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We present a real-time stamp classifier of astronomical events for the ALeRCE(Automatic Learning for the Rapid Classification of Events) broker. Theclassifier is based on a convolutional neural network, trained on alertsingested from the Zwicky Transient Facility (ZTF). Using only the\textit{science, reference} and \textit{difference} images of the firstdetection as inputs, along with the metadata of the alert as features, theclassifier is able to correctly classify alerts from active galactic nuclei,supernovae (SNe), variable stars, asteroids and bogus classes, with highaccuracy ($\sim$94\%) in a balanced test set. In order to find and analyze SNcandidates selected by our classifier from the ZTF alert stream, we designedand deployed a visualization tool called SN Hunter, where relevant informationabout each possible SN is displayed for the experts to choose among candidatesto report to the Transient Name Server database. From June 26th 2019 toFebruary 28th 2021, we have reported 6846 SN candidates to date (11.8candidates per day on average), of which 971 have been confirmedspectroscopically. Our ability to report objects using only a single detectionmeans that 70\% of the reported SNe occurred within one day after the firstdetection. ALeRCE has only reported candidates not otherwise detected orselected by other groups, therefore adding new early transients to the bulk ofobjects available for early follow-up. Our work represents an importantmilestone toward rapid alert classifications with the next generation of largeetendue telescopes, such as the Vera C. Rubin Observatory.

Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier