Presents a tensorized GPU implementation of the 2-to-2 elastic self-collision operator for dark-sector particles and applies it to a two-source freeze-in scenario where self-interactions erase bimodal features.
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In a two-scalar dark sector, non-equilibrium phase-space evolution during sequential freeze-in alters the dark matter relic abundance by up to an order of magnitude relative to the standard number-density treatment.
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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Exploring non-equilibrium effects in sequential freeze-in
In a two-scalar dark sector, non-equilibrium phase-space evolution during sequential freeze-in alters the dark matter relic abundance by up to an order of magnitude relative to the standard number-density treatment.