A Machine Learning Approach to Estimating H I Deficiency in Galaxies

Measurements of the H i content of galaxies serve as an important tracer for probing the impact of environment on galaxy evolution. More specifically, the H i deficiency (defined as the difference between expected unaltered and observed H i content of a galaxy) is closely related with environmental effects, which are most significant in large groups and clusters. In this work, we aim to estimate the H i deficiency of ALFALFA galaxies and investigate its relation with galactic environment. Using a random forest machine learning algorithm, we developed a predictive model capable of estimating the original H i content of a galaxy based solely on its optical properties. The model was trained on a subsample of 6982 isolated ALFALFA galaxies with optical photometric data from the Sloan Digital Sky Survey. Our predictive model outperforms the traditional approach, in which H i mass is linearly related to optical size (both on a logarithmic scale). The model achieves RMSE ≈ 0.22 ± 0.004 dex and R2 ≈ 0.79 ± 0.008, compared with RMSE ≈ 0.26 ± 0.004 dex and R2 ≈ 0.70 ± 0.010 for the traditional method. We applied this model to predict the expected H i content for non-isolated ALFALFA galaxies, enabling the calculation of H i deficiency. Controlling for the effects of internal factors, like stellar mass and presence of AGN, we find an increase in binned median H i deficiency of 0.15 dex attributable to environmental effects. In addition, we evaluate the temporal evolution of the predicted H i mass, and associated H i deficiency, due to the evolving stellar populations, following a gas removal event.