The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker

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dc.contributor.authorFörster, F.
dc.contributor.authorCabrera-Vives, G.
dc.contributor.authorCastillo-Navarrete, E.
dc.contributor.authorEstévez, P. A.
dc.contributor.authorSánchez-Sáez, P.
dc.contributor.authorArredondo, J.
dc.contributor.authorBauer, F. E.
dc.contributor.authorCarrasco-Davis, R.
dc.contributor.authorCatelan, M.
dc.contributor.authorElorrieta, F.
dc.contributor.authorEyheramendy, S.
dc.contributor.authorHuijse, P.
dc.contributor.authorPignata, G.
dc.contributor.authorReyes, E.
dc.contributor.authorReyes, I.
dc.contributor.authorRodríguez-Mancini, D.
dc.contributor.authorRuz-Mieres, D.
dc.contributor.authorValenzuela, C.
dc.contributor.authorÁlvarez-Maldonado, I.
dc.contributor.authorAstorga, N.
dc.contributor.authorBorissova, Jura
dc.contributor.authorClocchiatti, A.
dc.contributor.authorDe Cicco, D.
dc.contributor.authorDonoso-Oliva, C.
dc.contributor.authorHernández-García, L.
dc.contributor.authorGraham, M. J.
dc.contributor.authorJordán, A.
dc.contributor.authorKurtev, R.
dc.contributor.authorMahabal, A.
dc.contributor.authorMaureira, J. C.
dc.contributor.authorMuñoz-Arancibia, A.
dc.contributor.authorMolina-Ferreiro, R.
dc.contributor.authorMoya, A.
dc.contributor.authorPalma, W.
dc.contributor.authorPérez-Carrasco, M.
dc.contributor.authorProtopapas, P.
dc.contributor.authorRomero, M.
dc.contributor.authorSabatini-Gacitua, L.
dc.contributor.authorSánchez, A.
dc.contributor.authorSan Martín, J.
dc.contributor.authorSepúlveda-Cobo, C.
dc.contributor.authorVera, E.
dc.contributor.authorVergara, J. R.
dc.date.accessioned2022-11-30T02:46:20Z
dc.date.available2022-11-30T02:46:20Z
dc.date.issued2021
dc.description.abstractWe 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 https://alerce.science). 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 × 108 alerts, the stamp classification of 3.4 × 107 objects, the light-curve classification of 1.1 × 106 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.en_ES
dc.facultadFacultad de Cienciasen_ES
dc.file.nameFörster_Aut2021.pdf
dc.identifier.citationF. Förster et al 2021 AJ 161 242en_ES
dc.identifier.doihttps://doi.org/10.3847/1538-3881/abe9bc
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7329
dc.languageen
dc.publisherAmerican Astronomical Society (Aas)
dc.rights© 2021. The American Astronomical Society. All rights reserved.
dc.sourceThe Astronomical Journal
dc.subjectSUPERNOVAEen_ES
dc.subjectVARIABLE STARSen_ES
dc.subjectACTIVE GALACTIC NUCLEIen_ES
dc.subjectASTROINFORMATICS; SURVEYSen_ES
dc.subjectCLASSIFICATIONen_ES
dc.subjectASTROSTATISTICSen_ES
dc.subjectCONVOLUTIONAL NEURAL NETWORKSen_ES
dc.subjectRANDOM FORESTSen_ES
dc.subjectCLOUD COMPUTINGen_ES
dc.subjectDISTRIBUTED COMPUTINGen_ES
dc.subjectSMALL SOLAR SYSTEM BODIESen_ES
dc.titleThe Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker
dc.typeArticulo
uv.departamentoInstituto de Fisica y Astronomia

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