Examinando por Autor "Graham, M. J."

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    Ítem
    La Serena School for Data Science: multidisciplinary hands-on education in the era of big data
    (Cambridge University Press, 2021) Bayo, Amelia; Graham, M. J.; Norman, D.; Cerda, M.; Damke, G.; Zenteno, A.; Ibarlucea, C.
    La Serena School for Data Science is a multidisciplinary program with six editions so far and a constant format: during 10-14 days, a group of ∼30 students (15 from the US, 15 from Chile and 1-3 from Caribbean countries) and ∼9 faculty gather in La Serena (Chile) to complete an intensive program in Data Science with emphasis in applications to astronomy and bio-sciences. The students attend theoretical and hands-on sessions, and, since early on, they work in multidisciplinary groups with their “mentors” (from the faculty) on real data science problems. The SOC and LOC of the school have developed student selection guidelines to maximize diversity. The program is very successful as proven by the high over-subscription rate (factor 5-8) and the plethora of positive testimony, not only from alumni, but also from current and former faculty that keep in contact with them.
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    Ítem
    The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker
    (American Astronomical Society (Aas), 2021) Förster, F.; Cabrera-Vives, G.; Castillo-Navarrete, E.; Estévez, P. A.; Sánchez-Sáez, P.; Arredondo, J.; Bauer, F. E.; Carrasco-Davis, R.; Catelan, M.; Elorrieta, F.; Eyheramendy, S.; Huijse, P.; Pignata, G.; Reyes, E.; Reyes, I.; Rodríguez-Mancini, D.; Ruz-Mieres, D.; Valenzuela, C.; Álvarez-Maldonado, I.; Astorga, N.; Borissova, Jura; Clocchiatti, A.; De Cicco, D.; Donoso-Oliva, C.; Hernández-García, L.; Graham, M. J.; Jordán, A.; Kurtev, R.; Mahabal, A.; Maureira, J. C.; Muñoz-Arancibia, A.; Molina-Ferreiro, R.; Moya, A.; Palma, W.; Pérez-Carrasco, M.; Protopapas, P.; Romero, M.; Sabatini-Gacitua, L.; Sánchez, A.; San Martín, J.; Sepúlveda-Cobo, C.; Vera, E.; Vergara, J. R.
    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 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.