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 DR2 BAO data exhibits 2.3 sigma tension with CMB in Lambda-CDM but prefers evolving dark energy (w0 > -1, wa < 0) at 3.1 sigma with CMB and 2.8-4.2 sigma when including supernovae.
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.
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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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DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cosmological Constraints
DESI DR2 BAO data exhibits 2.3 sigma tension with CMB in Lambda-CDM but prefers evolving dark energy (w0 > -1, wa < 0) at 3.1 sigma with CMB and 2.8-4.2 sigma when including supernovae.
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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.