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.
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DESI measures BAO from the Lyα forest at z_eff=2.33, reporting H(z) = (239.2 ± 4.8) (147.09 Mpc/rd) km/s/Mpc and DM(z) = (5.84 ± 0.14) (rd/147.09 Mpc) Gpc.
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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Lyman-Alpha Forest and its Cross-Correlation with High-Redshift Galaxies in Effective Field Theory at the Field Level
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.
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DESI 2024 IV: Baryon Acoustic Oscillations from the Lyman Alpha Forest
DESI measures BAO from the Lyα forest at z_eff=2.33, reporting H(z) = (239.2 ± 4.8) (147.09 Mpc/rd) km/s/Mpc and DM(z) = (5.84 ± 0.14) (rd/147.09 Mpc) Gpc.
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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.