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.
Variations in pre-nucleosynthesis cosmology produce distinct seasons in the phase-space distribution of freeze-in dark matter, directly affecting its warmness and mass bounds.
citing papers explorer
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KineticXGPU: A Tensorized Collision Operator for Dark-Sector Self-Scattering
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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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.
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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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Seasons of Dark Matter Freeze-In Shaped by the Weather of the Early Universe
Variations in pre-nucleosynthesis cosmology produce distinct seasons in the phase-space distribution of freeze-in dark matter, directly affecting its warmness and mass bounds.