2020/11/30 by Nhat-Minh Nguyen, Fabian Schmidt, Guilhem Lavaux +1 · 1 citation
Physics and Astronomy · #Amplitude #Bayesian inference #Bayesian probability #Cosmology #Cosmology and Gravitation Theories #Dark energy #Dark matter #Field (mathematics) #Galaxies: Formation, Evolution, Phenomena #Gaussian #Inference #Statistical Mechanics and Entropy #astro-ph.CO
paper · pdf · doi:10.1088/1475-7516/2021/03/058
published as JCAP03(2021)058 · 29 pages, 11 figures, 1 table; main results in Sec 4.2; comments welcome. v2: Match JCAP accepted version (no major change), update references. v3: Fix links, typos and further update reference info. v4: Match JCAP published version, fix an incorrect link and line spacing
openalex created_date 2020/11/23 · openalex publication_date 2021/03/01 · arxiv created 2021/03/16 · arxiv updated 2021/03/18 · openalex updated_date 2026/08/05
Abstract We investigate the impact of each ingredient in the employed physical data model on the Bayesian forward inference of initial conditions from biased tracers at the field level. Specifically, we use dark matter halos in a given cosmological simulation volume as tracers of the underlying matter density field. We study the effect of tracer density, grid resolution, gravity model, bias model and likelihood on the inferred initial conditions. We find that the cross-correlation coefficient between true and inferred phases reacts weakly to all ingredients above, and is well predicted by the theoretical expectation derived from a Gaussian model on a broad range of scales. The bias in the amplitude of the inferred initial conditions, on the other hand, depends strongly on the bias model and the likelihood. We conclude that the bias model and likelihood hold the key to an unbiased cosmological inference. Together they must keep the systematics — which arise from the sub-grid physics that are marginalized over — under control in order to obtain an unbiased inference.