BAO scale inference from biased tracers using the EFT likelihood. Issue 8 (1st August 2022)

• Record Type: Journal Article
• Authors: Babić, Ivana; Schmidt, Fabian; Tucci, Beatriz
• Is Part Of: Journal of cosmology and astroparticle physics. Volume 2022:Issue 8(2022)
• Publication Date: 2022-08-01
• Subjects: baryon acoustic oscillations -- cosmological parameters from LSS -- galaxy clustering -- Bayesian reasoning; Cosmology -- Periodicals; Astrophysics -- Periodicals; 523.0105
• DOI: 10.1088/1475-7516/2022/08/007
• Languages: English
• ISSNs: 1475-7516

Abstract

Abstract: The physical scale corresponding to baryon acoustic oscillations (BAO), the size of the sound horizon at recombination, is precisely determined by CMB experiments. Measuring the apparent size of the BAO scale imprinted in the clustering of galaxies gives us a direct estimate of the angular-diameter distance and the Hubble parameter as a function of redshift. The BAO feature is damped by non-linear structure formation, which reduces the precision with which we can infer the BAO scale from standard galaxy clustering analysis methods. Many methods to undo this damping via the so-called BAO reconstruction have so far been proposed; however, they all rely on backward modeling. In this paper, we present the first results of isotropic BAO inference from rest-frame halo catalogs using forward modeling combined with the EFT likelihood, in the case where the initial phases of the density field are fixed. We show that the remaining systematic bias is less than 2% when we consider cutoff values of Λ ≤ 0.25 h Mpc -1 for all halo samples considered, and below 1% and consistent with zero for all but the most highly biased samples. We also demonstrate that, when compared to the standard power spectrum likelihood approach under the same assumption of fixed phases, the 1σ errors associated to the field level inference of the BAO scale are 1.1 to 3.3 times smaller, depending on the value of the cutoff and the halo sample. Our analysis therefore unveils another promising feature of using field-level inference for high-precision cosmology.