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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.23019 |
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Table of Contents:
- 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 (DES), and the Legacy Survey of Space and Time (LSST).