Unveiling the drivers of the Baryon Cycles with Interpretable Multi-step Machine Learning and Simulations
Year: 2026
Type: article
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 z=0 retained baryon fraction of ≈ 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 massmeasurements, 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.
Source: The Open Journal of Astrophysics
DOI: 10.33232/001c.156271
Authors: Mst Shamima Khanom, Benjamin W. Keller, Javier Ignacio Saavedra Moreno
Institution: University of Memphis
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414830109
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