The identification and characterization of stellar members within a star-forming region are critical to many aspects of star formation, including formalization of the initial mass function, circumstellar disk evolution and star-formation history. Previous surveys of the Lupus star-forming region have identified members through infrared excess and accretion signatures. We use machine learning to identify new candidate members of Lupus based on surveys from two space-based observatories: ESA's Gaia and NASA's Spitzer. Astrometric measurements from Gaia's Data Release 2 and astrometric and photometric data from the Infrared Array Camera (IRAC) on the Spitzer Space Telescope, as well as from other surveys, are compiled into a catalog for the Random Forest (RF) classifier. The RF classifiers are tested to find the best features, membership list, non-membership identification scheme, imputation method, training set class weighting and method of dealing with class imbalance within the data. We list 27 candidate members of the Lupus star-forming region for spectroscopic follow-up. Most of the candidates lie in Clouds V and VI, where only one confirmed member of Lupus was previously known. These clouds likely represent a slightly older population of star-formation.
@article{arxiv.2003.10575,
title = {A Random Forest Approach to Identifying Young Stellar Object Candidates in the Lupus Star-Forming Region},
author = {Elizabeth Melton},
journal= {arXiv preprint arXiv:2003.10575},
year = {2020}
}