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Stars are ubiquitous X-ray emitters and will be a substantial fraction of theX-ray sources detected in the on-going all-sky survey performed by the eROSITAinstrument aboard the Spectrum Roentgen Gamma (SRG) observatory. We use theX-ray sources in the eROSITA Final Equatorial-Depth Survey (eFEDS) fieldobserved during the SRG performance verification phase to investigate differentstrategies to identify the stars among other source categories. We focus hereon Support Vector Machine (SVM) and Bayesian approaches, and our approaches arebased on a cross-match with the Gaia catalog, which will eventually containcounterparts to virtually all stellar eROSITA sources. We estimate that 2060stars are among the eFEDS sources based on the geometric match distancedistribution, and we identify the 2060 most likely stellar sources with the SVMand Bayesian methods, the latter being named HamStars in the eROSITA context.Both methods reach completeness and reliability percentages of almost 90%, andthe agreement between both methods is, incidentally, also about 90%. Knowingthe true number of stellar sources allowed us to derive associationprobabilities $p_{ij}$ for the SVM method similar to the Bayesian method sothat one can construct samples with defined completeness and reliabilityproperties using appropriate cuts in $p_{ij}$. The thus identified stellarsources show the typical characteristics known for magnetically active stars,specifically, they are generally compatible with the saturation level, show alarge spread in activity for stars of spectral F to G, and have comparativelyhigh fractional X-ray luminosities for later spectral types.

astronomy and astrophysics

The eROSITA Final Equatorial-Depth Survey (eFEDS). The stellar  counterparts of eROSITA sources identified by machine learning and Bayesian  algorithms