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