PIRSA:26060037

Modeling high-redshift tracers at the field level

APA

de Belsunce, R. (2026). Modeling high-redshift tracers at the field level. Perimeter Institute for Theoretical Physics. https://pirsa.org/26060037

MLA

de Belsunce, Roger. Modeling high-redshift tracers at the field level. Perimeter Institute for Theoretical Physics, Jun. 12, 2026, https://pirsa.org/26060037

BibTex

          @misc{ scivideos_PIRSA:26060037,
            doi = {10.48660/26060037},
            url = {https://pirsa.org/26060037},
            author = {de Belsunce, Roger},
            keywords = {Cosmology},
            language = {en},
            title = {Modeling high-redshift tracers at the field level},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2026},
            month = {jun},
            note = {PIRSA:26060037 see, \url{https://scivideos.org/pirsa/26060037}}
          }
          
Talk numberPIRSA:26060037
Talk Type Conference
Subject

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.