Capturing Star Formation Activity from Compressed Photometric Images of Galaxies

Abstract

We present a novel approach for classifying star-forming galaxies using photometric images. By utilizing approximately 124,000 optical color composite images and spectroscopic data of nearby galaxies at 0.01 < z < 0.06 from the Sloan Digital Sky Survey, along with follow-up spectroscopic line measurements from the OSSY catalog, and leveraging the vision transformer machine learning technique, we demonstrate that galaxy images in JPEG format alone can be directly used to determine whether star-forming activity dominates the galaxy, bypassing traditional spectroscopic analyses such as emission-line diagnostic diagrams. We anticipate that this method holds significant potential for application in current and future large-scale surveys, such as Euclid, the Dark Energy Survey, and the Legacy Survey of Space and Time.