Acknowledgments
We would like to thank Michael Balogh, Ofek Birnholtz, Avery Broderick, Vitor Cardoso, Ramit Dey, Hannah Dykaar, Will East, Steve Giddings, Bob Holdom, Badri Krishnan, Lam Hui, Luis Lehner, Luis Longo, Samir Mathur, Emil Mottola, Shinji Mukohyama, Rob Myers, Ramesh Narayan, Alex Nielsen, Paolo Pani, Joe Polchinski (RIP), Chanda Prescod-Weinstein, Jing Ren, Krishan Saraswat, Rafael Sorkin, Daichi Tsuna, Yasaman Yazdi, Huan Yang, Aaron Zimmerman, and many others for discussions and/or collaborations about Quantum Black Holes over the past decade. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center (
https://www.gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. LIGO is funded by the U.S. National Science Foundation. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Instituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes.
Figure 1.
The Penrose diagram describing an evaporating BH.
Figure 1.
The Penrose diagram describing an evaporating BH.
Figure 2.
Near-horizon matter field of fuzzball solution from paper [
95]. Shown in the figure,
. Asymptotically
.
Figure 2.
Near-horizon matter field of fuzzball solution from paper [
95]. Shown in the figure,
. Asymptotically
.
Figure 3.
Near-horizon relationship between density and pressure of the fuzzball solution from paper [
95]. The dashed line is the asymptotic behavior, and the solid line is the real behavior of
.
Figure 3.
Near-horizon relationship between density and pressure of the fuzzball solution from paper [
95]. The dashed line is the asymptotic behavior, and the solid line is the real behavior of
.
Figure 4.
The geometry of Schwarzschild mock fuzzball. Red curve shows that the metric is modified around the horizon, and the shaded area is removed from the spacetime.
Figure 4.
The geometry of Schwarzschild mock fuzzball. Red curve shows that the metric is modified around the horizon, and the shaded area is removed from the spacetime.
Figure 5.
Anisotropic behavior of pressure of Schwarzschild mock fuzzball versus proper distance from horizon. The absolute value of two pressures are equal at . Radial pressure (dashed line) is much larger than tangential (solid line) pressure near horizon (proper length = 0), while the trend reverses far away. The figure shows that when approaching horizon, both pressures reach their maxima, hence have no singularity at r = 2M.
Figure 5.
Anisotropic behavior of pressure of Schwarzschild mock fuzzball versus proper distance from horizon. The absolute value of two pressures are equal at . Radial pressure (dashed line) is much larger than tangential (solid line) pressure near horizon (proper length = 0), while the trend reverses far away. The figure shows that when approaching horizon, both pressures reach their maxima, hence have no singularity at r = 2M.
Figure 6.
The energy density of mock fuzzball, compared with that of Hawking radiation as seen by a radially infalling observer along a geodesic. Here the orange curve is positive and the black curve is negative. The energy density of mock fuzzball is larger than the Hawking radiation near horizon and drops faster than the Hawking radiation away from the horizon.
Figure 6.
The energy density of mock fuzzball, compared with that of Hawking radiation as seen by a radially infalling observer along a geodesic. Here the orange curve is positive and the black curve is negative. The energy density of mock fuzzball is larger than the Hawking radiation near horizon and drops faster than the Hawking radiation away from the horizon.
Figure 7.
The energy flux seen by an observer falling through fuzzball geometry, compared to that of Hawking radiation. Here the orange curve is positive, while the black curves are negative. The flux of mock fuzzball is larger than the Hawking radiation near horizon and drops faster than the Hawking radiation away from the horizon.
Figure 7.
The energy flux seen by an observer falling through fuzzball geometry, compared to that of Hawking radiation. Here the orange curve is positive, while the black curves are negative. The flux of mock fuzzball is larger than the Hawking radiation near horizon and drops faster than the Hawking radiation away from the horizon.
Figure 8.
The CD potentials with and for and . The potential () is singular for in this case.
Figure 8.
The CD potentials with and for and . The potential () is singular for in this case.
Figure 9.
GW echoes following a BBH merger from a cavity of membrane/firewall-angular momentum barrier [
18].
Figure 9.
GW echoes following a BBH merger from a cavity of membrane/firewall-angular momentum barrier [
18].
Figure 10.
The amplification factor for and . In the right panel, low frequency region (solid) is calculated with the potential and the higher frequency region (dashed) is calculated from in the CD equation.
Figure 10.
The amplification factor for and . In the right panel, low frequency region (solid) is calculated with the potential and the higher frequency region (dashed) is calculated from in the CD equation.
Figure 11.
