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We apply machine learning algorithms to classify Infrared (IR)-selectedtargets for NASA's upcoming SPHEREx mission. In particular, we are interestedin classifying Young Stellar Objects (YSOs), which are essential forunderstanding the star formation process. Our approach differs from previouswork, which has relied heavily on broadband color criteria to classifyIR-bright objects, and are typically implemented in color-color andcolor-magnitude diagrams. However, these methods do not state the confidenceassociated with the classification and the results from these methods are quiteambiguous due to the overlap of different source types in these diagrams. Here,we utilize photometric colors and magnitudes from seven near and mid-infraredbands simultaneously and employ machine and deep learning algorithms to carryout probabilistic classification of YSOs, Asymptotic Giant Branch (AGB) stars,Active Galactic Nuclei (AGN) and main-sequence (MS) stars. Our approach alsosub-classifies YSOs into Class I, II, III and flat spectrum YSOs, and AGB starsinto carbon-rich and oxygen-rich AGB stars. We apply our methods toinfrared-selected targets compiled in preparation for SPHEREx which are likelyto include YSOs and other classes of objects. Our classification indicates thatout of $8,308,384$ sources, $1,966,340$ have class prediction with probabilityexceeding $90\%$, amongst which $\sim 1.7\%$ are YSOs, $\sim 58.2\%$ are AGBstars, $\sim 40\%$ are (reddened) MS stars, and $\sim 0.1\%$ are AGN whose redbroadband colors mimic YSOs. We validate our classification using the spatialdistributions of predicted YSOs towards the Cygnus-X star-forming complex, aswell as AGB stars across the Galactic plane.

Probabilistic Classification of Infrared-selected targets for SPHEREx mission: In search of YSOs