Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
P. S\'anchez-S\'aez, I. Reyes, C. Valenzuela, F. F\"orster, S., Eyheramendy, F. Elorrieta, F. E. Bauer, G. Cabrera-Vives, P. A. Est\'evez, M., Catelan, G. Pignata, P. Huijse, D. De Cicco, P. Ar\'evalo, R. Carrasco-Davis,, J. Abril, R. Kurtev, J. Borissova, J. Arredondo

TL;DR
This paper introduces the first version of the ALeRCE light curve classifier that processes ZTF alert data to classify various types of variable and transient sources using a hierarchical Random Forest approach.
Contribution
It presents a novel hierarchical classification system for multiple classes of stochastic, periodic, and transient sources using real ZTF data and a comprehensive labeled dataset.
Findings
High precision and recall at top classification level (0.96 and 0.99).
Moderate precision and recall at detailed class level (0.57 and 0.76).
Classifies over 868,000 sources with daily updates.
Abstract
We present the first version of the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream, and colors obtained from AllWISE and ZTF photometry. We apply a Balanced Random Forest algorithm with a two-level scheme, where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes, amongst 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition…
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