Analytic compression of EFT parameters for Lyα forest P1D via Fisher matrix and linearization allows efficient marginalization, saturating constraints with linear bias plus five effective terms and forecasting 10% and 2% precision on Δ²_p and n_p at k_p=0.7 Mpc^{-1}.
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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.
Composite dark matter from a keV–MeV confining phase transition sources an IR-enhanced curvature spectrum that competes with free-streaming suppression, yielding concrete CMB and Lyman-α bounds on transition strength and temperature.
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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Analytic compression of the effective field theory of the Lyman-alpha forest
Analytic compression of EFT parameters for Lyα forest P1D via Fisher matrix and linearization allows efficient marginalization, saturating constraints with linear bias plus five effective terms and forecasting 10% and 2% precision on Δ²_p and n_p at k_p=0.7 Mpc^{-1}.
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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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Probing Confining Dark Sectors with Cosmological Perturbations
Composite dark matter from a keV–MeV confining phase transition sources an IR-enhanced curvature spectrum that competes with free-streaming suppression, yielding concrete CMB and Lyman-α bounds on transition strength and temperature.
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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.