)), so we cannot define a final detectability window V(m, P), as described in Section 2.1, (6–12). To construct the distribution of the long-period planets, we can either consider planets discovered by surveys with long observation times T (for them condition (5a) will always be satisfied, and we can apply formalism (6)–(12)), or consider planets of large masses, for which condition (5b) will always be satisfied, and different observation times T. In the latter case, the corrected distribution of the long-period planets will depend on the assumed dependence dN/dP.
Figure 8b shows the distribution of planets with masses 0.12–13 MJ, from the surveys with a total observation time T exceeding 1077 days, here (5a) is satisfied for planets with periods less than 2154 days. The distributions of the planets from the surveys with noise level σ(O − C) < 50 m/s and σ(O − C) < 15 m/s are shown. The obtained distributions display a monotonic increase in the number of planets with increasing orbital periods from 6.8 to 680 days, which can be approximated by a power law with a power index of 0.69 ± 0.03 (dN/dlogPP0.69 ± 0.03) and 0.70 ± 0.03 (dN/dlogPP0.70 ± 0.03), respectively.
Moreover, Figure 8b shows the distribution of planets with m = 0.12–13 MJ with a total observational time exceeding 2320 days and noise level σ(O − C) < 50 m/s, planets with P < 4640 days obey the condition (5a). In the range of P = 6.8–680 days, this distribution can be approximated by dN/dlogPP0.77 ± 0.07, whereas in the range of P = 680–4640 days it becomes flat (dN/dlogP ≈ 0). For comparison, the biased distribution (from the NASA Exoplanet Archive [1]) of planets with m = 0.12–13 MJ discovered by surveys with any total observation time T = 40–11,314 days is shown.
The choice of the coefficient δ in (5a) affects the distribution on the orbital period distribution only in the region of periods δ times the total observing time T. To avoid this uncertainty associated with the choice of δ, we consider only planets discovered by surveys with long observing times T, but they are few. Thus, the number of planets discovered by surveys with a total observation time T > 5000 days is 107 out of 695. To cover as many planets as possible, we considered (i) planets discovered by surveys with full observing time T > 2320 days and noise level σ(O − C) < 10 m/s (316 planets), and (ii) planets discovered by surveys with full observing time T > 1077 days and noise level σ(O − C) < 15 m/s (523 planets). In this case, for planets with m=1.2–13 MJ condition (5b) is always fulfilled (i) for planets with P < 4640 days, (ii) for planets with P < 2154 days.
Figure 8c shows the orbital period distributions for planets m = 1.2–13 MJ (columns 9–12), at different values of the parameter δ (δ = 1.5, 2.0, 2.5) and the minimum total observation time T (T > 1077, 2320, 5000 days).
From Figure 8c, one cannot draw conclusions about the distribution of the planets over orbital periods of more than twice the minimum total observing time since, in this region, the type of distribution is strongly influenced by choice of coefficient δ. Nevertheless, all three distributions in Figure 8c show similar behavior: in the region of periods of less than 46.4 days, there are very few or no planets of 1.2–13 Jupiter masses, in the region of 46.4–464 days, there is a steady increase in the number of planets, in the region of 464–4640 days the distribution becomes flat. There is a hint of a decrease in the number of planets in the last bin (4640–104 days), but it is not yet clear how much of this decrease is real and how much is caused by the observational selection.
The de-biased orbital-period distributions of RV planets were compared to the distribution of the Kepler planets with radii of (1–16) RE and orbital periods of 6.25–100 days [17], it is shown by a dash-dotted black line (Petigura et al. (2013) [17] reduced the orbital-period distribution to a single star, i.e., it represents the occurrence rate. To pass from the occurrence rate to the distribution, one should multiply it by a constant, which is equivalent to a vertical shift). As can be seen from Figure 8a, the orbital-period distributions of the Kepler planets and the RV planets are in good agreement in a domain of 6.25–100 days.
We compared the de-biased orbital-period distributions of RV planets with a similar distribution of Kepler planets with periods of 1–300 days from [6], Figure 12, and found good agreement. The distribution of planets with radii (1–16) RE (shown by the black line in Figure 12 in [6]) looks similar to the blue dotted line in Figure 8a, and the distribution of planets with radii (8–16) RE (shown by the red line in Figure 12 in [6]) looks similar to the distribution of planets with masses (0.12–13) MJ in Figure 8b.
We compare the result in (Figure 8b,c) with ([32], Figure 2), which is based on the orbital period distribution of 155 RV planets with masses of 0.1–20 MJ orbiting 822 stars detected by HARPS and CORALIE. In the range of 7–1000 days, there is a similar increase in the number of planets, which transitions to a roughly flat distribution in the ~1000–4000 day range. For planets with periods in the range of 4·103–104 days [32] show a sharp decrease in the number of planets. Figure 8c does not show such a strong decrease. Perhaps the observed decrease in [32] is due to the reduced detection efficiency of light gas giants (0.1–0.3 MJ) with large orbital periods. The semi-amplitude of their induced radial velocity K is less than ~3 m/s (for stars of Solar mass), and such planets are not detected in most cases.
Since the projective-mass distribution of RV planets exhibits different behavior in mass domains of (0.011–0.14) MJ, (0.14–1.7) MJ, and (1.7–13) MJ, we constructed the orbital-period distributions for the planets from each of these mass domains (Figure 9).
Figure 9 shows that the orbital-period distributions of planets with small, intermediate, and large masses ((0.02–0.12) MJ, (0.12–1.2) MJ, and (1.2–13) MJ) differ from each other, which suggests a dominating structure of planetary systems. The most massive planets, with m > 2.2 MJ, are mainly on wide orbits with orbital periods longer than 100 days. Only 18 (7.9%) out of 227 massive planets have orbital periods shorter than 100 days. The analogous portion of planets with intermediate masses is 65/233 = 27.9%. The distribution of planets with intermediate masses exhibits a two-fold peak in a range of 2.15–4.64 days, which is not observed in the distributions of planets with small and large masses.
Both distributions of planet numbers versus orbital period agree well within error bars.
We compared the de-biased orbital-period distributions of RV planets with masses of (0.02–0.12) MJ (Figure 9, blue line) with the same distribution of Kepler planets with radii (1–6) RE from ([33], Figure 15, top panel). Although the two sets of planets considered do not match completely, we found good agreement between both distributions (a rapid increase in the number of planets as orbital periods increase from 1 to 10 days and then an approximately flat distribution for orbital periods of 10–100 days).
We compared the de-biased orbital-period distributions of RV planets with masses of (0.02–0.12) MJ with the distribution of Kepler planets with radii (1.7–4) RE (Sub-Neptunes) with periods 1–300 days from ([34], Figure 7) and also found good agreement. The distribution of planets with radii (8–24) RE (Jupiters) and periods 1–300 days from [34] agrees with the distribution of planets with masses (1.23–13) MJ inside the matched interval of orbital periods we obtained.
For this analysis, the detectability matrices W,  W ˜ were calculated by δ = 2.0 for all planets and γ = 0.75, 1.6, and 2.0 for planets with small, intermediate, and large masses, respectively, same for FGK host stars. Matrix V was calculated assuming that the distribution of the masses of the light planets follows the power law dN/dm ∝ m−3, the medium-mass planets follow dN/dm ∝ m−1, and the heavy planets follow dN/dm ∝ m−2. Heavy planets do not require any correction because the corresponding matrix elements were equal to 1.

4. Discussion and Conclusions

The observed distributions of RV exoplanets over minimum masses m = M· sini and orbital periods P were initially distorted (biased) by the observational selection. We have corrected important selection factors as caused by differences in the sensitivity of spectrographs, the activity level of host stars, the mass of the host star, and the duration of the surveys. The results presented here on the mass and period distributions of planets are related to the particular (observed) mass distribution of the 547 stars. Their choice was eventually mixed over different host star types, it may not be well representative of the true and full star distribution in the Galaxy. The overall distribution of planetary masses and periods of the host stars of all types can be artificial because the host star mass or its spectral class possibly determines a planetary interior to be compact or Solar system close analogue or different.
Separately we have considered planets distribution of FGK host stars in stellar mass domain of Mstar = 1.00 ± 0.25. Most of the host stars of the known RV planets are sun-like.
Evidently, the mass distribution of transiting planets can be dependent on the spectral class of their parent stars. However, if the giant planets in stars of spectral classes F, G, K were considered in [8], a certain independence was shown.
To account for the observational selection, the “detectability window” regularization algorithm was used. The method is based on a concept of the detectability window matrix V(m, P) in the mP plane, the components of which are determined as the probabilities of detecting planets in each of the intervals of minimum masses and orbital periods. When constructing the corrected distributions over minimum masses and orbital periods in the form of a histogram, each detected RV planet was accounted for with a statistical weight inverse to the value of the V matrix component for specified values of P and m.
We considered planets with minimum masses m = (0.011–13) MJ (or (3.5–4131)ME) and orbital periods P = 1–104 days. Within these limits, some elements of the V matrix contain zeros, which means that planets of low masses and large orbital periods cannot be detected. We obtained the distributions of planets with all masses and orbital periods of 1–100 days (Equation (13b)) or the distributions of planets with all orbital periods and masses larger than 0.12 MJ (Equation (13c)).
The composite projective-mass distribution of RV planets obeys a piecewise power law with two breakpoints at ~0.12 MJ and ~2.2 MJ (Figure 6). The distribution of RV planets with m = (0.02–0.087) MJ (or (6.3–28)ME) follows a power law with an exponent of −3, dN/dmm−3. The distribution of RV planets with m = (0.21–2.2) MJ follows a power law with an exponent ranging from −0.8 to −1.0, dN/dmm−0.8…−1.0. The distribution of RV planets with m = (2.2–13) MJ is fitted by a power law with an exponent ranging from −1.7 to −2.0, dN/dmm−1.7…−2.0. In general, the composite projective-mass distribution of RV planets partly agrees with the predictions of the population synthesis theory [2,3].
At the same time, the corrected projective-mass distribution exhibits some peculiarities. In a mass domain of (0.087–0.21) MJ (or (28–67)ME), there is a minimum, the depth of which exceeds a 7.7-fold for planets with P = 1–100 days and m = (0.11–0.14) MJ (or (34–43)ME).
When considering samples of planets with large orbital periods, this minimum becomes smaller and just disappears for planets with orbital periods of 10–1000 days (Figure 7b). We assume that this minimum is caused by a lack of this kind of planets in tight orbits and corresponds to the so-called desert of hot Neptunes [27,28].
In a mass domain of (6–9) MJ, the distribution of RV planets exhibits a maximum. Probably, this maximum appears due to the contribution of planets formed due to the gravitational instability in the protoplanetary disk, while the other giant planets were formed due to the core accretion.
We have directly compared true mass distributions (from theory of formation [2,3]) with observed minimum mass distributions. Is this legitimate? If the true mass distribution follows a power law with exponent α, does the minimum mass distribution has the same exponent α? and vice-versa? To the best of our knowledge, this question has not yet been cleared in the literature. However, using a particular geometrical representation of an ensemble of exoplanets, Bertaux et al. (2021) [35] have shown that this is indeed the case. In addition, Bertaux et al. (2021) have performed forward simulations of true mass distributions for various values of a power law exponent, giving minimum mass distributions. They were then fitted by a power law. For a true mass distribution with a power law with exponents α = −1.5, α = −2, and α = −2.5, we found, respectively, for the minimum mass distribution: −1.57, −2.03, and −2.498. The small differences are due to edge effects. Therefore, we think it is fully legitimate to compare directly the exponents of power laws of a true mass distribution (coming from theory) and an observed minimum mass distribution (See also [4,36]).
We also analyzed the orbital-period distributions of planets. Due to the blind spot, the distribution of RV planets cannot be obtained for the entire mP plane. We derived the orbital-period distribution of planets with masses of (0.02–13) MJ with P = 1–100 days and the distribution of planets with all considered orbital periods but with masses exceeding 0.12 MJ.
The de-biased distribution of planets with masses (0.02–13) MJ and orbital periods of 1–100 days displays a rapid increase in the number of planets with increasing periods from 1 to 10 days and a close to flat distribution in the region of 10–100 days. The distribution shape is well consistent with that for the Kepler (transiting) planets with radii of (1–16) RE and orbital periods of 6.25–100 days [6,17,33]).
The distribution of planets with masses larger than 0.12 MJ (37 ME) shows a local maximum at P = 2.15–4.64 days. In the domain of 6.8–680 days, the distribution follows a power law with an exponent of −0.3 (dN/dP   P−0.3), in the region of 680–4640 days the distribution becomes flat (dN/dP   P−1) (Figure 8b,c). The orbital-period distributions of RV planets from three mass domains, where the projective-mass distributions behave differently ((0.02–0.12) MJ, (0.12–1.2) MJ, and (1.2–13) MJ), also differ (Figure 9). Specifically, most massive planets, with m > 2.2 MJ, are mainly on wide orbits with orbital periods longer than 100 days. This may suggest that there is a prevailing structure of planetary systems, within which low-mass planets are on orbits close to host stars, while massive planets are on wide orbits, analogous to the situation in the Solar System.

Author Contributions

Conceptualization, V.A.; methodology, V.A.; software, O.Y.; validation, O.K. and J.-L.B.; investigation, V.A.; resources, V.A. and A.I.; data curation, V.A.; writing—original draft preparation, I.S.; writing—review and editing, V.A. and A.T.; visualization, I.S.; supervision, A.T. All authors have read and agreed to the published version of the manuscript.

