Predicting galaxy bias using machine learning

Дата публикации: 18-08-2026 00:00:00

Context. Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This connection, commonly described through the galaxy bias, b , can be studied effectively using machine-learning (ML) techniques, which offer strong predictive capabilities and can capture nonlinear relationships in high-dimensional data. Recent work has also highlighted the need for probabilistic methods to properly account for the intrinsic stochasticity of this connection. Aims. We aim to incorporate the linear bias parameter assigned to individual galaxies into a ML framework, quantify its dependence on various halo and environmental properties, and evaluate whether different algorithms can accurately predict this parameter and reproduce the scatter in several bias relations. Methods. We use data from the IllustrisTNG300 magnetohydrodynamical...

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