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Author (up) 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 2021 Publication Astronomical Journal Abbreviated Journal Astron. J.
Volume 161 Issue 5 Pages 242
Keywords
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.
Address
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
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Author (up) Sanchez-Saez, P.; Reyes, I.; Valenzuela, C.; Forster, F.; Eyheramendy, S.; Elorrieta, F.; Bauer, FE.; Cabrera-Vives, G.; Estevez, PA.; Catelan, M.; Pignata, G.; Huijse, P.; De Cicco, D.; Arevalo, P.; Carrasco-Davis, R.; Abril, J.; Kurtev, R.; Borissova, J.; Arredondo, J.; Castillo-Navarrete, E.; Rodriguez, D.; Ruz-Mieres, D.; Moya, A.; Sabatini-Gacitua, L.; Sepulveda-Cobo, C.; Camacho-Iniguez, E.
Title Alert Classification for the ALeRCE Broker System: The Light Curve Classifier Type
Year 2021 Publication Astronomical Journal Abbreviated Journal Astron. J.
Volume 161 Issue 3 Pages 141
Keywords Active galaxies; Astronomy data analysis; Variable stars; Supernovae; Surveys
Abstract We present the first version of the Automatic Learning for the Rapid Classification of Events (ALeRCE) 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 among 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 to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and Gaia DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with >= 6 g-band or >= 6 r-band detections in ZTF (868,371 sources as of 2020 June 9), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the ALeRCE Explorer website (http://alerce.online)..
Address
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:000620974000001 Approved
Call Number UAI @ alexi.delcanto @ Serial 1347
Permanent link to this record