Funding

The authors acknowledge the support of Ministry of Science and Higher Education of the Russian Federation under the grant 075-15-2020-780 (N13.1902.21.0039).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Method for Merging Together Several Surveys

Let us consider one particular RV survey of exoplanets containing N stars, with its own sensitivity and duration. Within a particular bin (ΔmP), this survey detected fobsmP), while the exercise of checking the “detectability” of a dummy planet (m, P) around all N stars yielded N fpmP). Therefore, we can say that the true occurrence rate foccmP) of this type of planet around stars, in units of “planets per star”, is given by:
f occ Δ m , Δ P = f obs Δ m , Δ P N   f p Δ m , Δ P
This is exactly the Equation (13) of Tuomi et al. (2019) [16].
We can describe this Equation (A1) by stating that the occurrence rate of planets in a certain bin is simply the ratio of the number of actually detected planets to the number of “detectable” planets, given the particular characteristics of the survey (accuracy of RV, period covered).
Now, we describe how can be simply merged the results of two surveys S1 and S2 with different sensitivity ((O − C) threshold), different durations (which will impact the coverage of periods) and different numbers of planets monitored, respectively, n1 and n2. Let us call q1 and q2 the number of “detected” dummy planets in the bin (Δm, ΔP). We have, by definition of fpmP):
q1 = n1 fp1mP); q2 = n2 fp2mP)
Therefore, we have two estimates of the occurrence rate of planets (m, P) foccmP):
focc1mP) = fobs1mP)/q1 ; focc2mP) = fobs2mP)/q2
In order to obtain an estimate from the combination of the two surveys, one could make an average of focc1mP) and focc2mP), or some kind of weighted average. However, there is a simpler way to obtain a combined estimate. Indeed, we note that (dropping ((ΔmP) for simplicity):
f occ = f obs 1 q 1 = f obs 2 q 2 = f obs 1 + f obs 2 q 1 + q 2
The numerator of the last fraction is simply the sum of the planets detected in the two surveys. The denominator is the sum of dummy planets detected (detectable planets) in the two surveys. This can be extrapolated to any number of surveys with different characteristics, and we can state:
Regardless of the different characteristics of the various surveys, the (true) occurrence of planets (number of planets per star) in a certain bin is simply the ratio of the total number of actually detected planets in all surveys to the total number of “detectable” planets in all surveys, when detectability is computed for each survey given the particular characteristics of each survey (accuracy of RV, period covered).
f occ = k f obs k k q k
and therefore the true number of planets N(m,P) in this bin for all Q stars (Q = (547 in the present study) of the combined surveys is:
N m , P = Q f occ = Q k f obs k k q k
We then define the detectability window for a particular bin as:
W = k q k Q
and therefore the true number of planets N(m, P) in this bin for all Q stars of the combined survey is:
N m , P = Q f occ = k f obs k W m , P
Coming back to one particular single survey, Figure A1 is a sketch of the Detectability window in a diagram Period P/amplitude K of the reflex motion of a star influenced by the presence of one planet around the host star. The solid lines result from Equation (2) for a planet of 0.1 MJ and two masses of the host star. Within a given survey, the planet will be or will not be detected according to the mass of the host star and the sensitivity limit of the survey.
Figure A1. This is a sketch of the Detectability window, in a diagram of observables in one RV survey: the period and the amplitude K (m/s) of a sinusoidal variation of RV induced by a planet. The two solid lines represent the relation for a planet of 0.1 MJ around a host star with 1 solar mass (red) and 0.1 solar mass Msun (black). The same planet will be detected around a host star with mass 0.1 Msun and not detected around a host star with mass 1 Msun. The rectangular blue-shaded area represents the area of the detectability window; γ and δ are two multiplicative factors affecting the boundaries of the detectability window that can be adjusted simultaneously to all surveys, for a more homogeneous treatment (see text).
Figure A1. This is a sketch of the Detectability window, in a diagram of observables in one RV survey: the period and the amplitude K (m/s) of a sinusoidal variation of RV induced by a planet. The two solid lines represent the relation for a planet of 0.1 MJ around a host star with 1 solar mass (red) and 0.1 solar mass Msun (black). The same planet will be detected around a host star with mass 0.1 Msun and not detected around a host star with mass 1 Msun. The rectangular blue-shaded area represents the area of the detectability window; γ and δ are two multiplicative factors affecting the boundaries of the detectability window that can be adjusted simultaneously to all surveys, for a more homogeneous treatment (see text).
Atmosphere 14 00353 g0a1

