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Characterising X-ray Stellar Content of Our Galaxy Using Archival Data
Thursday 19 February 2026, 04:00pm - 05:00pm
Dr. Pooja Sharma, (Thapar Institute of Engineering & Technology)
Location : AB1 Conference room
Abstract: I will present my work on the population study of the Milky Way’s X-ray
emitting stellar objects by combining the latest XMM-Newton (4XMM-DR13)
observations with Gaia (DR3) astrometry and photometry. Using the
probabilistic ARCHES cross-matching tool developed at the Observatory of
Strasbourg, we identified 37,901 high-confidence optical stellar
counterparts to XMM sources. The CMD revealed a prominent second
sequence, which Simbad classifications showed to be dominated by
magnetically active young stars and unresolved binaries.
To extend classifications beyond the limited Simbad labels, we trained a deep neural network to distinguish Binaries, Evolved, Main Sequence, Peculiar, and Young stars, achieving 77% accuracy. Together, probabilistic cross-matching, multiwavelength data, and machine learning provide an efficient framework for automatically characterizing Galactic X-ray stellar populations.
To extend classifications beyond the limited Simbad labels, we trained a deep neural network to distinguish Binaries, Evolved, Main Sequence, Peculiar, and Young stars, achieving 77% accuracy. Together, probabilistic cross-matching, multiwavelength data, and machine learning provide an efficient framework for automatically characterizing Galactic X-ray stellar populations.
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