2016/11/30 by Hamsa Padmanabhan, Alexandre Réfrégier, Alexandre Refregier +2 · 3 citations
Physics and Astronomy · #Astronomy and Astrophysical Research #Astrophysics #Astrophysics and Star Formation Studies #Galaxies: Formation, Evolution, Phenomena #Galaxy #Galaxy formation and evolution #Halo #Markov chain Monte Carlo #Monte Carlo method #Physics #Redshift #Reionization #Statistics #astro-ph.CO #astro-ph.GA
paper · pdf · doi:10.1093/mnras/stx979
published as MNRAS, Volume 469, Issue 2, p.2323-2334 (2017) · 13 pages, 12 figures, 5 tables; version accepted for publication in MNRAS
openalex created_date 2016/11/30 · arxiv created 2017/04/21 · openalex publication_date 2017/04/24 · arxiv updated 2017/08/23 · openalex updated_date 2026/08/05
We extend the results of previous analyses towards constraining the abundance and clustering of post-reionization (z ∼ 0–5) neutral hydrogen (H i) systems using a halo model framework. We work with a comprehensive H i data set including the small-scale clustering, column density and mass function of H i galaxies at low redshifts, intensity mapping measurements at intermediate redshifts and the ultraviolet/optical observations of Damped Lyman Alpha (DLA) systems at higher redshifts. We use a Markov Chain Monte Carlo (MCMC) approach to constrain the parameters of the best-fitting models, both for the H i–halo mass (HIHM) relation and the H i radial density profile. We find that a radial exponential profile results in a good fit to the low-redshift H i observations, including the clustering and the column density distribution. The form of the profile is also found to match the high-redshift DLA observations, when used in combination with a three-parameter HIHM relation and a redshift evolution in the H i concentration. The halo model predictions are in good agreement with the observed H i surface density profiles of low-redshift galaxies, and the general trends in the impact parameter and covering fraction observations of high-redshift DLAs. We provide convenient tables summarizing the best-fitting halo model predictions.