DESI DR1 Lyman-alpha data yields Δ²★=0.379±0.032 and n★=-2.309±0.019 at k★=0.009 km⁻¹s and z=3, sharpening N_eff, α_s, and β_s constraints by factors of 1.18-1.90 when combined with other probes.
Garcia-Gallego, V
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Derives the power spectrum evolution and cross-spectra for arbitrary multi-species wave and particle dark matter, incorporating free-streaming, Jeans scales, and intrinsic fluctuations.
Presents a general analytic framework based on truncated BBGKY hierarchy solved via Volterra equations for computing power spectra in multi-species dark matter with finite velocity dispersion and Poisson fluctuations.
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
citing papers explorer
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Cosmological analysis of the DESI DR1 Lyman alpha 1D power spectrum
DESI DR1 Lyman-alpha data yields Δ²★=0.379±0.032 and n★=-2.309±0.019 at k★=0.009 km⁻¹s and z=3, sharpening N_eff, α_s, and β_s constraints by factors of 1.18-1.90 when combined with other probes.
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Growth of Structure in Multi-species Wave Dark Matter
Derives the power spectrum evolution and cross-spectra for arbitrary multi-species wave and particle dark matter, incorporating free-streaming, Jeans scales, and intrinsic fluctuations.
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Multi-species Dark Matter with Warmth and Randomness
Presents a general analytic framework based on truncated BBGKY hierarchy solved via Volterra equations for computing power spectra in multi-species dark matter with finite velocity dispersion and Poisson fluctuations.
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Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.