Understanding the regulation of star formation within TNG100 galaxies on kpc-scales using machine learning I: Global versus local

TL;DR

This study uses machine learning on simulated galaxy data to identify key factors influencing star formation and quenching, revealing local and global property roles.

Contribution

It demonstrates the effectiveness of Random Forest and XGBoost in predicting galaxy quenching and star formation, highlighting dominant features like black hole mass and stellar surface density.

Findings

Black hole mass is crucial for quenching in central and high-mass satellite galaxies.

Halo mass predominantly influences environmental quenching in low-mass satellites.

Local stellar mass surface density strongly predicts star formation activity.

Abstract

We apply Random Forest and XGBoost machine learning algorithms to determine which galaxy properties most effectively predict star formation and quenching in simulated galaxies. Using spatially-resolved data from approximately 63,000 annular bins across 6,189 TNG100 galaxies, we train classification models to predict quenching states and regression models to predict star formation rate surface densities. Despite their different algorithmic approaches, both methods produce consistent feature importance rankings, with XGBoost distributing importance more evenly among correlated features. For central galaxies and high-mass satellites, black hole mass dominates quenching predictions, consistent with quenching via active galactic nuclei (AGN) feedback. Classification of low-mass satellites shows overwhelming importance for halo mass, indicating environmental quenching. Star formation predictions are dominated by local stellar mass surface density across all star-forming galaxy types, confirming that active star formation is a local process while quenching is driven by global properties.