Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier
Rodrigo Carrasco-Davis, Esteban Reyes, Camilo Valenzuela, Francisco, F\"orster, Pablo A. Est\'evez, Giuliano Pignata, Franz E. Bauer, Ignacio, Reyes, Paula S\'anchez-S\'aez, Guillermo Cabrera-Vives, Susana Eyheramendy,, M\'arcio Catelan, Javier Arredondo

TL;DR
This paper introduces a real-time convolutional neural network classifier for astronomical alerts in the ALeRCE system, enabling rapid identification of various transient objects from ZTF data with high accuracy.
Contribution
The paper presents a novel real-time alert classifier using CNNs trained on ZTF data, and a visualization tool for early supernova candidate identification, enhancing rapid transient detection capabilities.
Findings
Achieved 94% accuracy in alert classification
Reported 6846 supernova candidates, with 971 spectroscopically confirmed
70% of supernovae detected within one day of first alert
Abstract
We present a real-time stamp classifier of astronomical events for the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker. The classifier is based on a convolutional neural network, trained on alerts ingested from the Zwicky Transient Facility (ZTF). Using only the \textit{science, reference} and \textit{difference} images of the first detection as inputs, along with the metadata of the alert as features, the classifier is able to correctly classify alerts from active galactic nuclei, supernovae (SNe), variable stars, asteroids and bogus classes, with high accuracy (94\%) in a balanced test set. In order to find and analyze SN candidates selected by our classifier from the ZTF alert stream, we designed and deployed a visualization tool called SN Hunter, where relevant information about each possible SN is displayed for the experts to choose among candidates…
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