Capturing star formation activity from compressed photometric images of galaxies
arXiv:2507.23019
Abstract
We present a novel approach for classifying star-forming galaxies using photometric images. By utilizing approximately optical color composite images and spectroscopic data of nearby galaxies at 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 (DES), and the Legacy Survey of Space and Time (LSST).
Accepted for publication in ApJ