Simultaneously constraining cosmology and baryonic physics via deep learning from weak lensing
Abstract
Ongoing and planned weak lensing (WL) surveys are becoming deep enough to contain information on angular scales down to a few arcmin. To fully extract information from these small scales, we must capture nonGaussian features in the cosmological WL signal while accurately accounting for baryonic effects. In this work, we account for baryonic physics via a baryonic correction model that modifies the matter distribution in dark matteronly Nbody simulations, mimicking the effects of galaxy formation and feedback. We implement this model in a large suite of raytracing simulations, spanning a grid of cosmological models in Ω_{m}σ_{8} space. We then develop a convolutional neural network (CNN) architecture to learn and constrain cosmological and baryonic parameters simultaneously from the simulated WL convergence maps. We find that in a HyperSuprime Camlike survey, our CNN achieves a 1.7× tighter constraint in Ω_{m}σ_{8} space (1σ area) than the power spectrum and 2.1× tighter than the peak counts, showing that the CNN can efficiently extract nonGaussian cosmological information even while marginalizing over baryonic effects. When we combine our CNN with the power spectrum, the baryonic effects degrade the constraint in Ω_{m}σ_{8} space by a factor of 2.4, compared to the much worse degradation by a factor of 4.7 or 3.7 from either method alone.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 March 2022
 DOI:
 10.1093/mnras/stac161
 arXiv:
 arXiv:2109.11060
 Bibcode:
 2022MNRAS.511.1518L
 Keywords:

 gravitational lensing: weak;
 cosmological parameters;
 cosmology: theory;
 largescale structure of Universe;
 Astrophysics  Cosmology and Nongalactic Astrophysics
 EPrint:
 12 pages, 6 figures