Spectra of echo + ringdown with , , and , , Mpc, and . The left panel shows the spectrum in the constant reflectivity model with and the right panel shows the spectrum in the Boltzmann reflectivity model with .
Figure 11.
Spectra of echo + ringdown with , , and , , Mpc, and . The left panel shows the spectrum in the constant reflectivity model with and the right panel shows the spectrum in the Boltzmann reflectivity model with .
Figure 12.
QNMs with (red filled circles) and (blue filled circles), , , and .
Figure 12.
QNMs with (red filled circles) and (blue filled circles), , , and .
Figure 13.
Mode function obtained by solving (
128) with the boundary condition of
for
.
Figure 13.
Mode function obtained by solving (
128) with the boundary condition of
for
.
Figure 14.
The energy reflection rate in the Boltzmann reflectivity model.
Figure 14.
The energy reflection rate in the Boltzmann reflectivity model.
Figure 15.
The time domain function with , , Mpc, , , and in the Boltzmann reflectivity model with and .
Figure 15.
The time domain function with , , Mpc, , , and in the Boltzmann reflectivity model with and .
Figure 16.
GW echoes following a collapse of binary neutron star merger event from a cavity of membrane-angular momentum barrier [
20].
Figure 16.
GW echoes following a collapse of binary neutron star merger event from a cavity of membrane-angular momentum barrier [
20].
Figure 17.
Maximized SNR
2 around the expected time of merger echoes Equation (
144), for the combined (top) and GW150914 (bottom) events. The significance and
p-values of the peaks within the gray rectangle are specified in this plot [
18].
Figure 17.
Maximized SNR
2 around the expected time of merger echoes Equation (
144), for the combined (top) and GW150914 (bottom) events. The significance and
p-values of the peaks within the gray rectangle are specified in this plot [
18].
Figure 18.
Same as
Figure 17, but over an extended range of
. The SNR peaks at the predicted value of
within gray rectangle have false detection probability of 0.11 (0.011) and significance of 1.6
(2.5
), for GW150914 (combined events) [
18] (See also [
27]).
Figure 18.
Same as
Figure 17, but over an extended range of
. The SNR peaks at the predicted value of
within gray rectangle have false detection probability of 0.11 (0.011) and significance of 1.6
(2.5
), for GW150914 (combined events) [
18] (See also [
27]).
Figure 19.
Average number of background peaks higher than a particular SNR-value within a time-interval
(gray rectangle in
Figure 17 and
Figure 18) for combined (left) and GW150914 (right) events [
18]. The red dots show the observed SNR peak at
(
Figure 17 and
Figure 18). The correspondence between SNR values and their significance is indicated in horizontal bar.
Figure 19.
Average number of background peaks higher than a particular SNR-value within a time-interval
(gray rectangle in
Figure 17 and
Figure 18) for combined (left) and GW150914 (right) events [
18]. The red dots show the observed SNR peak at
(
Figure 17 and
Figure 18). The correspondence between SNR values and their significance is indicated in horizontal bar.
Figure 20.
Original GW template of GW150914 [
18], along with best fit echoes template (
147).
Figure 20.
Original GW template of GW150914 [
18], along with best fit echoes template (
147).
Figure 21.
with respect to
. Solid, dashed, and dotted lines correspond to
for combined (Hanford and Livingston), Hanford, and Livingston, respectively, for the best fit parameters of the event GW150914 [
19].
Figure 21.
with respect to
. Solid, dashed, and dotted lines correspond to
for combined (Hanford and Livingston), Hanford, and Livingston, respectively, for the best fit parameters of the event GW150914 [
19].
Figure 22.
Time-frequency representations of
(Equation (
154); combining all harmonics with frequencies
, with
) around the merger for the BNS gravitational-wave event GW170817, observed through cross-correlating the two LIGO detectors [
20]. The possible resonance peaks of echoes found in this plot are marked with a green squares. The color scale shows the peak at
Hz and
1.0 s, is the highest peak in this diagram, from before and after the BNS merger (see
Figure 23,
Figure 24 and
Figure 25). A secondary peak at the same frequency but
32.9 s is also highlighted in this plot.
Figure 22.
Time-frequency representations of
(Equation (
154); combining all harmonics with frequencies
, with
) around the merger for the BNS gravitational-wave event GW170817, observed through cross-correlating the two LIGO detectors [
20]. The possible resonance peaks of echoes found in this plot are marked with a green squares. The color scale shows the peak at
Hz and
1.0 s, is the highest peak in this diagram, from before and after the BNS merger (see
Figure 23,
Figure 24 and
Figure 25). A secondary peak at the same frequency but
32.9 s is also highlighted in this plot.
