DeepLSS: Breaking Parameter Degeneracies in Large-Scale Structure with Deep-Learning Analysis of Combined Probes

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 sigma(8) and matter density omega(m) roughly follow the S8 1/4 ci8 eth SZm/0.3 THORN 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 sigma(8) and omega(m) , 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 sigma(8), omega(m) , AIA, bg, rg, and IA redshift evolution parameter riIA. 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 sigma(8). Galaxy bias bg is improved by 1.5 times, stochasticity rg by 3 times, and the redshift evolution riIA and rib by 1.6 times. Breaking these degeneracies leads to a significant gain in constraining power for sigma(8) and omega(m) , 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.