Modeling high-redshift tracers at the field level

Abstract

Current and future high-redshift spectroscopic surveys such as DESI, DESI-II, and Spec-S5 raise the question of how to fully extract the information contained in these datasets. Field-level inference is opening a new frontier in cosmology by enabling analyses that capture the full information content of cosmological observations, rather than relying on two- or three-point summary statistics. High-redshift surveys probe unprecedented cosmological volumes, access quasi-linear modes sensitive to fundamental physics, benefit from precise calibration against small-scale hydrodynamic simulations, and exhibit diminishing shot noise toward higher redshift. However, accurately modeling these observations — particularly at the field level — remains a major challenge. I will present recent progress in modeling the Lyman-alpha forest at the field level and compare the information content of joint power spectrum and compressed bispectrum analyses with field-level inference approaches, combining methods from effective field theory (EFT) and machine learning (ML). This framework is then extended to additional high-redshift tracers, including Lyman-break galaxies and Lyman-alpha emitters, whose cross-correlations unlock new opportunities for extracting cosmological information and controlling systematic uncertainties. Finally, I will present ongoing work that combines perturbative mock generation with generative ML techniques to bridge large and small scales, enabling simulations that span DESI-like cosmological volumes while retaining the small-scale structure captured by hydrodynamic simulations. Together, these developments provide a scalable path toward fully exploiting next-generation surveys such as DESI-II and Spec-S5, establishing high-redshift structure as a key arena for precision, field-level cosmology.