Figure 23.
A 3d representation of
Figure 22 within echo search frequency window
Hz [
20]. This plot shows the tentative detection of echoes at
Hz and
1.0 s clearly stands above noise.
Figure 23.
A 3d representation of
Figure 22 within echo search frequency window
Hz [
20]. This plot shows the tentative detection of echoes at
Hz and
1.0 s clearly stands above noise.
Figure 24.
Amplitude-frequency plot of
(Equation (
154); combining all harmonics with frequencies
, with
) for the first peak (red) at 1.0 sec after the merger for the BNS merger gravitational-wave event GW170817, observed by x-correlating the LIGO detectors [
20]. The same amplitude-frequency plot for a random time in data (yellow), is also shown for comparison. Solid area between 63 Hz and 92 Hz was the search frequency prior range for Planckian echoes.
Figure 24.
Amplitude-frequency plot of
(Equation (
154); combining all harmonics with frequencies
, with
) for the first peak (red) at 1.0 sec after the merger for the BNS merger gravitational-wave event GW170817, observed by x-correlating the LIGO detectors [
20]. The same amplitude-frequency plot for a random time in data (yellow), is also shown for comparison. Solid area between 63 Hz and 92 Hz was the search frequency prior range for Planckian echoes.
Figure 25.
Amplitude-time plot of first echo peak at 1.0 sec after the merger at frequency of 72 Hz [
20]. After this detection Gill et al. [
149] with independent Astrophysical considerations also determined that the remnant of GW170817 must have collapsed to a BH after
Section Error-bar (in blue) is the time of collapse considering this independent observation in [
149] compared to the detected signal of echoes which is also as a consequence of BH collapse. The shaded region is 0–1 s prior range after the merger.
Figure 25.
Amplitude-time plot of first echo peak at 1.0 sec after the merger at frequency of 72 Hz [
20]. After this detection Gill et al. [
149] with independent Astrophysical considerations also determined that the remnant of GW170817 must have collapsed to a BH after
Section Error-bar (in blue) is the time of collapse considering this independent observation in [
149] compared to the detected signal of echoes which is also as a consequence of BH collapse. The shaded region is 0–1 s prior range after the merger.
Figure 26.
Average number of background peaks higher than a particular -X(t,f) within a frequency-intervals of 63–92 Hz and time-intervals of 1 Section the observed
peak at 1.0 s after the merger is marked by red square. The horizontal bar shows the relation between
values and their significance [
20].
Figure 26.
Average number of background peaks higher than a particular -X(t,f) within a frequency-intervals of 63–92 Hz and time-intervals of 1 Section the observed
peak at 1.0 s after the merger is marked by red square. The horizontal bar shows the relation between
values and their significance [
20].
Figure 27.
Correlation vs. echo time delay
vs.
for GW170104 using method II [
21]. Here
is the number of frequency steps between spikes.
Figure 27.
Correlation vs. echo time delay
vs.
for GW170104 using method II [
21]. Here
is the number of frequency steps between spikes.
Figure 28.
Determination of
for the events in O1 and O2 [
22].
Figure 28.
Determination of
for the events in O1 and O2 [
22].
Figure 29.
This plot shows whether it is possible to recover the potential signals with a variety of amplitudes in [
23]. Here it can be seen that amplitudes less than A=0.1 in ADA [
18] are difficult to be identified in data, while amplitude twice this value would be clearly identifiable.
Figure 29.
This plot shows whether it is possible to recover the potential signals with a variety of amplitudes in [
23]. Here it can be seen that amplitudes less than A=0.1 in ADA [
18] are difficult to be identified in data, while amplitude twice this value would be clearly identifiable.
Figure 30.
This plot shows injected and recovered values for
. The diagonal line is accurate recovery [
23]. The preference for
(dashed line) at lower injection amplitudes can be clearly seen.
Figure 30.
This plot shows injected and recovered values for
. The diagonal line is accurate recovery [
23]. The preference for
(dashed line) at lower injection amplitudes can be clearly seen.
Figure 31.
In this plot echoes signals are injected into Gaussian noise and are analysed for different overall amplitudes A [
23]. Injection amplitudes above
of the peak strain, can be accurately recovered. For the injections with lower amplitudes no matter what value they take, recovered amplitudes are likely to be around
. Horizontal lines show the amplitudes reported in [
18]. The injections are made with
.
Figure 31.
In this plot echoes signals are injected into Gaussian noise and are analysed for different overall amplitudes A [
23]. Injection amplitudes above
of the peak strain, can be accurately recovered. For the injections with lower amplitudes no matter what value they take, recovered amplitudes are likely to be around
. Horizontal lines show the amplitudes reported in [
18]. The injections are made with
.
