Non-supersymmetric spin-3/2 dark matter with baryon-violating portals can explain the relic abundance through UV and Boltzmann-suppressed freeze-in, with viable parameter space constrained by indirect detection, direct detection, and LHC monojet searches.
Hooper,TASI Lectures on Indirect Searches For Dark Matter,PoST ASI2018(2019) 010 [1812.02029]
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abstract
In these lectures, I describe a variety of efforts to identify or constrain the identity of dark matter by detecting the annihilation or decay products of these particles, or their effects. After reviewing the motivation for indirect searches, I discuss what we have learned about dark matter from observations of gamma rays, cosmic rays and neutrinos, as well as the cosmic microwave background. Measurements such as these have been used to significantly constrain a wide range of thermal relic dark matter candidates, in particular those with masses below a few hundred GeV. I also discuss a number of anomalies and excesses that have been interpreted as possible signals of dark matter, including the Galactic Center gamma-ray excess, the cosmic-ray antiproton excess, the cosmic-ray positron excess, and the 3.5 keV line. These lectures were originally presented as part of the 2018 Theoretical Advanced Study Institute (TASI) summer school on "Theory in an Era of Data". Although intended for advanced graduate students, these lectures may be useful for a wide range of physicists, astrophysicists and astronomers who wish to get an overview of the current state of indirect searches for dark matter.
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A 1D convolutional neural network reconstructs the dark-matter phase-space distribution from the matter power spectrum with greater accuracy and broader applicability than an earlier empirical formula.
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Decaying spin-3/2 dark matter from baryon number violation
Non-supersymmetric spin-3/2 dark matter with baryon-violating portals can explain the relic abundance through UV and Boltzmann-suppressed freeze-in, with viable parameter space constrained by indirect detection, direct detection, and LHC monojet searches.
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Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum
A 1D convolutional neural network reconstructs the dark-matter phase-space distribution from the matter power spectrum with greater accuracy and broader applicability than an earlier empirical formula.