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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
astro-ph.CO 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
citing papers explorer
-
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.
-
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.