2 citations · 2 across the 3 of their papers we have counts for
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astro-ph.GA2024
Exploring galactic properties with machine learning Predicting star formation, stellar mass, and metallicity from photometric data
F. Z. Zeraatgari, F. Hafezianzadeh, Y. -X. Zhang +2
Aims. We explore machine learning techniques to forecast star formation rate, stellar mass, and metallicity across galaxies with redshifts ranging from 0.01 to 0.3. Methods. Levera…
astro-ph.GA2023★ 2 cited
Machine learning-based photometric classification of galaxies, quasars, emission-line galaxies, and stars
Fatemeh Zahra Zeraatgari, Fatemeh Hafezianzade, Yanxia Zhang +4
This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; star…