Speakers
Description
The UV continuum slope (β) of high-redshift galaxies is an important observable for understanding the stellar populations, dust attenuation, and ionizing photon production of galaxies during the Epoch of Reionization. However, the relationship between UV β slopes and underlying galaxy properties is highly non-linear, involving the combined effects of star formation, metallicity, gas evolution, and feedback processes.
In this work, we develop a machine learning framework to identify the key physical properties that determine the UV β slope of reionization-era galaxies using the SIMBA-EoR cosmological simulation. We train supervised learning models to predict galaxy UV β slopes from a set of intrinsic galaxy properties, including stellar mass, star formation rate, metallicity, gas properties, and structural parameters. By comparing model performance and applying interpretable machine learning techniques, we quantify the relative importance of different galaxy properties and uncover the dominant features driving variations in UV spectral slopes.
Our approach allows us to explore complex non-linear relationships beyond traditional correlation analyses and provides a systematic method to connect observable galaxy properties with their underlying physical processes. The results will help improve the interpretation of high-redshift galaxy observations from facilities such as the James Webb Space Telescope and provide new insights into the physical origins of UV β slope diversity during cosmic reionization.