Appendix B

Here we discuss an artificial example in which only two types of planets are considered: the heavy (1) and light (2) planets. Their occurrence rates are f1, f2, correspondingly. We consider, therefore, two surveys, the first can detect only heavy planets, and the second can detect all the planets. Let the first survey observe N1 stars, while the second survey observe N2 stars.
The first survey detects f1·N1 heavy planets and zero light planets. The second survey detects f1·N2 heavy planets and f2 ·N2 light planets. The number of heavy planets detected is f1·N1 + f1·N2 = f1·(N1 + N2). The number of light planets detected is f2 ·N2.
However, the true number of light planets orbiting all observed stars is f2 ·(N1 + N2).
For de-bias, the detectability window matrix V consists of the following 2 cells:
v2v1
Here v1 = 1 (means that all surveys detect the heavy planets), v2 = (f2 ·N2)/(f2 ·(N1 + N2)) = N2/(N1 + N2).
N2/(N1 + N2)1
To check and visualize it we assume some casual numeric, f1 = 0.05, f2 = 3, N1 = 1000, N2 = 20. Then the first survey detects f1·N1 = 50 heavy planets. The second survey detects f1·N2 = 1 heavy planets and f2 ·N2 = 60 light planets. The total number of detected heavy planets is f1·N1+ f1·N2 = 51, the total number of detected light planets is 60. However, while the true number of light planets is f2 ·(N1 + N2) = 3060.
If one corrects the number of light planets by v2 = N2/(N1 + N2) = 20/1020 = 1/51, then resumed the corrected number of light planets is f2 ·N2/v2 = 60/(1/51) = 3060.
From here we complicate our de-biasing technique a bit, we assume we do not know the numbers of observed stars in surveys: N1 and N2 (e.g., due to the lack of a proper criterion of planet non-detection). However, oppositely, we know the numbers of the stars denoted by S1 and S2 with detected planets corresponding to the surveys.
The first survey detects f1·N1 heavy planets orbiting S1 = d1·f1·N1 stars, the second survey detects f1·N2 heavy planets and f2 ·N2 light planets orbiting S2 = d2·N2·(f1 + f2) stars. The coefficient di defines the ratio of the number of stars to the number of observable planets in those stars. For small fi di→ 1 (for small fi every star with planets has only one planet). For big fi SiNi (every observed star has planets).
The detectability window matrix W also consists of 2 cells:
w2w1
w1 = 1.
w2 = S2/(S1 + S2) = 1/(1 + S1/S2).
If S1 = 50, S2 = 20, than w2 = 2/7.
The corrected number of light planets is 210. Thus, one counts only stars with planets results in underestimate the number of light planets.
We express S1/S2 through w2: S1/S2 = 1/w2 − 1.
We express Ni through Si: N1 = S1/(d1·f1), N2 = S2/(d2·(f1 + f2)).
Than v2 = N2/(N1 + N2) = 1/(1 + N1/N2) = 1/(1 + S1/S2·d2/d1·(f1 + f2)/f1) =
= 1/(1 + (1/w2 − 1)·d2/d1·(f1 + f2)/f1).
In order to exclude the unknown factors di, further we consider the star as many times as the number of the orbiting planets are known, that in the case of a multiplanet system when constructing the detectability window matrix: W(S) → Ŵ(Ŝ). Then Ŝ1 is the number of planets detected by the first survey and Ŝ2 is the number of planets detected by the second survey.
N1 = Ŝ1/f1,
N2 = Ŝ2/(f1 + f2), and
v2 = N2/(N1 + N2) = 1/(1 + (1/ŵ2 − 1)·(f1 + f2)/f1).
To calculate v2 through ŵ2, we have to know the relation f2/f1.
In our case f2/f1 = 60 and (f1 + f2)/f1 = 61.
Let us check it out.
ŵ2 = Ŝ2/(Ŝ1 + Ŝ2) = 61/(50 + 61) = 61/111
v2 = 1/(1 + (111/61 − 1)·61) = 1/51.
Finally, we have to estimate the unknown ratio f2/f1. We consider it from the ratio of light and heavy planets discovered by second survey Ŝ2/Ŝ1. If one underestimates f2/f1, (e.g., accepts f2/f1 = 50 and (f1 + f2)/f1 = 51), then the number of light planets is underestimated: v2 = 1/(1 + (111/61 − 1)·51) ≈ 0.0234, and the corrected number of light planets is 2568. If one overestimates f2/f1, (e.g., accepts f2/f1 = 70 and (f1 + f2)/f1 = 71), then the number of light planets is overestimated: v2 = 1/(1 + (111/61 − 1)·71) ≈ 0.0169, and the corrected number of light planets is 3558. Let us claim that these inaccuracies are not critical.
Figure A2 shows the ratio between w2 и v2 depending on the f2/f1. The Table A1 shows de-biased number of light planets by applied method (by W and by V).
Figure A2. The ratio between w2 и v2 versus of f2/f1.
Figure A2. The ratio between w2 и v2 versus of f2/f1.
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Table A1. De-biased number of light planets depending on applied method (W or V matrices).
Table A1. De-biased number of light planets depending on applied method (W or V matrices).
ParametersLow-Mass PlanetsIntermediate MassesMassive Planets
Without correction60306051
Correction by W21014.57
Correction by V (true f2/f1)30601
Correction by V (underestimated f2/f1)25681.19
Correction by V (overestimated f2/f1)35520.86
Table A2. List of considered RV planets (shading at FGK stars).
Table A2. List of considered RV planets (shading at FGK stars).
NPlanet Name Orbital Period, DaySpan, DayPlanet Mass, MJHost Star Mass, mσ(O-С), m/sRV K, m/s
1HD 24064 b535.61921 12.89 2.89 + 2.89 1.6134.5251.00
2HATS-59 c14221742 12.70 0.87 + 0.87 1.038100.00224.00
3BD+20 2457 c6221833 12.47 0.56 + 0.56 10.8360.00160.03
4HD 87646 b13.4812500 12.4 0.7 + 0.7 1.12270.00956.00
5HIP 67537 b2556.54419 11.10 0.4 + 1.1 2.418.00112.70
6HD 220074 b672.11374 11.10 1.8 + 1.8 1.257.40230.80
7HD 110014 b835.4772950 11.09 1 + 1 2.1745.80158.20
8HD 106270 b28901484 11 0.8 + 0.8 1.328.40142.10
9HD 13189 b471.61300 10.95 2.92 + 2.92 2.2454.50173.30
10TYC 4282-00605-1 b101.541200 10.78 0.12 + 0.12 0.9723.02495.20
11HD 114762 b83.9156901 10.69 0.56 + 0.56 0.827.40612.50
12HD 156846 b359.512686 10.67 0.74 + 0.74 1.386.06464.30
13HD 95127 b4822929 10.63 8.06 + 8.06 3.750.90116.00
1418 Del b993.31772 10.30 0.11 + 0.11 2.315.40119.40
15HD 17092 b359.91200 10.13 2.29 + 2.29 6.7316.0082.40
16HD 39091 b (pi Men b)20936570 10.02 0.15 + 0.15 1.0945.50192.60
17TYC 1422-614-1 c559.33651 10.00 1 + 1 1.1518.94233.00
18HD 208527 b875.51352 9.9 1.7 + 1.7 1.639.30155.40
19HD 156279 b131.05254.2 9.880 0.69 + 0.69 0.959.08578.00
20HD 162020 b8.428842 9.840 2.75 + 2.75 0.7513.601813.00
21Kepler-94 c820.3800 9.836 0.629 + 0.629 0.8113.90262.70
22BD-13 2130 b714.31529 9.78 4.56 + 4.56 2.1218.30137.60
23HD 139357 b1125.71287 9.76 2.15 + 2.15 1.3514.14161.20
24HD 221420 b224826500 9.70 1.1 + 1 1.673.9354.70
25HD 38801 b685.251143 9.70 0.57 + 0.59 1.20711.00197.30
26HD 141937 b653.22882 9.690 0.4 + 0.4 1.098.70234.50
27HD 106515 A b36304800 9.610 0.14 + 0.14 0.979.63158.20
28WASP-8 c43232099 9.450 2.26 + 1.04 1.342.91115.00
29HIP 63242 b124.61025 9.180 0.24 + 0.24 1.5423.70287.50
30HD 33564 b388417 9.10 0.2 + 0.2 1.256.70232.00
31HD 23596 b15612100 9.030 0.74 + 0.74 1.479.20127.00
32HD 175167 b12901828 8.970 3.32 + 3.32 1.376.91161.00
33HIP 75458 b (iota Dra)511.1678 8.82 0.72 + 0.72 1.0510.00307.60
34HD 1690 b5332547 8.790 3.63 + 3.63 1.8629.00190.00
35gam 1 Leo b428.52195 8.780 1 + 1 1.2343.00208.30
36HD 30177 b2527.86300 8.62 0.13 + 0.13 1.05310.00125.98
37HD 156279 c41914225 8.60 0.5 + 0.55 0.932.20110.20
38HD 191806 b1606.33616 8.520 0.63 + 0.63 1.1410.00140.50
39HD 181234 b74626794 8.37 0.34 + 0.36 1.0120.00126.80
40HD 222582 b572.38683 8.370 0.4 + 0.4 1.123.36276.30
41HD 89744 b256.785186 8.35 0.18 + 0.18 1.8616.40269.66
42HD 104985 b199.5051932 8.300 0.07 + 0.07 2.326.60166.80
43HIP 105854 b184.21279 8.20 0.2 + 0.2 2.123.60178.10
44HD 102329 b778.11484 8.160 2.19 + 2.19 3.217.2084.80
45HD 74156 c24733408 8.060 0.37 + 0.37 1.23812.8116.50
46HD 178911 B b71.4843392 8.030 2.51 + 2.51 1.249.10343.30
47bet Cnc b605.23460 7.800 0.8 + 0.8 1.747.20133.00
48HD 203473 b1552.91553 7.80 1.1 + 1.1 1.127.00133.60
49HD 14067 b14552302 7.800 0.7 + 0.7 2.412.7092.20
50Pr0211 c4850791 7.790 0.33 + 0.33 0.93526.00138.00
51HD 168443 b58.1125360 7.659 0.0975 + 0.0975 0.9953.81475.13
52HD 5891 b177.111392 7.630 1.43 + 1.43 1.9328.40178.50
53eps Tau b594.9938 7.600 0.2 + 0.2 2.79.9095.90
54HD 30177 c11,6136300 7.600 3.1 + 3.1 1.0537.1770.80
5570 Vir b116.6889480 7.416 0.057 + 0.057 1.096.10316.20
56HD 81040 b1001.72227 7.270 0.98 + 0.98 1.0526.00168.00
57HD 125612 d30082016 7.2 0.35 + 0.35 1.0913.7098.37
58HD 111232 b11431181 7.140 0.19 + 0.19 0.847.50159.30
594 UMa b269.3890 7.100 1.6 + 1.6 1.23428.80215.55
60HD 86264 b14752952 7.00 1.6 + 1.6 1.4226.20132.00
61Kepler-424 c223.3653 6.970 0.62 + 0.62 1.0120.00246.00
62HD 106252 b15313682 6.930 0.27 + 0.27 1.0112.20138.80
63HD 59686 A b299.364500 6.920 0.18 + 0.24 1.919.49136.90
64HD 183263 c46844908 6.90 0.12 + 0.12 1.123.6877.50
65HD 196067 b36384900 6.900 3.9 + 1.1 1.299.92104.00
66GJ 676 A c73373535 6.8 0.1 + 0.1 0.732.4688.70
67HD 98649 b60235771 6.79 0.53 + 0.31 1.0310.30140.10
68eps CrB b417.92503 6.700 0.3 + 0.3 1.725.00129.40
69HD 233604 b192.002956 6.575 0.16 + 0.16 1.519.13177.80