Figure 32.
Reconstruction of cWB for the event GW151012 via color-coded time-frequency maps [
25]. The upper plot shows the squared coherent network SNR and the plot bellow shows the normalized residual noise energy. The residual plot is given after the reconstructed signal was subtracted from the data. In this plots a secondary cluster occurring 200 ms after the merger (consistent with echo times predicted and seen by ADA, Equation (
144)). The dashed vertical lines denote coalescence time for GW151012 (the network has used the Livingston detector time as a reference).
Figure 32.
Reconstruction of cWB for the event GW151012 via color-coded time-frequency maps [
25]. The upper plot shows the squared coherent network SNR and the plot bellow shows the normalized residual noise energy. The residual plot is given after the reconstructed signal was subtracted from the data. In this plots a secondary cluster occurring 200 ms after the merger (consistent with echo times predicted and seen by ADA, Equation (
144)). The dashed vertical lines denote coalescence time for GW151012 (the network has used the Livingston detector time as a reference).
Figure 33.
Reconstruction of cWB for the event GW151226, as color-coded time-frequency maps [
25]. The upper plot shows the squared coherent network SNR and the plot bellow shows the normalized residual noise energy. The residual plot is given after the reconstructed signal was subtracted from the data. In this plots a secondary cluster occurring 100 ms after the merger (consistent with echo times predicted and seen by ADA, Equation (
144)). The dashed vertical lines denote coalescence time for GW151226 (the network has used the Livingston detector time as a reference).
Figure 33.
Reconstruction of cWB for the event GW151226, as color-coded time-frequency maps [
25]. The upper plot shows the squared coherent network SNR and the plot bellow shows the normalized residual noise energy. The residual plot is given after the reconstructed signal was subtracted from the data. In this plots a secondary cluster occurring 100 ms after the merger (consistent with echo times predicted and seen by ADA, Equation (
144)). The dashed vertical lines denote coalescence time for GW151226 (the network has used the Livingston detector time as a reference).
Figure 34.
Maximum posteriori probability for time delay between Hanford (H) and Livingston (L) in line-of-sight frame for the main event GW151012 (blue contour) and the secondary signal (green contour) [
25].
Figure 34.
Maximum posteriori probability for time delay between Hanford (H) and Livingston (L) in line-of-sight frame for the main event GW151012 (blue contour) and the secondary signal (green contour) [
25].
Figure 35.
This plot shows how residual signal because of wavelength degeneracy can cause 7.7 m·s
shift in maximum poster probability for time delay. The upper plot is residual plot in
Figure 32 and the plot below is the poster probability in
Figure 34 for the event GW151012 [
25].
Figure 35.
This plot shows how residual signal because of wavelength degeneracy can cause 7.7 m·s
shift in maximum poster probability for time delay. The upper plot is residual plot in
Figure 32 and the plot below is the poster probability in
Figure 34 for the event GW151012 [
25].
Figure 36.
Plot of mass ratio dependence of
p-values in [
25]. Vertical lines are error bars for 50% credible region and central points are best value of mass ratio obtained from posteriors distribution. Because of relation of
p-value to error function erf(SNR) we took roughly SNR
as horizontal axis [
144].
Figure 36.
Plot of mass ratio dependence of
p-values in [
25]. Vertical lines are error bars for 50% credible region and central points are best value of mass ratio obtained from posteriors distribution. Because of relation of
p-value to error function erf(SNR) we took roughly SNR
as horizontal axis [
144].
Figure 37.
Histogram of slopes for uniform random choices of
. We see that only 1.3% of these random realizations, the slope can exceed the observed value [
144].
Figure 37.
Histogram of slopes for uniform random choices of
. We see that only 1.3% of these random realizations, the slope can exceed the observed value [
144].
Figure 38.
p-value distribution for combined events of different stretches of data within 1 min of the main events. Surprisingly, the blue line which is closest to the main event, and has used to define
p-value in [
18] (
Figure 19), happens to give the smallest
p-value. The shaded region represents the Poisson error range for blue histogram. This shows that the variation in
p-values is clearly much larger. This behaviour is interpreted as non-gaussianity and/or non-stationarity of the LIGO noise. In this plot the y-axis on the left (right) shows
p-value (number of higher peaks) within the mentioned range of data. In each histogram the total number of “peaks” is (38 − 9)/0.02 = 1450.
