Priors on red galaxy stochasticity from hybrid effective field theory
Nickolas Kokron, Joseph DeRose, Shi-Fan Chen, Martin White, Risa H., Wechsler

TL;DR
This paper uses hybrid effective field theory and halo occupation distribution modeling to analyze the stochastic properties of red galaxy samples, providing priors for cosmological analyses and connecting different galaxy-halo modeling frameworks.
Contribution
It introduces a novel application of hybrid EFT to characterize galaxy-halo connection models at the field level, revealing stochasticity behaviors and establishing priors for future cosmological studies.
Findings
Galaxy samples show stochasticity from sub-Poisson to super-Poisson.
Halo host haloes exhibit sub-Poisson stochasticity.
Methodology links hybrid EFT with galaxy-halo connection modeling.
Abstract
We investigate the stochastic properties of typical red galaxy samples in a controlled numerical environment. We use Halo Occupation Distribution (HOD) modelling to create mock realizations of three separate bright red galaxy samples consistent with datasets used for clustering and lensing analyses in modern galaxy surveys. Second-order Hybrid Effective Field Theory (HEFT) is used as a field-level forward model to describe the full statistical distribution of these tracer samples, and their stochastic power spectra are directly measured and compared to the Poisson shot-noise prediction. While all of the galaxy samples we consider are hosted within haloes with sub-Poisson stochasticity, we observe that the galaxy samples themselves possess stochasticities that range from sub-Poisson to super-Poisson, in agreement with predictions from the halo model. As an application of our methodology,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStatistical Methods and Bayesian Inference · Spatial and Panel Data Analysis · Galaxies: Formation, Evolution, Phenomena
