Unveiling the drivers of the Baryon Cycles with Interpretable Multi-step Machine Learning and Simulations

Abstract

We present a new approach for understanding how galaxies lose or retain baryons by utilizing a pipeline of two machine learning methods applied the IllustrisTNG100 simulation. We employed a Random Forest Regressor and Explainable Boosting Machine (EBM) model to connect the retained baryon fraction of approximately 10^5 simulated galaxies to their properties. We employed Random Forest models to filter and used the five most significant properties to train an EBM. Interaction functions identified by the EBM highlight the relationship between baryon fraction and three different galactic mass measurements, the location of the rotation curve peak, and the velocity dispersion. This interpretable machine learning-based approach provides a promising pathway for understanding the baryon cycle in galaxies.