An Empirical Probabilistic Model for Field-Level Galaxy Distributions in Tomographic Density Slabs

          @misc{ scivideos_PIRSA:26060035,
            doi = {10.48660/26060035},
            url = {https://pirsa.org/26060035},
            author = {Sarma Boruah, Supranta},
            keywords = {Cosmology},
            language = {en},
            title = {An Empirical Probabilistic Model for Field-Level Galaxy Distributions in Tomographic Density Slabs},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2026},
            month = {jun},
            note = {PIRSA:26060035 see, \url{https://scivideos.org/pirsa/26060035}}
          }
          

Abstract

Upcoming photometric surveys such as LSST, Roman, and Euclid will map billions of galaxies, opening the door to field-level cosmological analyses. In this talk, I will present a density-slab framework for performing a field-level analog of the standard 3x2pt analysis, where we model galaxy and weak lensing observables by forward modeling density slabs of O[100 Mpc] at the map level rather than through two-point correlation functions. A central challenge in this program is modeling the field-level distribution of galaxies down to small scales (a few Mpcs), where the galaxy–matter connection is non-linear, non-local, non-Poissonian, and correlated across different tracer populations. I will describe our empirical probabilistic model of galaxy bias that captures all of these effects through a flexible parametric form calibrated against simulations. I will show that the model accurately reproduces the statistical properties of galaxy fields from both the UniverseMachine and IllustrisTNG simulations, demonstrating its robustness across different galaxy formation prescriptions and its viability as a forward model for field-level inference of photometric surveys. I will also briefly discuss a related model for the joint non-Poissonian distribution of multiple galaxy populations within halos in the Halo Occupation Distribution framework.