70HD 147018 c10082290 6.560 0.32 + 0.32 0.9277.39141.20
71HD 11977 b621.63864 6.5 0.2 + 0.2 2.3111.20105.00
72alf Tau b628.9611,314 6.470 0.53 + 0.53 1.1398.00142.10
73HD 1666 b270.03165 6.430 0.31 + 0.22 1.535.60199.40
74BD+03 2562 b481.94159 6.400 1.3 + 1.3 1.1469.80155.70
75HD 10697 b1075.74057 6.383 0.078 + 0.078 1.138.10116.90
76IC 4651 9122 b734.03284 6.300 0.5 + 0.5 2.125.0089.50
77HD 113996 b610.23994 6.300 1 + 1 1.4939.30120.00
78HD 2039 b11201337 6.290 1.16 + 1.16 1.2315.00153.00
79bet UMi b522.32920 6.100 1 + 1 1.440.50126.10
80HD 70573 b851.81100 6.100 0.4 + 0.4 118.70148.50
81HD 145377 b103.951106 6.020 0.48 + 0.48 1.215.30242.70
82HIP 65891 b1084.51733 6.00 0.49 + 0.49 2.59.3064.90
83HD 66141 b480.52605 6.00 0.3 + 0.3 1.138.80146.20
84HD 238914 b41003944 6.00 2.7 + 2.7 1.4719.8071.00
85HIP 67851 c2131.84017 5.980 0.76 + 0.76 1.638.9069.00
86HD 224538 b1189.14128 5.970 0.42 + 0.42 1.345.20107.00
87HD 33142 c8342573 5.97 1.04 + 0.8 1.625.0011.40
88HD 28185 b3792971 5.900 0.24 + 0.24 1.027.33163.50
89HD 5583 b139.353225 5.780 0.53 + 0.53 1.0169.50225.80
90HD 99706 c12782418 5.69 1.43 + 0.96 1.464.2013.80
91HD 11755 b433.71716 5.630 0.92 + 0.92 0.7227.70191.30
92Kepler-56 d10021490 5.610 0.38 + 0.38 1.321.8095.21
93HD 75784 c50403694 5.60 1.2 + 1.2 1.414.6357.00
94HD 213240 b882.7770 5.580 0.31 + 0.31 1.5711.0096.60
95HIP 8541 b1560.22194 5.50 1 + 1 1.179.1087.40
96HD 72892 b39.4751486 5.450 0.37 + 0.37 1.024.50318.40
97HD 27894 d51744748 5.415 0.239 + 1.214 0.82.0479.76
98TYC 3667-1280-1 b26.4682844 5.400 0.4 + 0.4 1.8728.30242.40
99HD 132406 b9741078 5.380 1.31 + 1.31 1.037.50115.00
100HD 154672 b163.943693 5.370 0.39 + 0.39 1.183.60225.00
101HD 142 c60055067 5.300 0.7 + 0.7 1.23211.2055.20
10281 Cet b952.71638 5.300 0.13 + 0.13 2.49.2062.80
103HD 240210 b501.751655 5.210 0.11 + 0.11 0.8238.90161.89
104HD 148164 c50624383 5.16 0.82 + 0.82 1.215.6254.28
105HD 147873 b116.5963995 5.140 0.34 + 0.34 1.382.60171.50
106HD 13908 c9311589 5.130 0.25 + 0.25 1.299.6090.90
107HD 16175 b9903988 5.100 0.81 + 0.81 1.639.2094.00
108BD-17 63 b655.61760 5.10 0.12 + 0.12 0.744.10173.30
109HD 50554 b12932000 4.954 0.389 + 0.389 1.0412.00104.00
110HD 102272 b127.581450 4.940 0.64 + 0.64 1.4515.40155.50
111HD 67087 c23743423 4.850 10 + 3.61 1.3611.8093.30
112HD 11506 b16223574 4.83 0.52 + 0.52 1.244.8078.17
11314 And b185.841486 4.800 0.06 + 0.06 2.220.30100.00
114GJ 676 A b1051.13535 4.733 0.011 + 0.01 0.732.46124.50
115HD 40979 b264.153588 4.670 0.34 + 0.34 1.4520.30119.40
11614 Her b1773.44428 4.660 0.15 + 0.15 0.913.0090.00
117gam Lib c964.65234 4.580 0.45 + 0.43 1.4716.4173.00
118HD 111998 b825.92989 4.510 0.5 + 0.5 1.1817.3587.60
119HD 120084 b20823530 4.500 2.8 + 0.93 2.395.8053.00
120HD 25015 b60196428 4.48 0.30 + 0.28 0.8630.0060.10
121Kepler-454 c523.91750 4.460 0.12 + 0.12 0.855.00110.44
122HD 142022 A b19282170 4.440 3.17 + 3.17 0.910.8092.00
123GJ 86 b15.7651090 4.420 0.2 + 0.2 0.937.00376.70
124HD 111591 b1056.43796 4.400 0.4 + 0.4 1.9421.9059.00
125HD 80606 b111.43673480 4.380 0.74 + 0.74 1.1513.30472.00
126tau Boo b3.3123287 4.320 0.04 + 0.04 1.3413.90471.73
127BD+20 274 b578.22548 4.200 0.22 + 0.22 0.835.80121.40
128ups And d1276.467383 4.132 0.029 + 0.029 1.313.7656.26
129omi UMa b16302625 4.100 0.26 + 0.26 3.097.6033.60
130HD 76920 b415.43028 3.930 0.14 + 0.15 1.179.74186.80
131HIP 14810 b6.6741112 3.90 0.49 + 0.49 1.013.29423.34
13242 Dra b479.11209 3.880 0.85 + 0.85 0.9826.00110.50
13355 Cancri d48258476 3.878 0.068 + 0.068 0.90516.3048.29
134HD 55696 b18271827 3.87 0.72 + 0.72 1.297.1876.70
135HD 24040 b36686392 3.860 0.36 + 0.36 1.117.5047.40
136HD 72659 b36584515 3.850 0.23 + 0.23 1.434.2041.00
137HD 128311 c921.5384566 3.789 0.924 + 0.432 0.8289.3774.80
138HD 35759 b82.4671261 3.760 0.17 + 0.17 1.156.00173.90
139HD 92788 b325.86867 3.76 0.16 + 0.15 1.155.00108.24
140HD 95872 b43754080 3.740 0.93 + 0.93 0.77.9059.00
141KELT-6 c1276567 3.710 0.21 + 0.21 1.4217.0065.70
142HD 195019 b18.2022240 3.70 0.3 + 0.3 1.2116.00272.80
143HD 92788 c11,6116867 3.67 0.3 + 0.25 1.155.0033.29
144HD 183263 b625.14908 3.635 0.034 + 0.034 1.123.6886.16
145HD 86950 b12702594 3.600 0.7 + 0.7 1.666.1049.00
146HD 169830 c1834.35155 3.54 0.1 + 0.1 1.48.9039.70
147HD 143361 b10394417 3.532 0.065 + 0.066 0.9682.8073.89
148HD 166724 b51444010 3.530 0.11 + 0.11 0.813.7671.00
149HD 17156 b21.21740 3.510 0.21 + 0.21 1.413.62279.80
150HD 108341 b11293770 3.500 3.4 + 1.2 0.8431.50144.00
151HD 1605 c21113281 3.48 0.13 + 0.11 1.316.4046.50
152HD 204313 b1920.13006 3.460 0.21 + 0.21 1.037.8057.00
153HD 95089 c17852422 3.45 0.14 + 0.14 1.547.6045.10
154TYC 3318-01333-1 b5623514 3.420 0.35 + 0.35 1.1915.0075.42
155HD 18742 b7662995 3.4 1.2 + 1.2 1.367.9061.00
156HAT-P-17 c55841869 3.400 1.1 + 0.7 0.995.0048.80
157GJ 3021 b133.71462 3.370 0.09 + 0.09 0.919.20167.00
158HD 13167 b26133105 3.31 0.16 + 0.16 1.354.0048.20
159eps Indi A b16,5109000 3.25 0.39 + 0.65 0.7541.0029.22
160HR 5183 b (HD 120066 b)27,0008200 3.23 0.15 + 0.14 1.073.4038.25
161HD 66428 b22635200 3.204 0.043 + 0.043 1.083454.03
16291 Aqr b181.44150 3.200 0.001 + 0.001 1.418.9091.50
163HD 37605 c27202841 3.19 0.38 + 0.38 0.946.4448.51
164WASP-41 c4211581 3.180 0.2 + 0.2 0.8120.0094.00
165HD 220842 b218.47997 3.180 0.15 + 0.15 1.134.50108.10
166HD 196050 b13781364 3.180 0.3 + 0.3 1.317.2049.70
167HD 18015 b22783105 3.18 0.23 + 0.23 1.498.4038.00
168HD 190984 b48851950 3.100 0.065 + 0.065 0.913.4448.00
169HD 73267 b12451586 3.097 0.044 + 0.043 0.91.7064.65
170HD 221287 b456.11130 3.090 0.79 + 0.79 1.258.5071.00
171HD 68402 b11032050 3.070 0.35 + 0.35 1.125.3054.70
172HD 142245 b12991465 3.070 0.42 + 0.42 3.54.8024.80
173HD 67087 b352.23423 3.060 0.22 + 0.2 1.3611.8073.60
174HD 32518 b157.541215 3.040 0.68 + 0.68 1.1318.33115.83
175HD 125612 b559.42016 3.0 0.09 + 0.09 1.0913.7080.00
17675 Cet b691.93609 3.00 0.16 + 0.16 2.4910.8038.30
177HD 12484 b58.83865 2.98 0.14 + 0.14 1.0125.20155.00
178HD 153950 b499.41791 2.95 0.29 + 0.29 1.253.9069.20
179HD 50499 c86207268 2.93 0.73 + 0.18 1.2510.0024.23
180HD 165155 b434.52740 2.89 0.23 + 0.23 1.025.8075.80
181HD 169830 b225.621506 2.88 0.03 + 0.03 1.48.9080.70
182HD 113337 b3242193 2.83 0.24 + 0.24 1.424.8075.60
183xi Aql b136.751152 2.80 0.07 + 0.07 2.222.3065.40
184HD 118203 b6.134402 2.79 0.25 + 0.25 1.8418.10217.00
185HD 1502 b428.51167 2.75 0.16 + 0.16 1.4611.3057.50
186HD 171238 b15322491 2.72 0.49 + 0.49 0.9611.6050.70
187HD 150706 b58945150 2.71 1.14 + 0.66 1.1715.3031.10
188HD 75898 b422.94800 2.71 0.36 + 0.36 1.265.8263.39
189HD 81688 b184.021945 2.70 0.045 + 0.045 2.124.0058.58
190HD 40956 b578.61818 2.70 0.6 + 0.6 223.6068.00
191HD 173416 b323.61278 2.70 0.3 + 0.3 218.551.80
192HD 37605 b55.0132841 2.69 0.3 + 0.3 0.946.44203.47
193HIP 109600 b232.082496 2.68 0.12 + 0.12 0.873.3098.60
194HD 152079 b29194032 2.66 0.046 + 0.046 1.1474.0840.76
195HD 171028 b5501320 2.62 0.16 + 0.16 1.532.3060.60
196HD 23079 b730.61358 2.61 0.11 + 0.11 1.124.8054.90
197HD 217107 c42703470 2.60 0.15 + 0.15 111.0035.70
198HD 155233 b818.81828 2.60 0.3 + 0.3 1.6910.0040.50
199HD 196885 A b13333780 2.58 0.16 + 0.16 1.2814.7053.90
200HD 154857 c34524109 2.58 0.16 + 0.16 1.963.2024.20
201HD 43691 b37821 2.55 0.34 + 0.34 1.3212.49130.06
202HD 181342 b564.11367 2.54 0.19 + 0.19 1.697.2044.10
203HD 41004 A b9631140 2.54 0.74 + 0.74 0.9510.0099.00
20447 UMa b10787175 2.53 0.07 + 0.06 1.036.548.40
205HD 60532 c600.11960 2.51 0.16 + 0.16 1.54.6646.10
206TYC 1422-614-1 b198.43651 2.50 0.4 + 0.4 1.1518.9482.00
207HD 133131 B b57691840 2.50 0.05 + 0.05 0.931.5937.41
208GJ 317 b6923708 2.50 0.7 + 0.4 0.428.575.20
209HD 154857 b408.64109 2.45 0.11 + 0.11 1.963.2048.30
210Kepler-432 c406.2437 2.43 0.22 + 0.24 1.3250.0062.10
211HD 290327 b24431986 2.43 0.42 + 0.42 0.841.641.30
212HD 108863 b437.71488 2.414 0.078 + 0.078 1.595.147.40
213mu Leo b357.83443 2.40 0.4 + 0.4 1.514.252.00
214HIP 74890 b822.31429 2.40 0.3 + 0.3 1.746.536.50
215HD 29021 b1362.31597 2.40 0.2 + 0.2 0.853.9356.40
216HD 212771 b380.7849 2.39 0.27 + 0.27 1.565.850.00
217HD 4732 c27322842 2.37 0.38 + 0.38 1.747.0924.40
218HD 4732 b360.22842 2.37 0.34 + 0.34 1.747.0947.30
219HD 73526 b188.95226 2.25 0.12 + 0.12 1.146.5482.70
220HD 98736 b968.84850 2.33 0.78 + 0.78 0.923.0852.00
221HD 82886 b7051507 2.33 0.33 + 0.33 2.537.728.70
222HD 62509 b (beta Gem)589.649100 2.30 0.45 + 0.45 2.120.641.00
223HD 47366 b359.152719 2.30 0.13 + 0.18 2.1914.739.01
224HD 147873 c491.543995 2.30 0.18 + 0.18 1.382.6047.90
225GJ 328 b41003753 2.30 0.13 + 0.13 0.696.0040.00
226HD 145934 b27306135 2.28 0.26 + 0.26 1.7487.822.90
227GJ 876 b61.1174600 2.2756 0.0045 + 0.0045 0.322.96214.00
228HR 810 b (HD 17051 b)302.81976 2.27 0.25 + 0.25 1.3410.457.10
229HD 145457 b176.31389 2.23 0.42 + 0.42 1.239.770.60
230HD 216437 b13346055 2.223 0.058 + 0.058 1.16510.0039.08
231HD 159868 b1184.13400 2.22 0.059 + 0.059 1.195.837.92
