A multi-eigenbasis denoising technique using mock reference and classifier eigenbases is introduced and shown on held-out mocks to outperform smoothing for covariance estimation in Lyα forest analyses.
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Using idealized synthetic data, knowing the true continuum in Lyα forest auto- and cross-correlations reduces uncertainties on the AP parameter and Ω_m by ~10%, with extension to 240 h^{-1}Mpc scales adding up to ~15% further improvement equivalent to a 40% larger survey area.
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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A multi-eigenbasis approach to covariance matrix denoising for cosmological inference
A multi-eigenbasis denoising technique using mock reference and classifier eigenbases is introduced and shown on held-out mocks to outperform smoothing for covariance estimation in Lyα forest analyses.
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Probing the limits of cosmological information from the Lyman-$\alpha$ forest 2-point correlation functions
Using idealized synthetic data, knowing the true continuum in Lyα forest auto- and cross-correlations reduces uncertainties on the AP parameter and Ω_m by ~10%, with extension to 240 h^{-1}Mpc scales adding up to ~15% further improvement equivalent to a 40% larger survey area.
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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.