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}.
Ivanov,Lyman alpha forest power spectrum in effective field theory, Phys
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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.
Lya2pcf is an efficient pipeline implementing standard algorithms for 2PCF and 3PCF of the Lyman-alpha forest, with GPU speedups over PICCA and the first large-sample anisotropic 3PCF measurement up to 80 Mpc/h.
DESI DR2 delivers 0.65% precision BAO measurements from the LyA forest at z_eff=2.33, with D_H/r_d = 8.632 ± 0.098 ± 0.026 and D_M/r_d = 38.99 ± 0.52 ± 0.12.
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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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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Lya2pcf: an efficient pipeline to estimate two- and three-point correlation functions of the Lyman-$\alpha$ forest
Lya2pcf is an efficient pipeline implementing standard algorithms for 2PCF and 3PCF of the Lyman-alpha forest, with GPU speedups over PICCA and the first large-sample anisotropic 3PCF measurement up to 80 Mpc/h.
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DESI DR2 Results I: Baryon Acoustic Oscillations from the Lyman Alpha Forest
DESI DR2 delivers 0.65% precision BAO measurements from the LyA forest at z_eff=2.33, with D_H/r_d = 8.632 ± 0.098 ± 0.026 and D_M/r_d = 38.99 ± 0.52 ± 0.12.
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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.