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20242026
most citedMachine Learning Classification of Young Stellar Objects and Evolved Stars in the Magellanic Clouds Using the Probabilistic Random Forest Classifier

2 citations · 2 across the 10 of their papers we have counts for

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astro-ph.GA2025

Metallicity Effects on Machine Learning Classification of Dusty Stellar Sources in the Magellanic Clouds

Sepideh Ghaziasgar, Mahdi Abdollahi, Atefeh Javadi +4

Differences in metallicity between the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) offer an opportunity to examine whether environmental metallicity affects t…

astro-ph.GA2025

Comparison of Photometric and Spectroscopic Labels in Classifying Dusty Stellar Sources Using Machine Learning in the Magellanic Clouds

Sepideh Ghaziasgar, Mahdi Abdollahi, Atefeh Javadi +4

Dusty stellar sources, including young stellar objects (YSOs) and evolved stars such as oxygen- and carbon-rich AGBs (OAGBs, CAGBs), red supergiants (RSGs), and post-AGB stars (PAG…

astro-ph.GA2025

Long-period variable stars in NGC 147 and NGC 185-II. Their dust production

Hamidreza Mahani, Atefeh Javadi, Jacco van Loon +6

This study presents a comparative analysis of mass-loss and dust-production rates in the dwarf galaxies NGC 147 and NGC 185, focusing on long-period variables (LPVs) and pulsating…

astro-ph.GA2025

The Isaac Newton Telescope Monitoring Survey of Local Group Dwarf Galaxies. VII. Long-Period Variable Stars in the Nearest Starburst Dwarf Galaxy, IC 10

Mahtab Gholami, Atefeh Javadi, Hamidreza Mahani +7

To identify long-period variable (LPV) stars in IC10 - the nearest starburst galaxy of the Local Group (LG) - we conducted an optical monitoring survey using the 2.5-m Isaac Newton…

astro-ph.GA2025

Dusty stellar sources classification by implementing machine learning methods based on spectroscopic observations in the Magellanic Clouds

Sepideh Ghaziasgar, Mahdi Abdollahi, Atefeh Javadi +7

Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and s…

astro-ph.GA2025★ 2 cited

Machine Learning Classification of Young Stellar Objects and Evolved Stars in the Magellanic Clouds Using the Probabilistic Random Forest Classifier

Sepideh Ghaziasgar, Mahdi Abdollahi, Atefeh Javadi +4

The Magellanic Clouds (MCs) are excellent locations to study stellar dust emission and its contribution to galaxy evolution. Through spectral and photometric classification, MCs ca…