Measuring Young Stars in Space and Time -- II. The Pre-Main-Sequence Stellar Content of N44
Victor F. Ksoll, Dimitrios Gouliermis, Elena Sabbi, Jenna E. Ryon,, Massimo Robberto, Mario Gennaro, Ralf S. Klessen, Ullrich Koethe, Guido de, Marchi, C.-H. Rosie Chen, Michele Cignoni, Andrew E. Dolphin

TL;DR
This study uses deep HST observations and machine learning to identify and analyze young pre-main-sequence stars in the N44 star-forming complex, revealing clustering structures and their association with known star-forming regions.
Contribution
Introduces a machine learning approach to distinguish PMS stars in extragalactic data and maps their clustering structure in N44.
Findings
Approximately 26,700 PMS candidates identified with high confidence.
Detected 18 significant PMS clustering structures, including a prominent star-forming subcluster.
Most clusters coincide with known H II regions and contain massive young stars.
Abstract
The Hubble Space Telescope (HST) 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…
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