DeepLSS: breaking parameter degeneracies in large scale structure with deep learning analysis of combined probes
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In classical cosmological analysis of large scale structure surveys with 2-pt functions, the parameter measurement precision is limited by several key degeneracies within the cosmology and astrophysics sectors. For cosmic shear, clustering amplitude σ_8 and matter density Ω_m roughly follow the S_8=σ_8(Ω_m/0.3)^0.5 relation. In turn, S_8 is highly correlated with the intrinsic galaxy alignment amplitude A_IA. For galaxy clustering, the bias b_g is degenerate with both σ_8 and Ω_m, as well as the stochasticity r_g. Moreover, the redshift evolution of IA and bias can cause further parameter confusion. A tomographic 2-pt 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 σ_8, Ω_m, A_IA, b_g, r_g, and IA redshift evolution parameter η_IA. The most significant gains are in the IA sector: the precision of A_IA is increased by approximately 8x and is almost perfectly decorrelated from S_8. Galaxy bias b_g is improved by 1.5x, stochasticity r_g by 3x, and the redshift evolution η_IA and η_b by 1.6x. Breaking these degeneracies leads to a significant gain in constraining power for σ_8 and Ω_m, with the figure of merit improved by 15x. 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 LSS surveys. We discuss perspectives and challenges in its practical deployment for a full survey analysis.
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