SHAPing the gas: understanding gas shapes in dark matter haloes with interpretable machine learning

The non-spherical shapes of dark matter and gas distributions introduce systematic uncertainties that affect observable-mass relations and selection functions of galaxy groups and clusters. However, the triaxial gas distributions depend on the non-linear physical processes of halo formation histories and baryonic physics, which are challenging to model accurately. In this study, we explore a machine learning approach for modelling the dependence of gas shapes on dark matter and baryonic properties. With data from the IllustrisTNG hydrodynamical cosmological simulations, we develop a machine learning pipeline that applies XGBoosT, an implementation of gradient-boosted decision trees, to predict radial profiles of gas shapes from halo properties. We show that XGBoosT models can accurately predict gas shape profiles in dark matter haloes. We also explore model interpretability with the SHapley Additive exPlanations (sHAP), a method that identifies the most predictive properties at different halo radii. We find that baryonic properties best predict gas shapes in halo cores, whereas dark matter shapes are the main predictors in the halo outskirts. This work demonstrates the power of interpretable machine learning in modelling observable properties of dark matter haloes in the era of multiwavelength cosmological surveys.

Automated summary

This study explores the dependence of gas shapes on dark matter and baryonic properties using a machine learning approach and identifies the most predictive properties at different halo radii. By utilizing interpretable machine learning tools, it demonstrates the power of predicting gas shapes in halo cores with baryonic properties and in the halo outskirts with dark matter shapes.