Year: 2025
Type: article
Abstract: Context. ALeRCE (Automatic Learning for the Rapid Classification of Events) is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory, and classifying objects using a broad taxonomy. The ALeRCE light curve classifier is a balanced random forest (BRF) algorithm with a two-level scheme that uses variability features computed from the ZTF alert stream, and colors obtained from AllWISE and ZTF photometry. Aims. This work develops an updated version of the ALeRCE broker light curve classifier that includes tidal disruption events (TDEs) as a new subclass. For this purpose we incorporated 24 new features, notably including the distance to the nearest source detected in ZTF science images and a parametric model of the power-law decay for transients. We also expanded the labeled set to include 219 792 spectroscopically classified sources, including 60 TDEs. Methods. To effectively integrate TDEs into the ALeRCE’s taxonomy, we identified specific characteristics that set them apart from other transient classes, such as their central position in a galaxy, the typical decay pattern displayed when fully disrupted, and the lack of color variability after disruption. Based on these attributes, we developed features to distinguish TDEs from other transient events. Results. The modified classifier can distinguish between a broad range of classes with a better performance compared to the previous version and it can integate the TDE class achieving 91% recall, also identifying a large number of potential TDE candidates in ZTF alert stream unlabeled data.
Authors: M. Pavez-Herrera, P. Sánchez-Sáez, L. Hernández-García, F. E. Bauer, F. Förster, M. Catelan, A. Muñoz Arancibia, C. Ricci, I. Reyes-Jainaga, A. Bayo, P. Huijse, G. Cabrera-Vives
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: INSTITUTO MILENIO DE ASTROFISICA, EXTREME SUPERMASSIVE BLACK HOLE ACCRETION, VARIABILITY STUDIES OF AGN IN THE ERA OF LARGE SURVEYS, AN UNPRECEDENTED CENSUS OF BLACK HOLE ACTIVITY IN THE LSST ERA, MACHINE LEARNING TOOLS FOR A MULTI-MESSENGER ASTRONOMICAL ALERT BROKER SYSTEM, G57481727, STELLAR ASTROPHYSICS IN THE GAIA-LSST ERA, SPECTRO-TEMPORAL STUDIES OF AGN POPULATIONS, CENTER FOR MATHEMATICAL MODELING (CMM), CENTRO DE ASTROFISICA Y TECNOLOGIAS AFINES (CATA), NOVEL DEEP LEARNING ARCHITECTURES FOR ASTRONOMICAL TIME SERIES
OpenAlex ID: W6959454122
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