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Over the past decade wide-field optical time-domain surveys have increasedthe discovery rate of transients to the point that $\lesssim 10\%$ are beingspectroscopically classified. Despite this, these surveys have enabled thediscovery of new and rare types of transients, most notably the class ofhydrogen-poor superluminous supernovae (SLSN-I), with about 150 eventsconfirmed to date. Here we present a machine-learning classification algorithmtargeted at rapid identification of a pure sample of SLSN-I to enablespectroscopic and multi-wavelength follow-up. This algorithm is part of theFLEET (Finding Luminous and Exotic Extragalactic Transients) observationalstrategy. It utilizes both light curve and contextual information, but withoutthe need for a redshift, to assign each newly-discovered transient aprobability of being a SLSN-I. This classifier can achieve a maximum purity ofabout 85\% (with 20\% completeness) when observing a selection of SLSN-Icandidates. Additionally, we present two alternative classifiers that useeither redshifts or complete light curves and can achieve an even higher purityand completeness. At the current discovery rate, the FLEET algorithm canprovide about $20$ SLSN-I candidates per year for spectroscopic follow-up with85\% purity; with the Legacy Survey of Space and Time we anticipate this willrise to more than $\sim 10^3$ events per year.

FLEET: A Redshift-Agnostic Machine Learning Pipeline to Rapidly Identify Hydrogen-Poor Superluminous Supernovae