Examinando por Autor "Donoso-Oliva, C."

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  • Miniatura
    Ítem
    Searching for Changing-state AGNs in Massive Data Sets. I. Applying Deep Learning and Anomaly-detection Techniques to Find AGNs with Anomalous Variability Behaviors
    (American Astronomical Society (Aas), 2021) Sánchez-Sáez, P.; Lira, H.; Martí, L.; Sánchez-Pi, N.; Arredondo, J.; Bauer, F. E.; Bayo, Amelia; Cabrera-Vives, G.; Donoso-Oliva, C.; Estévez, P. A.; Eyheramendy, S.; Förster, F.; Hernández-García, L.; Muñoz Arancibia, A. M.; Pérez-Carrasco, M.; Sepúlveda, M.; Vergara, J. R.
    The classic classification scheme for active galactic nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs. The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGNs right in the act of changing state. Here we present an anomaly-detection technique designed to identify AGN light curves with anomalous behaviors in massive data sets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive data sets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behavior of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm that is an anomaly detector for a "one class" kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.
  • Miniatura
    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.