|   | 
Author Forster, F.; Cabrera-Vives, G.; Castillo-Navarrete, E.; Estevez, PA.; Sanchez-Saez, P.; Arredondo, J.; Bauer, FE.; Carrasco-Davis, R.; Catelan, M.; Elorrieta, F.; Eyheramendy, S.; Huijse, P.; Pignata, G.; Reyes, E.; Reyes, I.; Rodriguez-Mancini, D.; Ruz-Mieres, D.; Valenzuela, C.; Alvarez-Maldonado, I.; Astorga, N.; Borissova, J.; Clocchiatti, A.; De Cicco, D.; Donoso-Oliva, C.; Hernandez-Garcia, L.; Graham, MJ.; Jordan, A.; Kurtev, R.; Mahabal, A.; Maureira, JC.; Munoz-Arancibia, A.; Molina-Ferreiro, R.; Moya, A.; Palma, W.; Perez-Carrasco, M.; Protopapas, P.; Romero, M.; Sabatini-Gacitua, L.; Sanchez, A.; San Martin, J.; Sepulveda-Cobo, C.; Vera, E.; Vergara, JR.
Title The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker Type
Year (down) 2021 Publication Astronomical Journal Abbreviated Journal Astron. J.
Volume 161 Issue 5 Pages 242
Abstract We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self-consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean-led broker run by an interdisciplinary team of astronomers and engineers working to become intermediaries between survey and follow-up facilities. ALeRCE uses a pipeline that includes the real-time ingestion, aggregation, cross-matching, machine-learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp-based classifier, designed for rapid classification, and a light curve-based classifier, which uses the multiband flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools, and services, which are made public for the community (see ). Since we began operating our real-time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real-time processing of 1.5 x 10(8) alerts, the stamp classification of 3.4 x 10(7) objects, the light-curve classification of 1.1 x 10(6) objects, the report of 6162 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead in going from a single stream of alerts such as ZTF to a multistream ecosystem dominated by LSST.
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0004-6256 ISBN Medium
Area Expedition Conference
Notes WOS:000645164000001 Approved
Call Number UAI @ alexi.delcanto @ Serial 1380
Permanent link to this record