2007/03/29 by Douglas H. Rudd, Andrew R. Zentner, Andrey V. Kravtsov · 12 citations
Physics and Astronomy · #Astrophysics #Baryon #Cold dark matter #Cosmology #Cosmology and Gravitation Theories #Dark Matter and Cosmic Phenomena #Dark energy #Dark matter #Galaxies: Formation, Evolution, Phenomena #Galaxy #Halo #Matter power spectrum #Physics #Redshift #Statistical physics #Theoretical physics #Weak gravitational lensing #astro-ph
paper · pdf · doi:10.1086/523836
published as Astrophys.J.672:19-32,2008 · 14 pages, 8 figures. Submitted to ApJ
arxiv created 2007/03/29 · openalex publication_date 2008/01/01 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We study the importance of baryonic physics on predictions of the matter power spectrum as it is relevant for forthcoming weak-lensing surveys. We quantify the impact of baryonic physics using a set of cosmological numerical simulations. Each simulation has the same initial density field, but models a different set of physical processes. We find that baryonic processes significantly alter predictions for the matter power spectrum relative to models that include only gravitational interactions. Our results imply that future weak-lensing experiments such as LSST and SNAP will likely be sensitive to the uncertain physics governing the nonlinear evolution of the baryonic component of the universe if these experiments are primarily limited by statistical uncertainties. In particular, this effect could be important for forecasts of the constraining power of future surveys if information from scales l ≳ 1000 is included in the analysis. We find that deviations are caused primarily by the rearrangement of matter within individual dark matter halos relative to the gravity-only case, rather than a large-scale rearrangement of matter. Consequently, we propose a simple model, based on the phenomenological halo model of dark matter clustering, for baryonic effects that can be used to aid in the interpretation of forthcoming weak-lensing data.