Measuring Young Stars in Space and Time. II. The Pre-main-sequence Stellar Content of N44

• Published in: The Astronomical journal 2021-06, Vol.161 (6), p.257
• Main Authors: Ksoll, Victor F., Gouliermis, Dimitrios, Sabbi, Elena, Ryon, Jenna E., Robberto, Massimo, Gennaro, Mario, Klessen, Ralf S., Koethe, Ullrich, de Marchi, Guido, Chen, C.-H. Rosie, Cignoni, Michele, Dolphin, Andrew E.
• Format: Article
• Language: English
• Subjects: Astronomical catalogs; Astronomy; ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; CLASSIFICATION; Clustering; Contaminants; DENSITY; H II regions; Hubble Space Telescope; INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Large Magellanic Cloud; Low mass stars; MACHINE LEARNING; MAGELLANIC CLOUDS; O stars; Photometric observations; PHOTOMETRY; Pre-main sequence stars; Random Forests; SPACE; Space telescopes; Star & galaxy formation; Star formation; Star forming regions; STARS; Stellar evolution; Support vector machine; Support vector machines; SURFACES; TELESCOPES; VECTORS; Young star clusters
• ISSN: 0004-6256; 1538-3881; 1538-3881
• DOI: 10.3847/1538-3881/abee8c

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 1 σ significance above the mean density with sub-clusters persisting up to and beyond 3 σ 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.