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.
König, A
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abstract
We investigate the early Universe production of sterile neutrino Dark Matter by the decays of singlet scalars. All previous studies applied simplifying assumptions and/or studied the process only on the level of number densities, which makes it impossible to give statements about cosmic structure formation. We overcome these issues by dropping all simplifying assumptions (except for one we showed earlier to work perfectly) and by computing the full course of Dark Matter production on the level of non-thermal momentum distribution functions. We are thus in the position to study all aspects of the resulting settings and apply all relevant bounds in a reliable manner. We have a particular focus on how to incorporate bounds from structure formation on the level of the linear power spectrum, since the simplistic estimate using the free-streaming horizon clearly fails for highly non-thermal distributions. Our work comprises the most detailed and comprehensive study of sterile neutrino Dark Matter production by scalar decays presented so far.
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Thermal corrections to reheating and freeze-in DM production rates are generally small in the computable regime but can be large in constructed counter-examples.
citing papers explorer
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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.
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Thermal effects on Dark Matter production during cosmic reheating
Thermal corrections to reheating and freeze-in DM production rates are generally small in the computable regime but can be large in constructed counter-examples.