Exploring galactic properties with machine learning: Predicting star formation, stellar mass, and metallicity from photometric data

Abstract

Aims. We explore machine learning techniques to forecast the star-formation rate, stellar mass, and metallicity across galaxies with redshifts ranging from 0.01 to 0.3. Methods. Leveraging CatBoost and deep learning architectures, we utilised multiband optical and infrared photometric data from SDSS and AllWISE trained on the SDSS MPA-JHU DR8 catalogue. Results. Our study demonstrates the potential of machine learning to accurately predict galaxy properties solely from photometric data. We achieved minimised root mean square errors specifically by employing the CatBoost model. For the star-formation rate prediction, we attained a value of RMSESFR = 0.336 dex, while for the stellar mass prediction, the error was reduced to RMSESM = 0.206 dex. Additionally, our model yields a metallicity prediction of RMSEmetallicity = 0.097 dex. Conclusions. These findings underscore the significance of automated methodologies in efficiently estimating critical galaxy properties amid the exponential growth of multi-wavelength astronomy data. Future research may focus on refining machine learning models and expanding datasets for even more accurate predictions.