In classical cosmological analysis of large-scale structure surveys with two-point functions, the parameter measurement precision is limited by several key degeneracies within the cosmology and astrophysics sectors. For cosmic shear, clustering amplitude σ8 and matter density ωm roughly follow the S8=σ8(ωm/0.3)0.5 relation. In turn, S8 is highly correlated with the intrinsic galaxy alignment amplitude AIA. For galaxy clustering, the bias bg is degenerate with both σ8 and ωm, as well as the stochasticity rg. Moreover, the redshift evolution of intrinsic alignment (IA) and bias can cause further parameter confusion. A tomographic two-point probe combination can partially lift these degeneracies. In this work we demonstrate that a deep-learning analysis of combined probes of weak gravitational lensing and galaxy clustering, which we call DeepLSS, can effectively break these degeneracies and yield significantly more precise constraints on σ8, ωm, AIA, bg, rg, and IA redshift evolution parameter ηIA. In a simulated forecast for a stage-III survey, we find that the most significant gains are in the IA sector: the precision of AIA is increased by approximately 8 times and is almost perfectly decorrelated from S8. Galaxy bias bg is improved by 1.5 times, stochasticity rg by 3 times, and the redshift evolution ηIA and ηb by 1.6 times. Breaking these degeneracies leads to a significant gain in constraining power for σ8 and ωm, with the figure of merit improved by 15 times. We give an intuitive explanation for the origin of this information gain using sensitivity maps. These results indicate that the fully numerical, map-based forward-modeling approach to cosmological inference with machine learning may play an important role in upcoming large-scale structure surveys. We discuss perspectives and challenges in its practical deployment for a full survey analysis.

Physical Review X, 12 (3)

ISSN:2160-3308