Astron. J., 161, 257-257 (2021/June-0)
Measuring Young Stars in Space and Time. II. The pre-main-sequence stellar content of N44.
KSOLL V.F., GOULIERMIS D., SABBI E., RYON J.E., ROBBERTO M., GENNARO M., KLESSEN R.S., KOETHE U., DE MARCHI G., CHEN C.-H.R., CIGNONI M. and DOLPHIN A.E.
Abstract (from CDS):
The Hubble Space Telescope survey Measuring Young Stars in Space and Time (MYSST) entails some of the deepest photometric observations of extragalactic star formation, capturing even the lowest-mass stars of the active star-forming complex N44 in the Large Magellanic Cloud. We employ the new MYSST stellar catalog to identify and characterize the content of young pre-main-sequence (PMS) stars across N44 and analyze the PMS clustering structure. To distinguish PMS stars from more evolved line of sight contaminants, a non-trivial task due to several effects that alter photometry, we utilize a machine-learning classification approach. This consists of training a support vector machine (SVM) and a random forest (RF) on a carefully selected subset of the MYSST data and categorize all observed stars as PMS or non-PMS. Combining SVM and RF predictions to retrieve the most robust set of PMS sources, we find ∼26,700 candidates with a PMS probability above 95% across N44. Employing a clustering approach based on a nearest neighbor surface density estimate, we identify 16 prominent PMS structures at 1σ significance above the mean density with sub-clusters persisting up to and beyond 3σ significance. The most active star-forming center, located at the western edge of N44's bubble, is a subcluster with an effective radius of ∼5.6 pc entailing more than 1100 PMS candidates. Furthermore, we confirm that almost all identified clusters coincide with known H II regions and are close to or harbor massive young O stars or YSOs previously discovered by MUSE and Spitzer observations.
© 2021. The American Astronomical Society. All rights reserved.
Pre-main sequence stars - Young star clusters - Low mass stars - Large Magellanic Cloud - Star-forming regions - Support vector machine - Random Forests
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