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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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.
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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.