The eROSITA Final Equatorial-Depth Survey (eFEDS). The stellar counterparts of eROSITA sources identified by machine learning and Bayesian algorithms
P. C. Schneider, S. Freund, S. Czesla, J. Robrade, M. Salvato, J. H., M. M. Schmitt

TL;DR
This study employs machine learning and Bayesian algorithms to identify stellar X-ray sources in the eROSITA eFEDS survey, achieving high completeness and reliability, and characterizing the properties of these stars.
Contribution
It introduces and compares SVM and Bayesian methods for stellar source identification in X-ray surveys, demonstrating high accuracy and providing a framework for constructing reliable stellar samples.
Findings
Both methods reach ~90% completeness and reliability.
Agreement between methods is about 90%.
Identified stars exhibit known magnetic activity characteristics.
Abstract
Stars are ubiquitous X-ray emitters and will be a substantial fraction of the X-ray sources detected in the on-going all-sky survey performed by the eROSITA instrument aboard the Spectrum Roentgen Gamma (SRG) observatory. We use the X-ray sources in the eROSITA Final Equatorial-Depth Survey (eFEDS) field observed during the SRG performance verification phase to investigate different strategies to identify the stars among other source categories. We focus here on Support Vector Machine (SVM) and Bayesian approaches, and our approaches are based on a cross-match with the Gaia catalog, which will eventually contain counterparts to virtually all stellar eROSITA sources. We estimate that 2060 stars are among the eFEDS sources based on the geometric match distance distribution, and we identify the 2060 most likely stellar sources with the SVM and Bayesian methods, the latter being named…
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Taxonomy
TopicsAstronomical Observations and Instrumentation · Astronomy and Astrophysical Research · Gamma-ray bursts and supernovae
