The stellar content of soft X-ray surveys

In the chromosphere of cool stars, the temperature starts rising with increasing height and values of more than 10${}^6$ K are reached in the corona. Hence, the corona emits most of its luminosity in soft X-rays. Its contribution to the total energy output of the star is small due to the low density of the corona. The mechanism of the coronal heating is still not fully understood but magnetic fields are thought to play a fundamental role. The study of large samples of stellar X-ray sources allows to determine the relation of the X-ray activity on mass, age, and stellar rotation. Counterparts in the optical or near infrared (NIR) to the X-ray sources provide additional information about the source, e.g., optical magnitude, color, and parallax, and help to identify stellar sources. However, the probability of obtaining counterparts that are by chance located in the same direction but are not associated to the X-ray source is often high due to large positional uncertainties of X-ray detections compared to other wavelength regimes. This thesis deals with methods to identify the stellar content of large X-ray surveys. Due to the high number of X-ray sources, automatic algorithms are presented that typically take into account geometric and further properties of the counterparts. The resulting flux-limited samples of stellar X-ray sources are subsequently analyzed. After an introduction to the background of this thesis, I present the stellar content of the XMM-Newton slew survey (XMMSL2) that is solely obtained by the geometric separations between the XMMSL2 sources and counterpart from various optical and NIR catalogs. The obtained sample contains 5920 bona fide stellar X-ray emitters and is expected to be complete and reliable to more than 96 %. For the first identification of the whole stellar content of the ROSAT all-sky survey (RASS), Bayes maps are applied to consider further properties of the counterparts in addition to the geometric match. It is shown that the results only weakly depend on the details of the estimation of the Bayes map. With this procedure, more than 28000 RASS sources are identified as stellar, which is the largest sample of stellar X-ray sources presented so far. it is a flux-limited sample containing all types of stars that were previously investigated separately. From matching probabilities provided for every source, the completeness and reliability is estimated to be about 93 %. A similar identification procedure is also applied to the eROSITA Final Equatorial-Depth Survey (eFEDS) and the results are compared to the identification of a machine learning method and another Bayesian approach that is not specialized to stars. Of the eFEDS detections, 2060 sources are identified as stars with a completeness and reliability of about 90 %. The identifications are confirmed by the other procedures also to about 90 %. The stellar identifications from the RASS and pointing observations with ROSAT, Chandra, and XMM-Newton are applied to analyze the X-ray properties of the Hyades cluster. X-ray detections for 281 of 1066 Hyades members are found. The X-ray activities increase for sources of later spectral types reaching the saturation limit for about M3 dwarfs. The rotation-activity relation of the Hyades members confirms previous results. With the identification procedure presented in this thesis and some modifications, it will be possible to identify the stellar content of the currently ongoing eROSITA all-sky survey (eRASS) and to revolutionize our understanding of stellar activity.