232HD 163607 c12724840 2.201 0.037 + 0.037 1.122.0038.37
233HD 180053 b213.722812 2.194 0.064 + 0.064 1.7513.851.50
234HD 13931 b42184394 2.20 0.21 + 0.21 1.33.3123.30
235HD 73526 c379.15226 2.19 0.12 + 0.12 1.146.5465.10
236NGC 2682 Sand 978 b511.211826 2.18 0.17 + 0.17 1.3712.945.48
237HD 4203 c67004715 2.17 0.52 + 0.52 1.253.9322.20
238HD 136418 b464.31040 2.14 0.15 + 0.15 1.485.0044.70
239HD 222155 b39994847 2.12 0.5 + 0.5 1.2111.0024.20
240HD 147018 b44.2362290 2.12 0.07 + 0.07 0.9277.39145.33
241HIP 79431 b111.7179 2.10 0.035 + 0.035 0.423.9149.50
242HD 192699 b340.941845 2.096 0.093 + 0.093 1.3810.549.30
243Kepler-48 e9821135 2.067 0.079 + 0.079 0.883.0045.83
244HD 206610 b673.2875 2.036 0.065 + 0.065 1.554.835.40
245HD 8574 b2273609 2.03 0.14 + 0.14 1.3414.258.30
246HD 65216 c53705371 2.03 0.11 + 0.11 0.8742.8426.00
2476 Lyn b (HD 45410 b)934.31826 2.01 0.077 + 0.077 1.449.3132.80
248HD 82943 c219.32300 2.01 0.001 + 0.001 1.087.8843.60
249HD 89307 b21994818 2.00 0.4 + 0.4 1.038.432.40
250HD 164604 b641.53100 2.00 0.26 + 0.26 0.778.260.66
251HD 70642 b21256300 1.99 0.018 + 0.018 1.0783.9930.40
252HD 187123 c38103543 1.99 0.25 + 0.25 12.525.50
253HD 20868 b380.851705 1.99 0.05 + 0.05 0.781.7100.34
25424 Sex b452.81907 1.99 0.26 + 0.38 1.546.840.00
255HD 190647 b11763500 1.985 0.033 + 0.033 1.0692.0037.51
256ups And c241.2587383 1.981 0.019 + 0.019 1.313.7668.14
257HIP 107773 b144.31674 1.98 0.21 + 0.21 2.4212.0042.70
258HD 68988 b6.277513 1.97 0.1 + 0.1 1.284.36184.40
259HD 210702 b354.11739 1.97 0.11 + 0.18 1.858.8237.45
260HD 98219 b433.81484 1.964 0.1 + 0.1 1.413.642.00
261HD 33844 b551.42408 1.96 0.12 + 0.12 1.787.333.50
262HD 12648 b133.61693 1.96 0.22 + 0.22 1.229.80102.00
263HD 4313 b356.21911 1.927 0.09 + 0.09 1.633.740.30
264HD 117207 b2621.756364 1.926 0.034 + 0.034 1.0533.427.78
265HD 159243 c248.4767 1.90 0.13 + 0.13 1.12512.4056.60
266Pr0211 b2.146791 1.88 0.03 + 0.03 0.93526.00309.70
267HD 47366 c682.852719 1.88 0.12 + 0.14 1.8114.725.86
268HD 152581 b686.51478 1.869 0.071 + 0.071 1.34.736.20
269gam Cep b903.310,800 1.85 0.16 + 0.16 1.47.7031.10
2707 CMa b735.16791 1.85 0.06 + 0.04 1.348.2034.30
271HD 9446 c192.9851 1.82 0.17 + 0.17 115.1063.90
272HD 4203 b4374715 1.82 0.05 + 0.05 1.253.9352.82
273HD 160691 c4205.82987 1.814 0.19 + 0.19 1.083.3421.79
274kap CrB b (HD 142091 b)12853353 1.811 0.057 + 0.057 1.54.826.18
275HD 131496 b8961465 1.8 0.1 + 0.1 1.346.331.60
276alf Ari b380.82320 1.80 0.2 + 0.2 1.517.8041.10
277HD 158038 b5211465 1.80 0.2 + 0.2 1.654.733.90
278HD 45350 b963.62265 1.79 0.14 + 0.14 1.029.1058.00
279HD 74156 b51.6383408 1.78 0.04 + 0.04 1.23812.8108.00
280HD 87883 b27543833 1.78 0.34 + 0.34 0.829.234.70
281HD 233832 b20585569 1.78 0.08 + 0.06 0.71538.29
28216 Cyg B b798.52899 1.78 0.08 + 0.08 1.0810.650.50
283HD 128311 b453.0194566 1.769 0.023 + 0.023 0.8289.3755.60
284HD 72490 b8582922 1.768 0.08 + 0.08 1.216.4333.50
285HD 5319 b6413574 1.76 0.07 + 0.07 1.517.1831.60
286HD 33844 c9162408 1.75 0.18 + 0.18 1.787.325.40
287HD 82943 b441.22300 1.75 0.001 + 0.001 1.087.8866.00
288ome Ser b277.024217 1.70 0.12 + 0.12 2.171731.80
289HD 167042 b420.771925 1.70 0.09 + 0.12 1.57.6832.16
290BD+48 740 b7333492 1.70 0.7 + 0.7 1.0921.754.00
291HD 149143 b4.072286 1.69 0.14 + 0.14 1.734.72149.60
292HD 160691 b643.252987 1.68 0.001 + 0.001 1.083.3437.78
293HD 142415 b386.31529 1.67 0.12 + 0.12 1.0710.651.30
294TAP 26 b10.7972 1.66 0.31 + 0.31 1.0451149.00
295HD 86081 b2.13887 1.64 0.09 + 0.09 1.394.38207.70
296HD 23127 b12145959 1.64 0.18 + 0.18 1.421127.50
29747 UMa d140027175 1.64 0.29 + 0.48 1.036.513.80
298HD 4917 b400.53307 1.615 0.093 + 0.093 1.32737.10
299HD 221585 b11733983 1.61 0.14 + 0.14 1.194.0427.90
300HD 100655 b157.61599 1.61 0.34 + 0.34 2.2811.235.20
301HD 134987 b258.184195 1.59 0.02 + 0.02 1.073.349.50
302HD 180902 b479830 1.60 0.2 + 0.2 1.523.334.25
303HD 42012 b857.54400 1.60 0.1 + 0.1 0.838.6139.00
304HD 129445 b18402153 1.60 0.6 + 0.6 0.997.338.00
305BD+49 828 b25903134 1.60 0.4 + 0.2 1.5211.618.80
306HAT-P-11 c34073614 1.60 0.09 + 0.08 0.8094.9830.90
307HD 200964 b606.31862 1.599 0.067 + 0.067 1.397.630.90
308NGC 2682 Sand 364 b120.953729 1.57 0.11 + 0.11 1.3515.9356.94
309HIP 109384 b499.482776 1.56 0.08 + 0.08 0.785.856.53
310HD 4113 b526.622922 1.56 0.04 + 0.04 0.998.497.10
311HD 27442 b428.11096 1.56 0.14 + 0.14 1.236.532.20
312HD 222076 b8712330 1.56 0.11 + 0.11 1.075.931.90
313HD 6718 b24962028 1.56 0.11 + 0.1 0.961.7924.10
314eps Eri b25028784 1.55 0.24 + 0.24 0.8311.718.50
315HD 30856 b8471294 1.547 0.091 + 0.091 1.175.229.90
316HD 28678 b380.21294 1.542 0.073 + 0.073 1.536.132.90
317GJ 317 c53122535 1.54 1.26 + 0.57 0.428.5030.00
318psi 1 Dra B b31176233 1.53 0.1 + 0.1 1.197.0521.00
319HD 48265 b778.513834 1.525 0.05 + 0.05 1.3123.3828.65
320HD 102329 c11232421 1.52 0.3 + 0.25 1.33.327.40
321HD 121504 b63.331496 1.51 0.13 + 0.13 1.1811.655.80
322omi CrB b187.833504 1.50 0.13 + 0.13 2.1316.432.25
323HD 33142 b326.61294 1.50 0.22 + 0.22 1.788.330.40
324HD 188015 b461.21322 1.50 0.13 + 0.13 1.094.337.60
325HD 190360 b28684346 1.495 0.1542 + 0.1542 0.983.123.39
326HD 27631 b21985550 1.494 0.042 + 0.042 0.9445.2924.51
327HD 132563 b15443645 1.49 0.09 + 0.09 1.08112.726.70
328HD 177830 b406.65180 1.49 0.03 + 0.03 1.473.8531.60
329HD 20782 b597.064111 1.488 0.105 + 0.107 1.022.34118.43
330HD 10442 b1032.33386 1.487 0.076 + 0.076 1.015.9829.90
331HD 216536 b148.62756 1.47 0.2 + 0.12 1.362350.00
332BD+14 4559 b268.941265 1.47 0.06 + 0.06 0.8611.4355.21
333HD 50499 b2483.77268 1.45 0.08 + 0.08 1.311018.94
334HD 220773 b3724.73311 1.45 0.3 + 0.3 1.166.5720.00
335HD 133131 A b6494460 1.42 0.04 + 0.04 0.959.3836.52
336HIP 5158 b345.721901 1.42 0.274 + 0.274 0.782.4759.00
337HD 5608 b792.63190 1.40 0.095 + 0.095 1.556.323.50
338HD 208897 b352.72760 1.40 0.08 + 0.08 1.2518.1334.70
339HD 116029 b6701487 1.40 0.29 + 0.29 0.836.936.60
340HD 99706 b8682418 1.39 0.24 + 0.24 1.74.222.40
341HIP 67851 b88.94017 1.38 0.15 + 0.15 1.638.945.50
342XO-2 S c120.8384 1.37 0.053 + 0.053 0.983.157.68
343HD 2952 b311.63219 1.37 0.26 + 0.26 1.9712.426.30
344HD 205739 b279.81209 1.37 0.07 + 0.09 1.228.6742.00
345HD 19994 b466.23367 1.37 0.12 + 0.12 1.3651429.30
346HD 79498 b1966.12661 1.34 0.07 + 0.07 1.065.1326.00
347HD 141399 c201.992566 1.33 0.08 + 0.08 1.074.844.20
348HD 231701 b141.61095 1.31 0.18 + 0.18 1.525.939.00
3498 UMi b93.41888 1.31 0.16 + 0.16 1.4417.246.10
350HD 217107 b7.1273470 1.30 0.05 + 0.05 111139.20
351HD 148427 b331.52748 1.30 0.17 + 0.17 1.64727.70
352HD 108874 b395.82850 1.29 0.06 + 0.06 14.137.00
353HIP 14810 c147.7471112 1.31 0.18 + 0.18 0.813.2950.91
354HD 116029 c9072252 1.27 0.15 + 0.15 1.33520.70
355HD 94834 b15763100 1.26 0.17 + 0.17 1.116.3220.70
356HD 216435 b13911560 1.26 0.18 + 0.18 1.256.6920.00
357HD 95089 b5072422 1.26 0.085 + 0.085 1.547.625.00
358WASP-47 c588.5494 1.253 0.029 + 0.029 1.043.730.00
359HD 142 b349.75067 1.25 0.15 + 0.15 1.23211.233.20
360HD 210277 b442.1433 1.29 0.05 + 0.05 1.016.138.90
361HD 148164 b328.554383 1.23 0.25 + 0.25 1.215.6239.60
362HD 30562 b11573691 1.22 0.14 + 0.14 1.127.5833.70
363HD 200964 c852.51862 1.214 0.072 + 0.072 1.397.621.50
364HD 147513 b528.41690 1.21 0.074 + 0.074 1.075.729.30
365HD 143105 b2.197422 1.21 0.06 + 0.06 1.5110.6144.00
366HD 52265 b119.27509 1.21 0.05 + 0.05 1.20410.142.97
367HD 73534 b17701765 1.20 0.1 + 0.1 1.43.3616.20
368HD 65216 b613.15371 1.18 0.06 + 0.06 0.882.8433.70
369HD 141399 d1069.82566 1.18 0.08 + 0.08 1.074.822.63
370HD 28254 b11161989 1.16 0.1 + 0.06 1.062.1937.30
371HD 100777 b383.7857 1.16 0.03 + 0.03 11.734.90
372HD 5319 c8863574 1.15 0.08 + 0.08 1.567.1818.80
373HD 75784 b341.73694 1.15 0.3 + 0.3 1.414.6326.70
374HD 130322 b10.7095178 1.15 0.025 + 0.025 0.9214.6112.50
375HD 114386 b937.71550 1.14 0.13 + 0.13 0.610.234.30
376HD 159243 b12.62767 1.13 0.05 + 0.05 1.12512.491.10
377HD 14787 b676.63287 1.121 0.069 + 0.069 1.435.320.70
378HD 220689 b2266.45200 1.118 0.035 + 0.035 1.0164.8517.12
379HD 9174 b11791723 1.11 0.14 + 0.14 1.032.220.80
380BD+15 2940 b137.482362 1.11 0.11 + 0.11 1.117.8942.70
381HD 114783 b493.73208 1.10 0.06 + 0.06 0.8536.331.90
382HIP 91258 b5.05146 1.09 0.21 + 0.21 0.975.97130.90
383BD+15 2375 b153.224103 1.061 0.27 + 0.27 1.0822.8238.30
384HD 60532 b201.91960 1.06 0.08 + 0.08 1.54.6629.10
385rho CrB b39.8463360 1.045 0.024 + 0.024 0.8892.5767.28
386gam Lib b415.25234 1.02 0.14 + 0.14 1.4716.4122.00
387HD 219415 b2093.32653 1.00 0.12 + 0.12 18.818.20
388HD 154345 b35386511 1.00 0.3 + 0.3 0.88417.00
389HD 108874 c16242850 0.99 0.06 + 0.06 14.118.20
390HD 1605 b577.93281 0.96 0.06 + 0.04 1.316.419.80
391HD 185269 b6.838749 0.94 0.046 + 0.046 1.2810.191.00
392HD 10647 b989.24748 0.94 0.08 + 0.08 1.118.9218.10
393HD 113538 c18183771 0.93 0.06 + 0.06 0.5853.522.60
394HD 86226 b16954680 0.92 0.1 + 0.1 1.066.8815.30
395HD 102956 b6.4951164 0.92 0.07 + 0.07 1.59673.40
