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 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.

Cone search capability for table J/AJ/159/200/known (Literature search results (Table 4) and catalog information (Table 5) for previously identified spectroscopic members)

Cone search capability for table J/AJ/159/200/cand (Astrometry (Table 9) and photometry (Table 10) information for the candidates identified by the highest ranked Random Forest classifier with a probability threshold of 0.4180)

Cone search capability for table J/AJ/159/200/table13 (Extended candidate catalog information)