Figure 38.
p-value distribution for combined events of different stretches of data within 1 min of the main events. Surprisingly, the blue line which is closest to the main event, and has used to define
p-value in [
18] (
Figure 19), happens to give the smallest
p-value. The shaded region represents the Poisson error range for blue histogram. This shows that the variation in
p-values is clearly much larger. This behaviour is interpreted as non-gaussianity and/or non-stationarity of the LIGO noise. In this plot the y-axis on the left (right) shows
p-value (number of higher peaks) within the mentioned range of data. In each histogram the total number of “peaks” is (38 − 9)/0.02 = 1450.
Figure 39.
Spectra of ringdown and echo phases with the reflectivity of , , , and . We set Mpc, , , , , and .
Figure 39.
Spectra of ringdown and echo phases with the reflectivity of , , , and . We set Mpc, , , , , and .
Figure 40.
Spectra of ringdown and echo phases in the Boltzmann reflectivity model with , , , , and Mpc. Here we also assume , (left) and (right).
Figure 40.
Spectra of ringdown and echo phases in the Boltzmann reflectivity model with , , , , and Mpc. Here we also assume , (left) and (right).
Figure 41.
Spectra of GW echoes in the Boltzmann reflectivity model with , , Gpc, and . The gray line shows the case of , , and the black line shows one for , , . We also plot the PSD for the IPTA (blue) and SKA (red).
Figure 41.
Spectra of GW echoes in the Boltzmann reflectivity model with , , Gpc, and . The gray line shows the case of , , and the black line shows one for , , . We also plot the PSD for the IPTA (blue) and SKA (red).
Table 1.
Asymptotical behavior of metric.
Table 1.
Asymptotical behavior of metric.
Metric | Fuzzball | Kerr BH | Sch. BH |
---|
| | | |
| | | |
| | | |
| | | |
| | | 0 |
Table 2.
Parameters Setting.
Table 2.
Parameters Setting.
| p | m | | | | |
---|
1 | 4 | 2 | 2 | 1 | 1 | |
Table 3.
Table of positive results (
p-value
) by different groups (
The
p-value for Nielsen et al. [
24] is a rough estimate, based on the log-Bayes
).
Table 3.
Table of positive results (
p-value
) by different groups (
The
p-value for Nielsen et al. [
24] is a rough estimate, based on the log-Bayes
).
| Authors | Method | Data | p-Value |
---|
1 | Abedi, Dykaar, Afshordi (ADA) 2017 [18] | ADA template | O1 | 1.1% |
2 | Conklin, Holdom, Ren 2018 [21] | spectral comb | O1+O2 | 0.2–0.8% |
3 | Westerweck, et al. 2018 [23] | ADA template | O1 | 2.0% |
4 | Nielsen, et al. 2019 [24] | ADA + Bayes | GW151012, GW151226 | 2% |
5 | Uchikata, et al. 2019 [19] | ADA template | O1 | 5.5% |
6 | Uchikata, et al. 2019 [19] | ADA template | O2 | 3.9% |
7 | Salemi, et al. 2019 [25] | coherent WaveBurst | GW151012, GW151226 | 0.4%, 3% |
8 | Abedi, Afshordi 2019 [20] | spectral comb | BNS | 0.0016% |
9 | Gill, Nathanail, Rezolla 2019 [149] | Astro Modelling | BNS EM | |
Table 4.
Table of failed searches and their possible caveat. “Infinite” prior refers to models that include unphysical non-decaying echoes. As discussed in the text, inclusion of such templates skews any statistical inference.
Table 4.
Table of failed searches and their possible caveat. “Infinite” prior refers to models that include unphysical non-decaying echoes. As discussed in the text, inclusion of such templates skews any statistical inference.
| Authors | Method | Data | Possible Caveat |
---|
1 | Westerweck, et al. 2018 [23] | ADA template | O1 | “Infinite” prior |
2 | Nielsen, et al. 2019 [24] | ADA+Bayes | GW150914 | mass-ratio dependence, |
| | | | inconsistent Max SNR |
3 | Uchikata, et al. 2019 [19] | ADA, hi-pass | O1,O2 | no low-frequencies |
4 | Salemi, et al. 2019 [25] | coherent WaveBurst | O1,O2 | mass-ratio dependence, |
| | | | only 1st echo |
5 | Lo, et al. 2019 [145] | ADA+Bayes | O1 | “Infinite” prior |
6 | Tsang, et al. 2019 [146] | BayesWave | O1+O2 | needs very loud echoes |
| | | | (9 free parameters) |
Table 5.
Values of best fit echo parameters of the model Equation (
147) of the highest SNR peak near the predicted
(gray rectangle in
Figure 17), and their significance [
18].
Table 5.
