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

Förster, F. and Cabrera-Vives, G. and Castillo-Navarrete, E. and Estévez, P. A. and Sánchez-Sáez, P. and Arredondo, J. and Bauer, F. E. and Carrasco-Davis, R. and Catelan, M. and Elorrieta, F. and Eyheramendy, S. and Huijse, P. and Pignata, G. and Reyes, E. and Reyes, I. and Rodríguez-Mancini, D. and Ruz-Mieres, D. and Valenzuela, C. and Álvarez-Maldonado, I. and Astorga, N. and Borissova, J. and Clocchiatti, A. and De Cicco, D. and Donoso-Oliva, C. and Hernández-García, L. and Graham, M. J. and Jordán, A. and Kurtev, R. and Mahabal, A. and Maureira, J. C. and Muñoz-Arancibia, A. and Molina-Ferreiro, R. and Moya, A. and Palma, W. and Pérez-Carrasco, M. and Protopapas, P. and Romero-Wolf, M. and Sabatini-Gacitua, L. and Sánchez, A. and San Martín, J. and Sepúlveda-Cobo, C. and Vera, E. and Vergara, J. R. (2021) The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker. Astronomical Journal, 161 (5). Art. No. 242. ISSN 0004-6256.

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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 × 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.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Förster, F.0000-0003-3459-2270
Cabrera-Vives, G.0000-0002-2720-7218
Estévez, P. A.0000-0001-9164-4722
Sánchez-Sáez, P.0000-0003-0820-4692
Arredondo, J.0000-0002-2045-7134
Bauer, F. E.0000-0002-8686-8737
Carrasco-Davis, R.0000-0003-4673-8791
Catelan, M.0000-0001-6003-8877
Elorrieta, F.0000-0002-1835-7433
Eyheramendy, S.0000-0003-4723-9660
Huijse, P.0000-0003-3541-1697
Pignata, G.0000-0003-0006-0188
Reyes, E.0000-0003-3627-0216
Reyes, I.0000-0003-3627-0216
Ruz-Mieres, D.0000-0002-1292-2374
Valenzuela, C.0000-0001-5306-1390
Borissova, J.0000-0002-5936-7718
Clocchiatti, A.0000-0003-3068-4258
Hernández-García, L.0000-0002-8606-6961
Graham, M. J.0000-0002-3168-0139
Jordán, A.0000-0002-5389-3944
Kurtev, R.0000-0002-9740-9974
Mahabal, A.0000-0003-2242-0244
Maureira, J. C.0000-0002-7458-6142
Muñoz-Arancibia, A.0000-0002-8722-516X
Moya, A.0000-0002-7003-5087
Pérez-Carrasco, M.0000-0003-4644-8698
Romero-Wolf, M.0000-0002-8024-7198
Sánchez, A.0000-0003-0820-4692
Vergara, J. R.0000-0001-6699-4181
Additional Information:© 2021. The American Astronomical Society. Received 2020 June 29; revised 2021 February 22; accepted 2021 February 25; published 2021 April 27. This work was funded by ANID—Millennium Science Initiative Program—ICN12_009 awarded to the Millennium Institute of Astrophysics MAS (A.C., A.M., A.M.A., A.S., C.S.C., C.D.O., C.V., D.D.C., D.R.Ma., D.R.Mi., E.C.N., E.R., F.E., F.E.B., F.F., G.C.V., G.P., I.A.M., I.R., J.A., J.B., J.R.V., L.H.G., L.S.G., M.C., M.P.C., N.A., P.A.E., P.H., P.S.S., R.C.D., S.E., R.K., and W.P.), and National Agency for Research and Development (ANID) grants: Basal Center for Mathematical Modeling grant CMM ANID PIA AFB170001 (A.M., A.M.A., C.V., C.S.C., E.C.N., E.V., D.R.Ma., D.R.Mi., F.F., I.A.M., I.R., J.C.M., J.S.M., L.S.G., and P.A.E.); Centro de Astrofísica y Tecnologías Afines AFB-170002 (D.D.C., F.E.B., M.C., P.S.S., and A.C.); FONDECYT Regular Nos. 1200710 (F.F.), 1190818 (F.E.B.), 1200495 (F.E.B.), 1171273 (M.C.), 1201793 (G.P.), and 1171678 (P.A.E.); FONDECYT Iniciacion Nos. 11200590 (F.E.) and 11191130 (G.C.V.); FONDECYT Postdoctorado Nos. 3200250 (P.S.S.) and 3200222 (D.D.C.); Magister Nacional 2019 No. 22190947 (E.R.); and ANID infrastructure funds QUIMAL140003 and QUIMAL190012. We acknowledge support from REUNA Chile, which hosts and maintains some of our infrastructure. This work has been possible thanks to the use of AWS-U.Chile-NLHPC credits. This work was funded in part by project CORFO 10CEII-9157 Inria Chile. Powered@NLHPC: This research was partially supported by the supercomputing infrastructure of the NLHPC (ECM-02). This project was supported by the Competition for Research Regular Projects, year 2019, code LPR19-22, Universidad Tecnológica Metropolitana and the high-performance computing system of PIDi-UTEM (SCC-PIDi-UTEM—CONICYT—FONDEQUIP—EQM180180). Software: Aladin (Bonnarel et al. 2000), Apache ECharts, 36 Apache Kafka, 37 Apache Spark (Zaharia et al. 2016), ASTROIDE (Brahem et al. 2018), Astropy (Astropy Collaboration et al. 2013), catsHTM (Soumagnac & Ofek 2018), Dask (Rocklin 2015), FATS (Nun et al. 2017), Grafana, 38 Imbalanced-learn (Lemaître et al. 2017), ipyladin (Boch & Desroziers 2020), Jupyter (Kluyver et al. 2016), Keras (Gulli et al. 2017), Matplotlib (Hunter 2007), NED (Steer et al. 2017), P4J (Huijse et al. 2018), Pandas (McKinney et al. 2010), Prometheus, 39 Python (Van Rossum & Drake 1995), scikit-learn (Pedregosa et al. 2011), Simbad-CDS (Wenger et al. 2000), Tensorflow (Abadi et al. 2016), Vue, 40 Vuetify, 41 PostgreSQL, 42 XGBoost. 43
Funding AgencyGrant Number
Millennium Institute of Astrophysics (MAS)ICN12_009
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)1200710
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)1190818
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)1200495
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)1171273
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)1201793
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)1171678
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)11200590
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)11191130
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)3200250
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)3200222
Magster Nacional 201922190947
Agencia Nacional de Investigación y Desarrollo (Chile)QUIMAL140003
Agencia Nacional de Investigación y Desarrollo (Chile)QUIMAL190012
Centre Inria ChileCORFO 10CEII-9157
Subject Keywords:Supernovae; Variable stars; Active galactic nuclei; Astroinformatics; Surveys; Classification; Astrostatistics; Convolutional neural networks; Random Forests; Cloud computing; Distributed computing; Small solar system bodies
Issue or Number:5
Classification Code:Unified Astronomy Thesaurus concepts: Supernovae (1668); Variable stars (1761); Active galactic nuclei (16); Astroinformatics (78); Surveys (1671); Classification (1907); Astrostatistics (1882); Convolutional neural networks (1938); Random Forests (1935);
Record Number:CaltechAUTHORS:20210503-115703881
Persistent URL:
Official Citation:F. Förster et al 2021 AJ 161 242
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108930
Deposited By: George Porter
Deposited On:06 May 2021 14:36
Last Modified:06 May 2021 14:36

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