Symmetry, Dark Matter and the Characterisation of Its Properties
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Physics".
Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 13285
Special Issue Editors
Interests: Standard Model (QCD and EW interactions); supersymmetry; nonminimal Higgs models; higher order corrections; Monte Carlo event generators
Special Issue Information
Dear Colleagues,
One of the major issues in modern particle and astroparticle physics is the determination of the nature of dark matter. The existence of an unknown source of matter which affects the gravitational interactions of astrophysical objects at scales of galaxies or larger and which is also implied by the analysis of the spectrum of the cosmic microwave background is a problem that the Standard Model of particle physics does not address in its current state. Dark matter candidates can be detected and studied in different ways, ranging from cosmological observations, direct or indirect detection, or even by producing them at colliders, assuming such particles are within the reach of current or future machines. If experimental observations are made in any of these fields which could be interpreted as originating from a dark matter candidate, the determination of its properties, such as mass, spin and interactions, would not be straightforward, given its elusive nature. On the other hand, the possibility of characterising observations of signatures compatible with the existence of dark matter would point towards possible theoretical models which predict its existence and, possibly, exclude others. The possibility of characterising the properties of dark matter candidates is therefore one of the main goals for current and future searches. Pinpointing the nature of dark matter by combining the potentialities of colliders with results from direct and indirect detection experiments or cosmological observations is the focus of this issue. Attention is given to new and advanced statistical methods for data analysis and to the possibility to use machine learning techniques for the characterisation of new signals.
Prof. Dr. Stefano Moretti
Dr. Rikard Enberg
Guest Editors
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Keywords
- particle physics
- beyond the Standard Model
- dark matter properties
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