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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Cosmic variance is modeled as Eulerian bias parameters of a finite-volume realization relative to the ensemble average, disentangling it from galaxy bias in the nonlinear BAO shift.
Generation of 100 full-sky quasar spectrophotometric mock catalogs using a hierarchical nonlocal nonlinear bias scheme on Augmented Lagrangian Perturbation Theory lightcone fields, calibrated to DESI data and including Quaia observational effects.
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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Cosmic variance or galaxy bias? Disentangling finite-volume and galaxy formation effects in cosmological analysis
Cosmic variance is modeled as Eulerian bias parameters of a finite-volume realization relative to the ensemble average, disentangling it from galaxy bias in the nonlinear BAO shift.
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Fast and accurate Gaia-unWISE quasar mock catalogs from LPT and Eulerian bias
Generation of 100 full-sky quasar spectrophotometric mock catalogs using a hierarchical nonlocal nonlinear bias scheme on Augmented Lagrangian Perturbation Theory lightcone fields, calibrated to DESI data and including Quaia observational effects.
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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.