396HD 285507 b6.088194 0.917 0.033 + 0.033 0.73415125.80
397HD 179949 b3.093735 0.916 0.076 + 0.076 1.217.7112.60
398HD 25171 b1802.34100 0.915 0.011 + 0.012 1.0762.414.56
399GJ 849 b19246210 0.911 0.036 + 0.036 0.493.7223.96
400BD+48 738 b392.62480 0.91 0.074 + 0.074 0.741631.90
40124 Boo b30.354808 0.91 0.13 + 0.1 0.9926.5159.90
402HD 96063 b361.11398 0.90 0.1 + 0.1 1.025.425.90
403HD 128356 b298.22633 0.89 0.07 + 0.07 0.653.936.90
4047 Cma c9966791 0.87 0.06 + 0.06 1.348.214.90
405HD 17674 b623.86709 0.87 0.07 + 0.06 0.988.2421.10
406HD 13908 b19.3821589 0.865 0.035 + 0.035 1.299.655.30
40724 Sex c8831907 0.86 0.35 + 0.22 1.546.814.50
408HD 155358 b194.33723 0.85 0.05 + 0.05 0.926.1432.00
409HD 148156 b10272168 0.85 0.06 + 0.05 1.223.6917.50
410Kepler-68 d6251207 0.84 0.05 + 0.05 1.19419.06
411HD 38529 b14.313745 0.839 0.03 + 0.03 1.47711.856.10
412HD 187085 b1019.745811 0.836 0.011 + 0.011 1.1895.5115.39
41355 Cancri b14.6528476 0.8306 0.0033 + 0.0033 0.9053.571.40
414HD 114729 b1121.796500 0.825 0.007 + 0.007 0.9363.9316.91
415HD 155358 c391.93723 0.82 0.07 + 0.07 0.926.1424.90
416HD 134987 c50004195 0.82 0.03 + 0.03 1.073.39.30
417GJ 179 b22883626 0.82 0.07 + 0.07 0.3579.5125.80
418HD 4208 b832.976562 0.81 0.014 + 0.015 0.8836.3619.03
419HD 197037 b1035.73924 0.79 0.05 + 0.05 1.11815.50
420HIP 65407 c67.31517 0.784 0.054 + 0.054 0.937.541.50
421HD 163607 b75.224840 0.784 0.01 + 0.01 1.12252.34
422HD 109246 b68.271120 0.77 0.09 + 0.09 1.017.738.20
423HD 159868 c3513400 0.768 0.044 + 0.044 1.195.820.00
424HD 156411 b842.22231 0.74 0.05 + 0.04 1.252.9414.00
425HD 192263 b24.3564799 0.733 0.015 + 0.015 0.80712.4259.30
426HD 207832 c1155.72722 0.73 0.18 + 0.05 0.948.4315.30
427GJ 876 c30.0884600 0.71 0.0039 + 0.0039 0.322.9688.34
428HD 224693 b26.73562 0.71 0.035 + 0.035 1.334.0740.20
429HD 9446 b30.052851 0.70 0.06 + 0.06 115.146.60
430HD 32963 b23725838 0.70 0.03 + 0.03 1.032.6411.10
431HD 209458 b3.5251885 0.699 0.007 + 0.007 1.2314.985.10
432HD 37124 d18624810 0.696 0.059 + 0.059 0.854.0312.80
433GJ 832 b36605570 0.689 0.16 + 0.16 0.451.615.40
434ups And b4.6177383 0.6876 0.0044 + 0.0044 1.313.7670.51
435HD 96167 b498.91832 0.68 0.18 + 0.18 1.314.620.80
436HD 8535 b13132220 0.68 0.07 + 0.04 1.132.4911.80
437HD 37124 b154.3784810 0.675 0.017 + 0.017 0.854.0328.50
438HD 211810 b15584052 0.67 0.44 + 0.44 1.032.5515.60
439Kepler-65 e258.72229 0.653 0.056 + 0.055 1.2486.0519.10
440HD 27894 b18.024748 0.665 0.009 + 0.007 0.82.0459.80
441HD 170469 b11452544 0.66 0.11 + 0.11 1.14.1812.00
442HD 141399 e50002566 0.66 0.1 + 0.1 1.074.88.80
443HD 45364 c342.851583 0.658 0.013 + 0.013 0.821.41721.92
444HD 37124 c885.54810 0.652 0.052 + 0.052 0.854.0315.40
445HD 216770 b118.45827 0.65 0.04 + 0.04 0.97.830.90
446HD 63765 b3583934 0.64 0.05 + 0.05 0.8653.4120.90
447HD 181433 c9621757 0.64 0.016 + 0.016 0.781.0616.20
448HD 34445 b1056.76830 0.629 0.028 + 0.028 1.073.4312.01
449HD 11964 b19454378 0.622 0.056 + 0.056 1.083.19.41
450HD 330075 b3.388204 0.62 0.004 + 0.004 0.72107.00
451HD 103720 b4.5563353 0.62 0.025 + 0.025 0.79413.189.00
452WASP-94 B b2.008625 0.618 0.028 + 0.029 1.247.1686.48
453HD 175541 b297.33685 0.61 0.087 + 0.87 1.655.614.00
454HD 43197 b327.81943 0.60 0.12 + 0.04 0.961.4432.40
455BD-10 3166 b3.488414 0.59 0.07 + 0.07 1.475.760.90
456HD 44219 b472.31988 0.58 0.06 + 0.04 12.3919.40
457HIP 14810 d981.81112 0.59 0.1 + 0.1 0.813.2912.17
458HD 207832 b161.972722 0.56 0.06 + 0.03 0.948.4322.10
459Pr0201 b4.426101 0.54 0.12 + 0.12 1.242658.10
460HD 181433 d21721757 0.54 0.043 + 0.043 0.781.0611.30
46147 UMa c23917175 0.54 0.066 + 0.073 1.036.58.00
462BD-11 4672 b16673271 0.53 0.05 + 0.05 0.5712.913.40
463HIP 57274 d431.71004 0.5267 0.03 + 0.03 0.733.1518.20
464HD 187123 b3.0973810 0.523 0.043 + 0.043 12.514.91
465HD 160691 e310.552987 0.522 0.001 + 0.001 1.083.3414.91
466HD 7449 b1255.54452 0.508 0.111 + 0.111 1.0534.2121.90
467HD 99109 b439.32575 0.502 0.07 + 0.07 0.936.2814.10
468HD 31253 b4664461 0.50 0.07 + 0.07 1.234.2312.00
469HD 210193 b649.93161 0.482 0.073 + 0.073 1.042.911.40
470HD 2638 b3.444401 0.48 0.003 + 0.003 0.933.367.40
471HD 164509 b282.42153 0.48 0.09 + 0.09 1.134.914.20
472HD 114613 b38275637 0.48 0.04 + 0.04 1.3643.95.52
473HD 30669 b16843799 0.47 0.06 + 0.06 0.923.68.60
474HD 45652 b43.6484 0.47 0.035 + 0.035 0.838.933.10
47551 Peg b4.2313278 0.468 0.007 + 0.007 1.1211.857.30
476GJ 3512 b203.59867 0.463 0.022 + 0.023 0.1233.2771.84
477NGC 2682 YBP 401 b4.0872493 0.46 0.05 + 0.05 1.1412.7449.06
478HD 141399 b94.442566 0.451 0.03 + 0.03 1.074.819.23
479HD 212301 b2.246723 0.45 0.005 + 0.005 1.276.759.50
480HD 208487 c9092650 0.45 0.11 + 0.13 1.054.410.10
481HD 102195 b4.114435 0.45 0.014 + 0.014 0.876.163.00
482HD 6434 b22.0175444 0.44 0.01 + 0.01 0.897.7735.00
483DMPP-2 b5.2074508 0.437 0.03 + 0.059 1.4417.3540.26
484HIP 65407 b28.1251517 0.428 0.032 + 0.032 0.937.530.50
485HD 133131A c35684460 0.42 0.15 + 0.15 0.959.386.89
486HD 75289 b3.51335 0.42 0.008 + 0.008 1.157.4454.00
487HD 126614A b12444029 0.41 0.06 + 0.06 1.263.997.30
488HD 208487 b129.972650 0.48 0.06 + 0.06 1.054.419.30
489HIP 57274 c32.031004 0.409 0.009 + 0.009 0.733.1532.40
490HD 11506 c223.413574 0.408 0.057 + 0.057 1.244.812.10
491NGC 2682 YBP 1514 b5.1181506 0.40 0.11 + 0.11 0.9614.652.29
492HD 108147 b10.9011065 0.40 0.011 + 0.011 1.279.236.00
493HD 181720 b9562239 0.40 0.06 + 0.06 1.031.378.40
494HD 63454 b2.8182215 0.398 0.01 + 0.01 0.846.8464.19
495HD 83443 b2.9861455 0.38 0.003 + 0.003 0.9958.10
496HD 34445 g57006830 0.38 0.13 + 0.13 1.073.434.08
497HD 93083 b143.58383 0.37 0.01 + 0.01 0.7218.30
498HD 103774 b5.8882734 0.367 0.022 + 0.022 1.33511.4334.30
499HD 149026 b2.877277 0.36 0.03 + 0.03 1.33.843.30
500HD 113538 b663.23771 0.36 0.04 + 0.04 0.5853.512.20
501HIP 12961 b57.4352226 0.36 0.07 + 0.07 0.693.924.70
502HD 102843 b30903009 0.3584 0.0456 + 0.0456 0.951.65.24
503HD 47186 c1353.61583 0.3506 0.075 + 0.075 0.990.916.65
504NGC 2682 YBP 1194 b6.9581889 0.34 0.05 + 0.05 1.0111.5537.72
505HD 219134 h22476837 0.34 0.02 + 0.02 0.7942.2236.10
506HD 38283 b363.23013 0.34 0.02 + 0.02 1.0854.310.00
507HD 164922 b12017017 0.3385 0.0154 + 0.0151 0.8742.637.15
508HD 33283 b18.179738 0.33 0.026 + 0.026 1.243.625.20
509HD 564 b492.34008 0.33 0.03 + 0.03 0.9612.98.79
510HD 215497 c567.941855 0.33 0.02 + 0.02 0.8721.7510.10
511BD-08 2823 c237.61826 0.33 0.03 + 0.03 0.744.313.40
512GJ 649 b598.33702 0.328 0.032 + 0.032 0.544.212.40
513GJ 1148 b41.386158 0.3043 0.0044 + 0.0032 0.3443.7138.37
514HD 101930 b70.46382 0.30 0.007 + 0.007 0.741.818.10
515HD 88133 b3.415185 0.29 0.02 + 0.02 1.205.335.70
516HD 7199 b6152579 0.29 0.023 + 0.023 0.892.637.80
517HD 109749 b5.24537 0.28 0.016 + 0.016 1.232.7728.30
518HD 168746 b6.404880 0.27 0.02 + 0.02 1.079.828.60
519HD 204941 b17332180 0.266 0.032 + 0.032 0.741.315.90
520HD 16141 b75.5231220 0.26 0.02 + 0.02 1.113.2412.00
521XO-2S b18.157384 0.259 0.014 + 0.014 0.983.120.64
522HD 46375 b3.024516 0.249 0.03 + 0.03 12.5935.20
523HD 126525 b960.44323 0.237 0.002 + 0.002 0.8972.55.26
524Kepler-25 d122.42945 0.226 0.031 + 0.031 1.196.149.67
525HD 76700 b3.9711244 0.233 0.024 + 0.024 1.136.227.60
526HD 3651 b62.2187376 0.229 0.008 + 0.008 0.8826.315.90
527HD 137388 b3302054 0.223 0.029 + 0.029 0.862.397.90
528GJ 1148 c532.586158 0.2141 0.0154 + 0.0069 0.3443.7111.34
529HD 218566 b225.75053 0.21 0.02 + 0.02 0.853.488.30
530HD 8326 b158.9913152 0.21 0.062 + 0.062 0.82.49.36
531HD 21411 b84.2883217 0.207 0.081 + 0.081 0.893.511.48
532HD 10180 h22222428 0.203 0.014 + 0.014 1.061.273.04
533HD 220197 b17281887 0.20 0.07 + 0.04 0.912.623.78
534HD 117618 b25.8276264 0.19 0.04 + 0.04 1.176.1612.80
535HD 45364 b226.931583 0.1872 0.0036 + 0.0036 0.821.4177.22
536HD 104067 b55.8062272 0.186 0.013 + 0.013 0.7914.611.56
537HD 102117 b20.82299 0.18 0.03 + 0.03 0.953.312.00
53855 Cancri c44.4188476 0.1714 0.0055 + 0.0055 0.9053.5310.18
539BD-06 1339 c125.942955 0.17 0.03 + 0.03 0.74.39.10
540HD 34445 c214.676830 0.168 0.016 + 0.016 1.073.435.45
541HD 27894 c36.074748 0.162 0.011 + 0.04 0.82.0411.57
542HD 177830 c110.95180 0.15 0.02 + 0.02 1.473.855.10
54355 Cancri f2628476 0.141 0.012 + 0.012 0.9056.3434.87
544HD 85390 b7882374 0.132 0.011 + 0.011 0.761.153.82
545HD 49674 b4.948452 0.12 0.02 + 0.02 15.5514.00
546HD 34445 f676.86830 0.119 0.021 + 0.021 1.073.432.74
547GJ 15A c76007310 0.11 0.08 + 0.06 0.383.082.50
548HD 206255 b96.0453099 0.108 0.022 + 0.022 1.422.33.92
549GJ 433 c50947305 0.102 0.02 + 0.02 0.482.11.75
550HD 103197 b47.842235 0.098 0.006 + 0.006 0.91.45.90
551HD 34445 d117.876830 0.097 0.013 + 0.013 1.073.433.81
552GJ 163 d6043068 0.0925 0.0091 + 0.0091 0.42.024.42
553HD 134060 c1291.564083 0.0922 0.0139 + 0.0133 1.0951.641.65
554HD 147379 b (GJ 617A)86.782185 0.08984 0.0047 + 0.046 0.63.875.83
555HD 179079 b14.4761580 0.0866 0.008 + 0.008 1.153.886.64
556HD 10180 e49.7472428 0.079 0.0038 + 0.0038 1.061.274.19