Values of best fit echo parameters of the model Equation (
147) of the highest SNR peak near the predicted
(gray rectangle in
Figure 17), and their significance [
18].
| Range | GW150914 | Combined |
---|
| (0.99, 1.01) | 1.0054 | 1.0054 |
| (0.1, 0.9) | 0.89 | 0.9 |
| (−0.1,0) | −0.084 | −0.1 |
Amplitude | | 0.0992 | 0.124 |
SNR | | 4.21 | 6.96 |
p-value | | | |
significance | | 1.6 | 2.5 |
Table 6.
Comparing the expected theoretical values of echo time delays
’s of each merger event (Equation (
144)), to their best combined fit within the 1
credible region, and the contribution of each event to the combined SNR for the echoes (Equation (
148)) [
18].
Table 6.
Comparing the expected theoretical values of echo time delays
’s of each merger event (Equation (
144)), to their best combined fit within the 1
credible region, and the contribution of each event to the combined SNR for the echoes (Equation (
148)) [
18].
| GW150914 | GW151012 | GW151226 |
---|
(sec) | 0.2925 | 0.1778 | 0.1013 |
| ± 0.00916 | ± 0.02789 | ± 0.01152 |
(sec) | 0.30068 | 0.19043 | 0.09758 |
| 0.091 | 0.34 | 0.33 |
SNR | 4.13 | 4.52 | 3.83 |
Table 7.
p-values along with Poisson errors for O1 events [
19].
Table 7.
p-values along with Poisson errors for O1 events [
19].
Event | Westerweck et al. [23] | Uchikata et al. [19] |
---|
GW150914 | | |
GW151012 | | |
GW151226 | | |
Total | | |
Table 8.
p-values for O2 events [
19].
Table 8.
p-values for O2 events [
19].
Event | Uchikata et al. [19] |
---|
GW170104 | 0.071 |
GW170608 | 0.079 |
GW170729 | 0.567 |
GW170814 | 0.024 |
GW170818 | 0.929 |
GW170823 | 0.055 |
Total | 0.039 |
Table 9.
The best-fit
,
p-value, bandpass and window parameters for the six signals [
21]. Here
is the number of frequency steps between spikes which has been chosen using injection and echoes model properties in [
21].
Table 9.
The best-fit
,
p-value, bandpass and window parameters for the six signals [
21]. Here
is the number of frequency steps between spikes which has been chosen using injection and echoes model properties in [
21].
Event (Method) | Best-Fit (s) | p-Value | Bandpass | Window Parameters for Average |
---|
GW151226 (I) | 0.0786 | <0.00139 | (34,62)10 | = (1–29), (5-29), (9-29)11 |
GW151226 (II) | 0.0791 | 0.0076 | (12,58) | = (260,270) |
GW170104 (II) | 0.201 | <0.0018 | (16,62) | = (100,125,150,175,200) |
GW170608 (II) | 0.0756 | <0.004 | (14,60) | = (140,200,260) |
GW170814 (II) | 0.231 | 0.04 | (12,58) | = (170,190)12 |
GW170814 (III) | 0.228 | 0.0077 | (30,80) | , 13 |
Table 10.
Echoes best fit time delays and corresponding
using method II for O1 and O2 events [
22].
Table 10.
Echoes best fit time delays and corresponding
using method II for O1 and O2 events [
22].
Event (Method) | Best-Fit (s) | | |
---|
GW150914 (II) | 0.251 | 200 | 806 |
GW151012 (II) | 0.145 | 160 | 826 |
GW151226 (II) | 0.0791 | 783 | 270 |
GW170104 (II) | 0.201 | 150 | 831 |
GW170608 (II) | 0.0756 | 200 | 862 |
GW170729 (II) | 0.489 | 180/170 | 1240 |
GW170809 (II) | 0.235 | 170 | 845 |
GW170814 (II) | 0.231 | 200 | 878 |
GW170817 (II) | 0.00719 | 250 | 663 |
GW170818 (II) | 0.275 | 140 | 933 |
Table 11.
Comparison of
p-values obtained in [
18] and using larger portion of data (4096 s of LOSC data) [
23]. This data is divided into segments of 16 or 32 s length. Here different combinations of the events are considered, denoted as (GW150914, GW151012, GW151226, GW170104)
. Having the original priors, the Poisson errors (as suggested in [
28]): for GW150914 our
p-values are
(
), and for (1,2,3) our
p-values are
(
). The Poisson errors for the full combination (1,2,3,4) with original priors, are
(
). The comparison of
p-values using widened priors are also shown in this table.
Table 11.
