What question did this study set out to answer?

The research aims to predict the galaxy bias, a key parameter in understanding galaxy formation under large-scale structures, using machine learning techniques.

June 6, 2026

Predicting galaxy bias using machine learning

Puntos clave

The research aims to predict the galaxy bias, a key parameter in understanding galaxy formation under large-scale structures, using machine learning techniques.
Incorporated linear bias parameters into a machine learning framework using data from the IllustrisTNG300 simulation.
Utilized three predictive models: random forest regressor, single-output neural network, and normalizing flows.
Analyzed relationships between bias and features like overdensities and halo properties.
Normalizing flows outperformed deterministic methods in predicting galaxy bias and joint distributions with galaxy properties.
Most informative features for predicting bias were identified as overdensities, particularly δ_8, and distances to cosmic-web structures.
Demonstrated the ability to capture intrinsic variance associated with the stochastic matter-halo-galaxy connection.

Resumen

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. 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. We use data from the IllustrisTNG300 magnetohydrodynamical simulation, including the distance to different cosmic web structures computed with DisPerSE. These data are complemented with an object-by-object estimator of the large-scale linear bias (bᵢ), providing the individual contribution of each galaxy to the bias of the entire population. Our ML framework uses three models to predict bᵢ: a random forest regressor, a single-output neural network and a probabilistic method (normalizing flows). We recover the full hierarchy of galaxy bias dependencies, showing that the most informative features are the overdensities, particularly δ₈, followed by the distances to cosmic-web structures and selected internal halo properties, most notably the formation redshift (z₁/2). We also demonstrate that normalizing flows clearly outperform deterministic methods in predicting galaxy bias, including its joint distributions with galaxy properties, owing to their ability to capture the intrinsic variance associated with the stochastic nature of the matter-halo-galaxy connection. Our ML framework provides a foundation for future efforts to measure the individual bias with upcoming spectroscopic surveys.

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