557HD 99492 b17.0546756 0.079 0.006 + 0.006 0.854.336.98
558HD 11964 c37.914378 0.0788 0.0097 + 0.0097 1.083.14.65
559rho CrB c102.543360 0.0787 0.0063 + 0.0063 0.8892.573.74
560HD 38677 b (DMPP-1 b)18.57763 0.0764 0.0037 + 0.005 1.211.15.16
561HD 192310 c525.82348 0.076 0.016 + 0.016 0.80.922.27
562HD 109271 c30.932683 0.076 0.007 + 0.007 1.0472.054.90
563HD 10180 f122.722428 0.0752 0.0044 + 0.0044 1.061.272.98
564GJ 3293 b30.5992300 0.0741 0.0028 + 0.0028 0.422.788.60
56561 Vir d (HD 115617 d)1231571 0.072 0.008 + 0.008 0.9422.173.25
566HD 47186 b4.0851583 0.0717 0.0014 + 0.0014 0.990.919.12
567Kepler-19 d62.95867 0.0708 0.0038 + 0.0176 0.9362.94.00
568HD 16417 b17.243874 0.0696 0.0063 + 0.0063 1.22.65.00
569HD 10180 g6022428 0.0673 0.0107 + 0.0107 1.061.271.59
570GJ 436 b2.6441645 0.067 0.007 + 0.007 0.415.2618.10
571HD 219134 d46.716837 0.067 0.004 + 0.004 0.7942.2234.40
572GJ 3293 c122.62300 0.0664 0.004 + 0.004 0.422.784.89
573HD 219828 b3.8355169 0.0661 0.0044 + 0.0044 1.231.647.53
574HD 190360 c17.1194346 0.0638 0.01 + 0.01 0.983.15.20
575Kepler-20 g34.942262 0.0628 0.0097 + 0.0114 0.9485.394.10
576HD 213885 c4.7853728 0.0628 0.0043 + 0.0043 1.06857.26
577GJ 96 b73.941896 0.0619 0.0076 + 0.0072 0.63.374.69
578HIP 71135 b87.193009 0.0592 0.0129 + 0.0129 0.663.13.71
579GJ 687 b38.146077 0.058 0.007 + 0.007 0.4136.626.40
580HD 125612 c4.1552016 0.058 0.01 + 0.01 1.0913.76.46
581HD 90156 b49.771607 0.057 0.005 + 0.005 0.841.233.69
58261 Vir c (HD 115617 c)38.0211571 0.057 0.003 + 0.003 0.9422.173.62
583HD 69830 d197826 0.057 0.005 + 0.005 0.860.812.20
584HD 64114 b45.7912633 0.056 0.011 + 0.011 0.951.63.33
585HD 204313 c34.9054748 0.0553 0.0053 + 0.0053 1.031.323.42
586HD 21693 c53.7364106 0.0547 0.0056 + 0.0056 0.82.053.44
587HD 109271 b7.8542683 0.054 0.004 + 0.004 1.0472.055.60
588HD 192310 b74.722348 0.0532 0.0028 + 0.0028 0.80.923.00
589HD 34445 e49.1756830 0.0529 0.0089 + 0.0089 1.073.432.75
590GJ 4276 b13.352774 0.052 0.003 + 0.003 0.4062.468.79
591HD 164595 b40809 0.0508 0.0086 + 0.0086 0.992.33.05
592HD 77338 b5.7362636 0.05 0.015 + 0.017 0.931.746.00
593HD 4308 b15.56680 0.05 0.0025 + 0.0025 0.931.34.10
594HD 102365 b122.14545 0.05 0.008 + 0.008 0.852.532.30
595GJ 581 b5.3686509 0.0478 0.0007 + 0.0009 0.312.9112.35
596GJ 876 e124.264600 0.046 0.005 + 0.005 0.322.963.42
597HD 20003 c33.9244063 0.0454 0.0046 + 0.0044 0.8751.653.15
598HD 42618 b149.616967 0.0453 0.0079 + 0.0076 1.0152.341.89
599HD 51608 c95.9454158 0.045 0.005 + 0.005 0.81.62.36
600BD-08 2823 b5.61826 0.045 0.007 + 0.007 0.744.36.50
601HD 31527 c51.2054135 0.0445 0.004 + 0.004 0.961.412.51
602HD 20781 e85.5074093 0.0442 0.0049 + 0.0049 0.71.452.60
603CoRoT-7 c3.7109+25 0.043 0.003 + 0.003 0.91.966.01
604LSPM J2116+0234 b14.44882 0.0418 0.0031 + 0.0035 0.434.146.26
605HD 10180 c5.762428 0.0412 0.0017 + 0.0017 1.061.274.50
606HD 125595 b9.6742075 0.041 0.004 + 0.004 0.7563.224.79
607GJ 378 b3.822880 0.041 0.0064 + 0.0063 0.564.867.96
608HD 211970 b25.2013102 0.0409 0.0079 + 0.0079 0.612.74.02
609HD 164922 c75.7657017 0.0406 0.005 + 0.005 0.8742.632.22
610HD 51608 b14.0734158 0.0402 0.0038 + 0.0037 0.81.63.95
611HIP 35173 b41.5163269 0.04 0.0085 + 0.0085 0.7922.80
612HIP 54373 c15.1443064 0.03914 0.00664 + 0.00664 0.574.24.84
613HD 45184 b5.8854160 0.0384 0.0033 + 0.0032 1.032.154.26
614HD 180617 b105.96100 0.0384 0.0031 + 0.0044 0.452.662.85
615HD 31527 d271.74135 0.0372 0.0053 + 0.0053 0.961.411.25
616HD 69830 c31.56826 0.0371 0.0022 + 0.0022 0.860.812.66
617HD 24085 b2.0463162 0.0371 0.0098 + 0.0098 1.2225.40
618HD 10180 d16.3572428 0.037 0.002 + 0.002 1.061.272.86
619HD 20003 b11.8484053 0.0367 0.0033 + 0.0033 0.8751.653.84
620HIP 57274 b8.1351004 0.0365 0.0041 + 0.0041 0.733.154.64
621HD 103949 b120.883064 0.0352 0.0072 + 0.0072 0.771.41.77
622GJ 674 b4.694820 0.035 0.0008 + 0.0008 0.353.278.70
623GJ 422 b20.1295200 0.0348 0.0035 + 0.0035 0.354.54.47
624HD 219134 g94.26837 0.034 0.004 + 0.004 0.7942.2231.80
625HD 136352 c27.5823993 0.034 0.0034 + 0.0033 0.811.352.65
626HD 20781 d29.1584093 0.0334 0.0038 + 0.0038 0.71.452.82
627GJ 163 b8.6323068 0.0334 0.0019 + 0.0019 0.42.026.13
628HD 160691 d9.6392987 0.0332 0.001 + 0.001 1.083.343.06
629GJ 3138 d257.82932 0.033 0.0072 + 0.0066 0.6812.61.47
630HD 31527 b16.5544135 0.0329 0.0028 + 0.0028 0.961.412.72
631HD 69830 b8.667826 0.0321 0.0014 + 0.0014 0.860.813.51
632HD 134060 b3.274083 0.0318 0.0025 + 0.0024 1.0951.644.61
633DMPP-1 c (HD 38677 c)6.584763 0.0302 0.0017 + 0.005 1.211.12.88
634HD 40307 d20.4321912 0.0299 0.0053 + 0.0047 0.771.162.75
635HD 176986 c16.8194821 0.0289 0.0031 + 0.0031 0.7892.52.63
636HD 285968 b (GJ 176 b)8.7764832 0.0285 0.0048 + 0.0022 0.4852.954.49
637GJ 685 b24.161605 0.0283 0.0053 + 0.0057 0.551.53.00
638HD 175607 b29.013390 0.0283 0.0035 + 0.0035 0.7122.37
639HD 219134 f22.8056837 0.028 0.003 + 0.003 0.7942.2232.30
640HD 45184 c13.1354160 0.0277 0.0034 + 0.0032 1.032.152.36
641HD 7924 b5.3984775 0.0273 0.0016 + 0.0016 0.8322.53.59
642HIP 54373 b7.763064 0.0271 0.0058 + 0.0058 0.573.14.19
643HD 136352 d107.63993 0.027 0.0037 + 0.0036 0.811.351.35
644HD 39855 b3.252271 0.027 0.005 + 0.005 0.871.84.08
645BD-06 1339 b3.8732955 0.027 0.004 + 0.004 0.74.34.40
646GJ 229A b526.1157262 0.0267 0.0064 + 0.0064 0.582.41.37
647HD 26965 b42.3785550 0.0266 0.0015 + 0.0015 0.782.61.81
648HD 97658 b9.4942016 0.026 0.004 + 0.004 0.782.782.90
649GJ 3082 b11.9492678 0.026 0.005 + 0.005 0.472.53.94
650GJ 3634 b2.646462 0.026 0.013 + 0.005 0.4525.59
651HD 21693 b22.6794106 0.0259 0.0034 + 0.0033 0.82.052.20
65255 Cancri e0.7378476 0.0256 0.0007 + 0.0007 0.9055.955.97
653GJ 676 A e35.393535 0.025 0.002 + 0.002 0.732.462.00
654HD 7924 c15.2994775 0.0247 0.0023 + 0.0022 0.8322.52.31
655Wolf 1061 d (GJ 628 d)217.214136 0.0242 0.0035 + 0.0033 0.2942.342.23
656HD 181433 b9.3741757 0.024 0.002 + 0.002 0.781.062.57
657GJ 3293 d48.1352300 0.0239 0.0033 + 0.0033 0.422.782.42
658GJ 180 d106.36182 0.0238 0.0034 + 0.0034 0.433.12.08
659K2-18 c8.962758 0.0236 0.0042 + 0.0042 0.3592.894.63
660GJ 1265 b3.651782 0.023 0.002 + 0.002 0.17839.89
661GJ 229A c121.9957262 0.0229 0.004 + 0.004 0.582.41.93
662GJ 3942 b6.9051203 0.0225 0.0019 + 0.0019 0.632.363.29
663GJ 686 b15.5327442 0.022 0.003 + 0.003 0.422.93.29
664Kapteyn c (GJ 191 c)121.543750 0.022 0.004 + 0.003 0.28122.27
665HD 3167 d8.509152 0.0217 0.0022 + 0.0022 0.873.162.39
666GJ 876 d1.9384600 0.0215 0.0013 + 0.0013 0.322.966.56
667GJ 163 c25.633068 0.021 0.003 + 0.003 0.42.022.75
668HD 40307 c9.6181912 0.0208 0.0035 + 0.0031 0.771.162.45
669GJ 3341 b14.2071456 0.0208 0.0003 + 0.0003 0.472.863.04
670GJ 536 b8.7084677 0.0205 0.0022 + 0.0013 0.5062.913.12
671GJ 180 b17.1336182 0.0204 0.0021 + 0.0021 0.433.13.25
672HD 1461 b5.7733725 0.0203 0.0019 + 0.0019 1.022.262.28
673HD 7924 d24.4514775 0.0203 0.0025 + 0.0025 0.8322.51.65
674HD 215497 b3.9341855 0.020 0.0023 + 0.0023 0.8721.753.00
675GJ 3998 c13.74869 0.0197 0.0025 + 0.0024 0.522.67
676GJ 357 d55.667779 0.019 0.003 + 0.003 0.3422.662.09
677GJ 433 b7.3717305 0.019 0.002 + 0.002 0.482.12.86
678HD 176986 b6.494821 0.0181 0.0021 + 0.0021 0.7892.52.56
679GJ 667 C b7.22201 0.018 0.001 + 0.001 0.331.843.90
680GJ 581 c12.9196509 0.0178 0.0012 + 0.0008 0.312.913.28
681HD 1461 c13.5053725 0.0176 0.0023 + 0.0023 1.022.261.49
682HD 20781 c13.894093 0.0168 0.0022 + 0.0021 0.71.451.81
683GJ 433 d36.0597305 0.0164 0.0029 + 0.0029 0.482.11.46
68461 Vir b (HD 115617 b)4.2151571 0.016 0.002 + 0.002 0.9422.172.12
685GJ 832 c35.675570 0.0157 0.0098 + 0.0098 0.451.61.79
686Kapteyn b (GJ 191 b)48.6163750 0.0151 0.0028 + 0.0031 0.28122.25
687HD 136352 b11.5823993 0.0151 0.0018 + 0.0018 0.811.351.59
688HD 20794 d90.3092610 0.015 0.002 + 0.002 0.70.820.85
689GJ 676 A d3.6013535 0.014 0.001 + 0.001 0.732.462.40
690GJ 3138 c5.9742932 0.0132 0.0019 + 0.0019 0.6812.61.93
691HD 38677 e (DMPP-1 e)5.516763 0.013 0.0021 + 0.0036 1.211.11.30
692GJ 667 C c28.12201 0.013 0.002 + 0.002 0.331.841.90
693HD 40307 b4.3121912 0.0126 0.0025 + 0.0022 0.771.161.94
694HD 219134 b3.0936837 0.012 0.001 + 0.001 0.7942.2231.90
695HD 219134 c6.7656837 0.011 0.002 + 0.002 0.7942.2231.40
696GJ 357 c9.1257779 0.0107 0.0014 + 0.0014 0.3422.662.13
697Wolf 1061 c (GJ 628 c)17.8724136 0.0107 0.0014 + 0.0013 0.2942.342.70
698HD 38677 d (DMPP-1 d)2.882763 0.0105 0.0012 + 0.0011 1.211.11.33
699GJ 3293 e13.2542300 0.0103 0.002 + 0.002 0.422.781.66
700GJ 15A b11.4417310 0.00953 0.00145 + 0.00138 0.383.081.68
701HD 20794 b18.3152610 0.0085 0.0009 + 0.0009 0.70.820.83
702GJ 3998 b2.65869 0.0078 0.0009 + 0.0009 0.521.82
703HD 20794 c40.1142610 0.0076 0.0013 + 0.0013 0.70.820.56
704HD 20781 b5.3144093 0.0061 0.0012 + 0.0011 0.71.45
705Wolf 1061 b (GJ 628 b)4.8874136 0.006 0.0008 + 0.0008 0.2942.34
706GJ 3138 b1.222932 0.0056 0.001 + 0.001 0.6812.6
707GJ 581 e3.1536509 0.0052 0.0008 + 0.0005 0.312.911.55
708HD 10180 b1.1782428 0.0044 0.0008 + 0.0008 1.061.27