Comparison of
p-values obtained in [
18] and using larger portion of data (4096 s of LOSC data) [
23]. This data is divided into segments of 16 or 32 s length. Here different combinations of the events are considered, denoted as (GW150914, GW151012, GW151226, GW170104)
. Having the original priors, the Poisson errors (as suggested in [
28]): for GW150914 our
p-values are
(
), and for (1,2,3) our
p-values are
(
). The Poisson errors for the full combination (1,2,3,4) with original priors, are
(
). The comparison of
p-values using widened priors are also shown in this table.
Event | [18] | Original 16 s (32 s) | Widened Priors 16s (32s) |
---|
GW150914 | 0.11 | 0.199 (0.238) | 0.705 (0.365) |
GW151012 | - | 0.056 (0.063) | 0.124 |
GW151226 | - | 0.414 (0.476) | 0.837 |
GW170104 | - | 0.725 | 0.757 |
(1,2) | - | 0.004 | 0.36 |
(1,3) | - | 0.159 | 0.801 |
(1,2,3) | 0.011 | 0.020 (0.032) | 0.18 (0.144) |
(1,3,4) | - | 0.199 (0.072) | 0.9 (0.32) |
(1,2,3,4) | - | 0.044 (0.032) | 0.368 (0.112) |
Table 12.
Results of Bayes factor [
24] using ADA model. Gaussian noise hypothesis is preferred for negative values of Log Bayes factor. Echoes hypothesis is preferred for positive values of Log Bayes factor. Log Bayes values of < 1 are “not worth more than a bare mention” [
24]. However, we note that the Max SNR reported here by [
24] for GW150914 is inconsistent with the one reported by the same group and ADA previously [
18,
23] (see
Table 6).
Table 12.
Results of Bayes factor [
24] using ADA model. Gaussian noise hypothesis is preferred for negative values of Log Bayes factor. Echoes hypothesis is preferred for positive values of Log Bayes factor. Log Bayes values of < 1 are “not worth more than a bare mention” [
24]. However, we note that the Max SNR reported here by [
24] for GW150914 is inconsistent with the one reported by the same group and ADA previously [
18,
23] (see
Table 6).
Event | Log Bayes Factor | Max SNR |
---|
GW150914 | −1.8056 | 2.86 |
GW151012 | 1.2499 | 5.5741 |
GW151226 | 0.4186 | 4.07 |
Table 13.
p-values for post-coalescence deviations from GR obtained by cWB for the eleven GW events from GWTC-1 [
25]. Post-coalescence SNR, SNR
; and estimated probability such that SNR
produced by a noise fluctuation. Here
and
are refering to the probability of SNR
and SNR
, respectively.
Table 13.
p-values for post-coalescence deviations from GR obtained by cWB for the eleven GW events from GWTC-1 [
25]. Post-coalescence SNR, SNR
; and estimated probability such that SNR
produced by a noise fluctuation. Here
and
are refering to the probability of SNR
and SNR
, respectively.
Event | Source | SNR | SNR | p-Value |
---|
GW150914 | BBH | 25.2 | | |
GW151012 | BBH | 10.5 | | |
GW151226 | BBH | 11.9 | | |
GW170104 | BBH | 13.0 | | |
GW170608 | BBH | 14.1 | | |
GW170729 | BBH | 10.2 | | |
GW170809 | BBH | 11.9 | | |
GW170814 | BBH | 17.2 | | |
GW170817 | BNS | 29.3 | | |
GW170818 | BBH | 8.6 | | |
GW170823 | BBH | 10.8 | | |
Table 14.
Events,
p-values reported in [
25], and average mass ratio of event. Highlighted rows are the most significant post-coalescence signals reported in [
25].
Table 14.
Events,
p-values reported in [
25], and average mass ratio of event. Highlighted rows are the most significant post-coalescence signals reported in [
25].
Event | p-Value | Average Mass Ratio |
---|
GW150914 | | 0.86 |
GW151012 | | 0.58 |
GW151226 | | 0.56 |
GW170104 | | 0.65 |
GW170608 | | 0.68 |
GW170729 | | 0.68 |
GW170809 | | 0.68 |
GW170814 | | 0.82 |
GW170818 | | 0.75 |
GW170823 | | 0.74 |
Table 15.
Prior range proposed by Lo et al. [
145] of the echo parameters of ADA model [
18].
Table 15.
Prior range proposed by Lo et al. [
145] of the echo parameters of ADA model [
18].
Parameter | Prior Range |
---|
A | [0.0, 1.0] |
| [0.0, 1.0] |
(s) | [−0.1,0.01] |
(s) | [0.05, 0.5] |
(s) | [0.05, 0.5] |
Table 16.
