Mapping the Three-dimensional Lyα Forest Large-scale Structure in Real and Redshift Space*

Abstract

This work presents a new physically motivated supervised machine-learning method, hydro-bam , to reproduce the three-dimensional Ly α forest field in real and redshift space, which learns from a reference hydrodynamic simulation and thereby saves about seven orders of magnitude in computing time. We show that our method is accurate up to k ∼ 1 h Mpc −1 in the one- (probability distribution function), two- (power spectra), and three-point (bispectra) statistics of the reconstructed fields. When compared to the reference simulation including redshift-space distortions, our method achieves deviations of ≲2% up to k = 0.6 h Mpc −1 in the monopole and ≲5% up to k = 0.9 h Mpc −1 in the quadrupole. The bispectrum is well reproduced for triangle configurations with sides up to k = 0.8 h Mpc −1 . In contrast, the commonly adopted Fluctuating Gunn–Peterson approximation shows significant deviations, already when peculiar motions are not included (real space) at configurations with sides of k = 0.2–0.4 h Mpc −1 in the bispectrum and is also significantly less accurate in the power spectrum (within 5% up to k = 0.7 h Mpc −1 ). We conclude that an accurate analysis of the Ly α forest requires considering the complex baryonic thermodynamical large-scale structure relations. Our hierarchical domain-specific machine-learning method can efficiently exploit this and is ready to generate accurate Ly α forest mock catalogs covering the large volumes required by surveys such as DESI and WEAVE.