First constraints from Ly-alpha forest limit DAO participation to at most 30% of dark matter for peaks below 50 h/Mpc at 95% CL using a deep kernel emulator of simulations.
PRIYA: a new suite of Lyman-αforest simulations for cosmology,
7 Pith papers cite this work. Polarity classification is still indexing.
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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}.
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.
An EFT-based field-level forward model for the Lyman-alpha forest matches simulations at the percent level on quasi-linear scales and generates mocks for DESI and DESI-II analyses.
Composite dark matter from a keV-MeV confining phase transition generates curvature perturbations constrained by CMB anisotropies and Lyman-alpha forest data, offering a testable scenario even without visible sector couplings.
Varying AGN feedback parameters shows that jet feedback from the most massive black holes suppresses the Lyman-alpha forest P1D unless it reduces the number of such black holes.
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
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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.