Papers › DeepLSS: breaking parameter degeneracies in large scale structure with deep learning...
DeepLSS: breaking parameter degeneracies in large scale structure with deep learning analysis of combined probes
Tomasz Kacprzak, Janis Fluri
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 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 A_(IA). For galaxy clustering, the bias b_g is degenerate with both σ₈ and Ωₘ, 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 σ₈, Ωₘ, 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 S₈. 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 σ₈ and Ωₘ, 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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections