A random forest model trained on isolated ALFALFA-SDSS galaxies predicts HI mass from optical properties with RMSE≈0.22 dex, revealing a 0.15 dex median HI deficiency increase in dense environments.
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A machine learning approach to estimating HI deficiency in galaxies
A random forest model trained on isolated ALFALFA-SDSS galaxies predicts HI mass from optical properties with RMSE≈0.22 dex, revealing a 0.15 dex median HI deficiency increase in dense environments.