References

  1. Archive: NASA Exoplanet Archive. 2021. Available online: https://exoplanetarchive.ipac.caltech.edu/ (accessed on 30 June 2022).
  2. Mordasini, C. Planetary Population Synthesis. In Handbook of Exoplanets; Deeg, H.J., Belmonte, J.A., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 2425–2474. [Google Scholar] [CrossRef]
  3. Emsenhuber, A.; Mordasini, C.; Burn, R.; Alibert, Y.; Benz, W.; Asphaug, E. The New Generation Planetary Population Synthesis (NGPPS). II. Planetary population of solar-like stars and overview of statistical results. Astron. Astrophys. 2021, 656, A70. [Google Scholar] [CrossRef]
  4. Yakovlev, O.Y.; Ananyeva, V.I.; Ivanova, A.E.; Tavrov, A.V. Comparison of the mass distributions of short-period exoplanets detected by transit and RV methods. Mon. Not. R. Astron. Soc. 2022, 509, L17–L20. [Google Scholar] [CrossRef]
  5. Dressing, C.D.; Charbonneau, D. The occurrence of potentially habitable planets orbiting M dwarfs estimated from the full Kepler dataset and an empirical measurement of the detection sensitivity. Astrophys. J. 2015, 807, 45. [Google Scholar] [CrossRef]
  6. Kunimoto, M.; Matthews, J.M. Searching the Entirety of Kepler Data. II. Occurrence Rate Estimates for FGK Stars. Astron. J. 2020, 159, 248. [Google Scholar] [CrossRef]
  7. Pinamonti, M.; Sozzetti, A.; Maldonado, J.; Affer, L.; Micela, G.; Bonomo, A.S.; Sánchez, R.Z. HADES RV Programme with HARPS-N at TNG. XV. Planetary occurrence rates around early-M dwarfs. Astron. Astrophys. 2022, A65, 21. [Google Scholar] [CrossRef]
  8. Ananyeva, V.I.; Ivanova, A.E.; Venkstern, A.A.; Tavrov, A.V.; Korablev, O.I.; Bertaux, J.L. The Dependence of the Mass Distribution of Exoplanets on the Spectral Class of Host Stars. Sol. Syst. Res. 2020, 54, 175–186. [Google Scholar] [CrossRef]
  9. Butler, R.P.; Wright, J.T.; Marcy, G.W.; Fischer, D.A.; Vogt, S.S.; Tinney, C.G.; Penny, A.J. Catalog of Nearby Exoplanets. Astrophys. J. 2006, 646, 505. [Google Scholar] [CrossRef]
  10. Marchi, S. Extrasolar Planet Taxonomy: A New Statistical Approach. Astrophys. J. 2007, 666, 475. [Google Scholar] [CrossRef]
  11. Tabachnik, S.; Tremaine, S. Maximum-likelihood method for estimating the mass and period distributions of extrasolar planets. Mon. Not. R. Astron. Soc. 2002, 335, 151–158. [Google Scholar] [CrossRef] [Green Version]
  12. Marcy, G.; Butler, R.P.; Fischer, D.; Vogt, S.; Wright, J.T.; Tinney, C.G.; Jones, H.R. Observed Properties of Exoplanets: Masses, Orbits, and Metallicities. Prog. Theor. Phys. Suppl. 2005, 158, 24–42. [Google Scholar] [CrossRef]
  13. Cumming, A.; Butler, R.P.; Marcy, G.W.; Vogt, S.S.; Wright, J.T.; Fischer, D.A. The Keck Planet Search: Detectability and the Minimum Mass and Orbital Period Distribution of Extrasolar Planets. Publ. Astron. Soc. Pac. 2008, 120, 531. [Google Scholar] [CrossRef]
  14. Howard, A.W.; Marcy, G.W.; Johnson, J.A.; Fischer, D.A.; Wright, J.T.; Isaacson, H.; Valenti, J.A.; Anderson, J.; Lin, D.N.C.; Ida, S. The occurrence and mass distribution of close-in super-Earths, Neptunes, and Jupiters. Science 2010, 330, 653–655. [Google Scholar] [CrossRef] [PubMed]
  15. Jiang, G.; Yeh, L.C.; Chang, Y.C.; Hung, W.L. On the Fundamental Mass-Period Functions of Extrasolar Planets. Astrophys. J. Suppl. Ser. 2010, 186, 48. [Google Scholar] [CrossRef]
  16. Tuomi, M.; Jones, H.R.A.; Butler, R.P.; Arriagada, P.; Vogt, S.S.; Burt, J.; Barnes, J.R. Frequency of planets orbiting M dwarfs in the Solar neighbourhood. Astrophys. J. Suppl. Ser. 2019, 1906, 04644. Available online: https://arxiv.org/pdf/1906.04644.pdf (accessed on 20 April 2022).
  17. Petigura, E.A.; Howard, A.W.; Marcy, G.W. Prevalence of Earth-size planets orbiting Sun-like stars. Proc. Natl. Acad. Sci. USA 2013, 110, 19273–19278. [Google Scholar] [CrossRef]
  18. Akeson, R.L.; Chen, X.; Ciardi, D.; Crane, M.; Good, J.; Harbut, M.; Zhang, A. The NASA exoplanet archive: Data and tools for exoplanet research. Publ. Astron. Soc. Pac. 2013, 125, 989–999. Available online: https://iopscience.iop.org/article/10.1086/672273/pdf (accessed on 24 June 2022). [CrossRef]
  19. Sturges, H.A. The choice of a class interval. J. Am. Stat. Assoc. 1926, 21, 65–66. Available online: https://www.jstor.org/stable/2965501?origin=JSTOR-pdf#metadata_info_tab_contents (accessed on 24 June 2022). [CrossRef]
  20. Ivanova, A.E.; Yakovlev, O.Y.; Ananyeva, V.I.; Shashkova, I.A.; Tavrov, A.V.; Bertaux, J.L. The ‘‘Detectability Window’’ Method to Take into Account Observational Selection in the Statistics of Exoplanets Discovered through Radial Velocity Measurements. Astron. Lett. 2021, 47, 43–49. [Google Scholar] [CrossRef]
  21. Rickman, E.L.; Ségransan, D.; Marmier, M.; Udry, S.; Bouchy, F.; Lovis, C.; Wyttenbach, A. The CORALIE survey for southern extrasolar planets. XVIII. Three new massive planets and two low-mass brown dwarfs at greater than 5 AU separation. Astron. Astrophys. 2019, 625, A71. [Google Scholar] [CrossRef]
  22. Feng, F.; Butler, R.P.; Shectman, S.A.; Crane, J.D.; Vogt, S.; Chambers, J.; Thompson, I.B. Search for Nearby Earth Analogs. II. Detection of Five New Planets, Eight Planet Candidates, and Confirmation of Three Planets around Nine Nearby M Dwarfs. Astrophys. J. Suppl. Ser. 2020, 246, 11. [Google Scholar] [CrossRef]
  23. Ma, B.; Ge, J.; Muterspaugh, M.; Singer, M.A.; Henry, G.W.; González Hernández, J.I.; Ealey, B. The first super-Earth detection from the high cadence and high radial velocity precision Dharma Planet Survey. Mon. Not. R. Astron. Soc. 2018, 480, 2411–2422. [Google Scholar] [CrossRef]
  24. Jorissen, A.; Mayor, M.; Udry, S. The distribution of exoplanet masses. Astron. Astrophys. 2001, 379, 3. [Google Scholar] [CrossRef]
  25. Ananyeva, V.I.; Tavrov, A.V.; Venkstern, A.A.; Churbanov, D.V.; Shashkova, I.A.; Korablev, O.I.; Bertaux, J.L. The Distribution of Giant Exoplanets over True and Minimum masses: Accounting for Observational Selection. Sol. Syst. Res. 2019, 53, 124–137. [Google Scholar] [CrossRef]
  26. Ivanova, A.E.; Ananyeva, V.I.; Venkstern, A.A.; Shashkova, I.A.; Yudaev, A.V.; Tavrov, A.V.; Bertaux, J.L. The mass distribution of transiting exoplanets corrected for observational selection effects. Astron. Lett. 2019, 45, 687–694. [Google Scholar] [CrossRef]
  27. Szabó, G.M.; Kiss, L.L. A short-period censor of sub-jupiter mass exoplanets with low density. Astrophys. J. Lett. 2011, 727, L44. [Google Scholar] [CrossRef]
  28. Mazeh, T.; Holczer, T.; Faigler, S. Dearth of short-period Neptunian exoplanets: A desert in period-mass and period-radius planes. Astron. Astrophys. 2016, 589, A75. [Google Scholar] [CrossRef]
  29. Boss, A. Giant Planet Formation by Gravitational Instability. Science 1997, 276, 5320. [Google Scholar] [CrossRef]
  30. Pollack, J.B.; Hubickyj, O.; Bodenheimer, P.; Lissauer, J.J.; Podolak, M.; Greenzweig, Y. Formation of the Giant Planets by Concurrent Accretion of Solids and Gas. Icarus 1996, 124, 1. [Google Scholar] [CrossRef]
  31. Moe, M.; Kratter, K.M. Impact of binary stars on planet statistics–I. Planet occurrence rates and trends with stellar mass. Mon. Not. R. Astron. Soc. 2021, 507, 3. [Google Scholar] [CrossRef]
  32. Fernandes, R.B.; Mulders, G.D.; Pascucci, I.; Mordasini, C.; Emsenhuber, A. Hints for a turnover at the snow line in the giant planet occurrence rate. Astrophys. J. 2019, 874, 81. [Google Scholar] [CrossRef] [Green Version]
  33. Mulders, G.D.; Pascucci, I.; Apai, D.; Ciesla, F.J. The Exoplanet Population Observation Simulator. I. Inn. Edges Planet. Syst. Astron. J. 2018, 156, 1. [Google Scholar] [CrossRef]
  34. Petigura, E.A.; Marcy, G.W.; Winn, J.N.; Weiss, L.M.; Fulton, B.J.; Howard, A.W.; Johnson, J. A The California-Kepler Survey. IV. Metal-rich Stars Host a Greater Diversity of Planets. Astron. J. 2018, 155, 2. [Google Scholar] [CrossRef]
  35. Bertaux, J.-L.; Ivanova, A. A numerical inversion of m sin i exoplanet distribution: The sub-Saturn desert is more depleted than observed and hint of a Uranus mass gap. Mon. Not. R. Astron. Soc. 2022, 512, 4. [Google Scholar] [CrossRef]
  36. Ho, S.; Turner, E.L. The Posterior distribution of sin (i) values for exoplanets with Mt sin (i) determined from radial velocity data. Astrophys. J. 2011, 739, 26. [Google Scholar] [CrossRef] [Green Version]
Figure 1. The detectability window W in the form of a map in the mP plane obtained with the coefficients γ = 0.8 and δ = 2.0 in (5a) and (5b). The upper and lower numbers in each of the cells (Δm, ΔP) present the number of known planets with the minimum mass and the orbital period in the corresponding interval and the probability of detecting planets with these parameters, respectively. The degree of shading a cell corresponds to the detection probability for this cell according to the scale on the right. The red dots show the positions of actually detected RV planets in the mP plane.
Figure 1. The detectability window W in the form of a map in the mP plane obtained with the coefficients γ = 0.8 and δ = 2.0 in (5a) and (5b). The upper and lower numbers in each of the cells (Δm, ΔP) present the number of known planets with the minimum mass and the orbital period in the corresponding interval and the probability of detecting planets with these parameters, respectively. The degree of shading a cell corresponds to the detection probability for this cell according to the scale on the right. The red dots show the positions of actually detected RV planets in the mP plane.
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Figure 2. The detectability windows: (a) W ˜ (for multiplanet systems) and (b) V (for stars with and without planets) in the format of maps in the mP plane obtained with the coefficients γ = 0.8 and δ = 2.0. The designations are the same as those in Figure 1.
Figure 2. The detectability windows: (a) W ˜ (for multiplanet systems) and (b) V (for stars with and without planets) in the format of maps in the mP plane obtained with the coefficients γ = 0.8 and δ = 2.0. The designations are the same as those in Figure 1.
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Figure 3. The distribution of RV planets over the ratio of the orbital period P to the total time of observations T.
Figure 3. The distribution of RV planets over the ratio of the orbital period P to the total time of observations T.
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Figure 4. The distribution of RV planets over the ratio of the semi-amplitude K of the radial velocity oscillations of a host star to the average deviation σ(O − C) from the best Keplerian curve.
Figure 4. The distribution of RV planets over the ratio of the semi-amplitude K of the radial velocity oscillations of a host star to the average deviation σ(O − C) from the best Keplerian curve.
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Figure 5. The corrected (de-biased) mass distributions of planets N(m). The coefficients are set at δ = 2.0 and γ = 0.65, 0.8, and 0.95 for the planets with m = (0.011–13) MJ and P = 1–100 days (panel (a)) and with m = (0.065–13) MJ and P = 1–104 days (panel (b)). The coefficients are set at δ = 1.5, 2.0, and 2.5 and γ = 0.8 for the planets with m = (0.011–13) MJ and P = 1–100 days (panel (c)) and with m = (0.065–13) MJ and P = 1–104 days (panel (d)). The error bars were estimated according to the Poisson distribution.
Figure 5. The corrected (de-biased) mass distributions of planets N(m). The coefficients are set at δ = 2.0 and γ = 0.65, 0.8, and 0.95 for the planets with m = (0.011–13) MJ and P = 1–100 days (panel (a)) and with m = (0.065–13) MJ and P = 1–104 days (panel (b)). The coefficients are set at δ = 1.5, 2.0, and 2.5 and γ = 0.8 for the planets with m = (0.011–13) MJ and P = 1–100 days (panel (c)) and with m = (0.065–13) MJ and P = 1–104 days (panel (d)). The error bars were estimated according to the Poisson distribution.
Atmosphere 14 00353 g005aAtmosphere 14 00353 g005b
Figure 6. (a) The composite de-biased (via V) distribution for the minimum masses of 598 RV planets with masses of 0.011–13 Jupiter masses that are part of systems with a noise level σ(O − C) < 15 m/s. For all sections of the distribution, δ = 2.0 was assumed. The blue solid line shows the distribution of planets with orbital periods of 1–100 days (γ = 0.75), the blue dashed line shows the same distribution multiplied by 3.75. The green and red solid lines show the corrected distribution of planets with periods of 1–3981 days with γ = 1.6 and 2.0, respectively. The dotted magenta line shows the biased distribution of RV planets with periods of 1–104 days (from the NASA Exoplanet Archive [1]). The black dashed line shows the distribution of exoplanets by mass predicted by population synthesis theory [2], the orange dotted line shows the distribution of planets with masses 5–50 Earth masses according to the new version of population synthesis theory [3]. (b) The similar dependencies for FGK host stars (with the star masses 1.00 ± 0.25 solar mass).
Figure 6. (a) The composite de-biased (via V) distribution for the minimum masses of 598 RV planets with masses of 0.011–13 Jupiter masses that are part of systems with a noise level σ(O − C) < 15 m/s. For all sections of the distribution, δ = 2.0 was assumed. The blue solid line shows the distribution of planets with orbital periods of 1–100 days (γ = 0.75), the blue dashed line shows the same distribution multiplied by 3.75. The green and red solid lines show the corrected distribution of planets with periods of 1–3981 days with γ = 1.6 and 2.0, respectively. The dotted magenta line shows the biased distribution of RV planets with periods of 1–104 days (from the NASA Exoplanet Archive [1]). The black dashed line shows the distribution of exoplanets by mass predicted by population synthesis theory [2], the orange dotted line shows the distribution of planets with masses 5–50 Earth masses according to the new version of population synthesis theory [3]. (b) The similar dependencies for FGK host stars (with the star masses 1.00 ± 0.25 solar mass).
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Figure 7. (a) The de-biased mass distributions of planets with masses m = (0.036–0.38) MJ and orbital periods P = 1–100 days for γ = 0.75 and 1.6. The planets with σ(O − C) < 8 m/s are considered. (b) The de-biased mass distributions of the planets having the orbital periods of 1–100 days (blue line), 10–1000 days (red line), and 1–10 days (black line). The planets of low-noise systems (with σ(O − C) < 4 m/s) are considered. By guess, the minimum is caused by planets in tight orbits.
Figure 7. (a) The de-biased mass distributions of planets with masses m = (0.036–0.38) MJ and orbital periods P = 1–100 days for γ = 0.75 and 1.6. The planets with σ(O − C) < 8 m/s are considered. (b) The de-biased mass distributions of the planets having the orbital periods of 1–100 days (blue line), 10–1000 days (red line), and 1–10 days (black line). The planets of low-noise systems (with σ(O − C) < 4 m/s) are considered. By guess, the minimum is caused by planets in tight orbits.
Atmosphere 14 00353 g007aAtmosphere 14 00353 g007b
Figure 8. The orbital-period distribution of RV planets. (a): NA(P) (14a)—de-biased distribution of planets with masses m = (0.02–13) MJ and orbital periods P = 1–100 days for γ = 0.75; the dotted blue line shows the distribution of planets with σ(O−C) < 15 m/s. The orbital-period distribution of the transit Kepler planets with radii of (1–16) RE and orbital periods of 6.25–100 days is shown by black dash-dot line [17]. (b): De-biased NB(P) (14b)—distributions of planets with masses m = (0.12–13) MJ for γ = 1.6: blue line—σ(O − C) < 50 m/s, the orange line—σ(O − C) < 50 m/s, T > 2320 days. The dashed lines in black and brown show approximations by power lows with exponents 0.70 and 0.77, respectively (dN/dlogP  P0.70 ± 0.03 and dN/dlogP   P0.77 ± 0.07). Initial from NASA Exoplanet Archive [1] (biased) distribution is shown by dotted magenta line. (c): The de-biased distributions of planets with m = (1.2–13) MJ, P = 1–104 days for γ = 2 and δ = 1.5, 2.0, and 2.5. Red line—T > 5000 days, green lines—T > 2320 days and σ(O − C) < 10 m/s, blue lines—T > 1077 days and σ(O−C) < 15 m/s. Solid lines show the distributions corrected by δ = 2.0, dash lines—by δ = 1.5, dotted lines—by δ = 2.5.
Figure 8. The orbital-period distribution of RV planets. (a): NA(P) (14a)—de-biased distribution of planets with masses m = (0.02–13) MJ and orbital periods P = 1–100 days for γ = 0.75; the dotted blue line shows the distribution of planets with σ(O−C) < 15 m/s. The orbital-period distribution of the transit Kepler planets with radii of (1–16) RE and orbital periods of 6.25–100 days is shown by black dash-dot line [17]. (b): De-biased NB(P) (14b)—distributions of planets with masses m = (0.12–13) MJ for γ = 1.6: blue line—σ(O − C) < 50 m/s, the orange line—σ(O − C) < 50 m/s, T > 2320 days. The dashed lines in black and brown show approximations by power lows with exponents 0.70 and 0.77, respectively (dN/dlogP  P0.70 ± 0.03 and dN/dlogP   P0.77 ± 0.07). Initial from NASA Exoplanet Archive [1] (biased) distribution is shown by dotted magenta line. (c): The de-biased distributions of planets with m = (1.2–13) MJ, P = 1–104 days for γ = 2 and δ = 1.5, 2.0, and 2.5. Red line—T > 5000 days, green lines—T > 2320 days and σ(O − C) < 10 m/s, blue lines—T > 1077 days and σ(O−C) < 15 m/s. Solid lines show the distributions corrected by δ = 2.0, dash lines—by δ = 1.5, dotted lines—by δ = 2.5.
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Figure 9. The de-biased orbital-period distributions for the planets from mass domains of (0.02–0.12) MJ, (0.12–1.2) MJ, and (1.2–13) MJ are shown by blue, green, and red lines, respectively. Planets with a total observation time T > 1077 days in systems with a noise level σ(O − C) < 15 m/s are represented. (a) All types of host stars were considered, (b) FGK host stars were considered.
Figure 9. The de-biased orbital-period distributions for the planets from mass domains of (0.02–0.12) MJ, (0.12–1.2) MJ, and (1.2–13) MJ are shown by blue, green, and red lines, respectively. Planets with a total observation time T > 1077 days in systems with a noise level σ(O − C) < 15 m/s are represented. (a) All types of host stars were considered, (b) FGK host stars were considered.
Atmosphere 14 00353 g009aAtmosphere 14 00353 g009b
Table 1. Optimal parameters and power law approximation for three mass intervals.
Table 1. Optimal parameters and power law approximation for three mass intervals.
Parameters, Approximation CoefficientsLow-Mass PlanetsIntermediate MassesMassive Planets
m, mass domain, MJ0.011–0.120.12–1.21.2–13
δ, in Equation (5a)222
γ, in Equation (5b)0.751.62
Planets numbers122185355
α, in approx. by N(m)   m α
de-biased by W *
−2−0.7…−0.8−1.7…−2 **
α, in approx. by N(m)   m α
de-biased by V ***
−3−0.8…−1.0−2
* accounting for the stars with planets. **—1.7 for all systems and −2 for systems with σ(O − C) < 15 m/s. *** accounting for the stars with planets (with planets multiplicity) and the stars without detected planets.
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Ananyeva, V.; Ivanova, A.; Shashkova, I.; Yakovlev, O.; Tavrov, A.; Korablev, O.; Bertaux, J.-L. Exoplanets Catalogue Analysis: The Distribution of Exoplanets at FGK Stars by Mass and Orbital Period Accounting for the Observational Selection in the Radial Velocity Method. Atmosphere 2023, 14, 353. https://doi.org/10.3390/atmos14020353

AMA Style

Ananyeva V, Ivanova A, Shashkova I, Yakovlev O, Tavrov A, Korablev O, Bertaux J-L. Exoplanets Catalogue Analysis: The Distribution of Exoplanets at FGK Stars by Mass and Orbital Period Accounting for the Observational Selection in the Radial Velocity Method. Atmosphere. 2023; 14(2):353. https://doi.org/10.3390/atmos14020353

Chicago/Turabian Style

Ananyeva, Vladislava, Anastasiia Ivanova, Inna Shashkova, Oleg Yakovlev, Alexander Tavrov, Oleg Korablev, and Jean-Loup Bertaux. 2023. "Exoplanets Catalogue Analysis: The Distribution of Exoplanets at FGK Stars by Mass and Orbital Period Accounting for the Observational Selection in the Radial Velocity Method" Atmosphere 14, no. 2: 353. https://doi.org/10.3390/atmos14020353

APA Style

Ananyeva, V., Ivanova, A., Shashkova, I., Yakovlev, O., Tavrov, A., Korablev, O., & Bertaux, J. -L. (2023). Exoplanets Catalogue Analysis: The Distribution of Exoplanets at FGK Stars by Mass and Orbital Period Accounting for the Observational Selection in the Radial Velocity Method. Atmosphere, 14(2), 353. https://doi.org/10.3390/atmos14020353

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