Detection statistic
vs. its corresponding statistical significances shown for both Gaussian and O1 backgrounds [
145].
Table 16.
Detection statistic
vs. its corresponding statistical significances shown for both Gaussian and O1 backgrounds [
145].
Statistical Significance | Detection Statistic (Gaussian Noise) | Detection Statistic (O1 Noise) |
---|
1 | −0.9 | 0.1 |
2 | −0.4 | 1.5 |
3 | 1.1 | 4.0 |
4 | 1.5 | 5.4 |
5 | 1.9 | 5.7 |
Table 17.
The detection statistic and its corresponding statistical significance and
p-value for O1 events [
145]. The ordering of events by their statistical significance is consistent with what reported by Nielsen et al. [
24].
Table 17.
The detection statistic and its corresponding statistical significance and
p-value for O1 events [
145]. The ordering of events by their statistical significance is consistent with what reported by Nielsen et al. [
24].
Event | Detection Statistic | p-Value | Statistical Significance |
---|
GW150914 | −1.3 | 0.806 | |
GW151012 | 0.4 | 0.0873 | 1.4 |
GW151226 | −0.2 | 0.254 | |
Table 18.
Obtained
p-values for each event along with total
p-value [
19]. A hyphen means that 4096-s of data are not available. Data version.
Table 18.
Obtained
p-values for each event along with total
p-value [
19]. A hyphen means that 4096-s of data are not available. Data version.
Event | C01 | C02 |
---|
GW150914 | 0.992 | 0.984 |
GW151012 | 0.646 | 0.882 |
GW151226 | 0.276 | - |
GW170104 | 0.717 | 0.677 |
GW170608 | - | 0.488 |
GW170729 | - | 0.575 |
GW170814 | - | 0.472 |
GW170818 | - | 0.976 |
GW170823 | - | 0.315 |
Total | 0.976 | 0.921 |
Table 19.
p-value for each event and total
p-value [
19]. Result 1 is the case when the phase shift is fixed to
, and result 2 is the case when the total phase shift is also a parameter.
Table 19.
p-value for each event and total
p-value [
19]. Result 1 is the case when the phase shift is fixed to
, and result 2 is the case when the total phase shift is also a parameter.
Event | Result 1 | Result 2 |
---|
GW150914 | 0.638 | 0.992 |
GW151012 | 0.417 | 0.646 |
GW151226 | 0.953 | 0.276 |
GW170104 | 0.213 | 0.717 |
Total | 0.528 | 0.976 |
Table 20.
Log Bayes factors for signal versus noise and signal versus glitch, and the corresponding
p-values, for events seen in two detectors of GWTC-1 [
147]. The bottom row shows the combined
p-values for all these events together.
Table 20.
Log Bayes factors for signal versus noise and signal versus glitch, and the corresponding
p-values, for events seen in two detectors of GWTC-1 [
147]. The bottom row shows the combined
p-values for all these events together.
Event | Log | | Log | |
---|
GW150914 | 2.32 | 0.26 | 2.95 | 0.43 |
GW151012 | −0.59 | 0.70 | 0.35 | 0.88 |
GW151226 | −0.67 | 0.72 | 2.48 | 0.53 |
GW170104 | 1.09 | 0.44 | 3.80 | 0.28 |
GW170608 | −0.90 | 0.75 | 0.90 | 0.82 |
GW170823 | 6.11 | 0.03 | 5.29 | 0.11 |
Combined | - | 0.34 | - | 0.57 |
Table 21.
Same as
Table 20, while for the events that three detectors has been involved [
147]. In the case of GW170817, in order to cover 1.0 sec after the merger for echoes found by Abedi and Afshordi [
20], additional search as first echo being from this time has been set. For this particular event, latter prior choice has been taken for combined
p-values.
Table 21.
Same as
Table 20, while for the events that three detectors has been involved [
147]. In the case of GW170817, in order to cover 1.0 sec after the merger for echoes found by Abedi and Afshordi [
20], additional search as first echo being from this time has been set. For this particular event, latter prior choice has been taken for combined
p-values.
Event | Log | | Log | |
---|
GW170729 | 4.24 | 0.67 | 5.64 | 0.62 |
GW170809 | 9.05 | 0.31 | 12.69 | 0.09 |
GW170814 | 8.75 | 0.33 | 8.54 | 0.34 |
GW170817 | 11.05 | 0.19 | 10.30 | 0.20 |
GW170817 | 6.19 | 0.52 | 9.39 | 0.27 |
GW170818 | 10.39 | 0.23 | 9.36 | 0.27 |
Combined | - | 0.47 | - | 0.22 |