Contributed Talk - Splinter eROSITA
How eROSITA discovers new members of star forming regions
P.C. Schneider, J. Robrade, S. Czesla
The vast majority of young stars looks quite like normal, main-sequence stars in the optical. Therefore, identifying young stars on their journey towards the main sequence has been and is still very challenging. One feature that sets young stars apart from their older siblings, however, is their short rotation period. This leads to pronounced activity features such as high levels of X-ray emission. Using the stellar X-ray emission as a key tracer to discover young stars has been greatly hampered by the small sky coverage of modern X-ray instruments and only became possible with the sensitivity and sky coverage provided by the eROSITA all-sky survey (eRASS). I will present two methods to discover new members of star forming regions: One uses a more classical approach based on the similarity between candidate and known members (proper motion, distance, etc.) while the second approach is based on unsupervised machine learning. Specifically, I will present results for the Lupus star forming region using both methods, describe how the two methods differ, their advantages and disadvantages, and how the two methods can be used for the eRASS at large.