Among the first observations released to the public from the JWST was a section of the star-forming region NGC 3324 known colloquially as the "Cosmic Cliffs." We build a photometric catalog of the region and test the ability of using the probabilistic random forest machine-learning method to identify its young stellar objects (YSOs). We find 450 candidate YSOs (cYSOs) out of 19497 total objects within the field, 413 of which are cYSOs not found in previous works. These classifications are verified with several different metrics, including recall and precision. Using the obtained probabilities of objects being YSOs, we employ a Monte Carlo approach to determine the surface density of cYSOs in the Cosmic Cliffs, which we find to be largely coincident with column densities derived from Herschel data, up to a column density of 1.37x10^22^cm^-2^. The newly determined number and spatial distribution of YSOs in the Cosmic Cliffs demonstrate that JWST is far more capable of detecting YSOs in dusty regions than Spitzer. Comparisons of the observed colors and brightness of faint cYSOs with those of pre-main-sequence models suggest JWST has detected a significant population of substellar YSOs in the Cosmic Cliffs. The size of this population further suggests previous estimates of star formation efficiencies in molecular clouds have been systematically low.

Cone search capability for table J/AJ/168/63/table3 (Fluxes, J2000 coordinates, and probabilities for sources classified as candidate YSOs)

Cone search capability for table J/AJ/168/63/table4 (Candidate YSOs from other works matched to our candidate YSOs)