Browsing by Author "Astorga, Nicolas"

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    Alert Classification for the ALeRCE Broker System: The Anomaly Detector
    (IOP Publishing Ltd, 2023) Pérez-Carrasco, Manuel; Cabrera-Vives, Guillermo; Hernández-García, Lorena; Forster, F.; Sanchez-Saez, Paula; Muñoz Arancibia, Alejandra M.; Arredondo, Javier; Astorga, Nicolas; Bauer, Franz Erik; Bayo, Amelia; Catelan, Marcio; Dastidar, Raya; Estevez, P. A.; Lira, Paulina; Pignata, Giuliano
    Astronomical broker systems, such as Automatic Learning for the Rapid Classification of Events (ALeRCE), are currently analyzing hundreds of thousands of alerts per night, opening up an opportunity to automatically detect anomalous unknown sources. In this work, we present the ALeRCE anomaly detector, composed of three outlier detection algorithms that aim to find transient, periodic, and stochastic anomalous sources within the Zwicky Transient Facility data stream. Our experimental framework consists of cross-validating six anomaly detection algorithms for each of these three classes using the ALeRCE light-curve features. Following the ALeRCE taxonomy, we consider four transient subclasses, five stochastic subclasses, and six periodic subclasses. We evaluate each algorithm by considering each subclass as the anomaly class. For transient and periodic sources the best performance is obtained by a modified version of the deep support vector data description neural network, while for stochastic sources the best results are obtained by calculating the reconstruction error of an autoencoder neural network. Including a visual inspection step for the 10 most promising candidates for each of the 15 ALeRCE subclasses, we detect 31 bogus candidates (i.e., those with photometry or processing issues) and seven potential astrophysical outliers that require follow-up observations for further analysis.
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    Multiscale Stamps for Real-time Classification of Alert Streams
    (IOP Publishing Ltd., 2023) Reyes-Jainaga, Ignacio; Forster, Francisco; Muñoz Arancibia, Alejandra M.; Cabrera-Vives, Guillermo; Bayo, Amelia; Bauer, Franz Erik; Arredondo, Javier; Reyes, Esteban; Pignata, Giuliano; Mourao, A. M.; Silva-Farfan, Javier; Galbany, Lluis; Alvarez, Alex; Astorga, Nicolas; Castellanos, Pablo; Gallardo, Pedro; Moya, Alberto; Rodriguez, Diego
    In recent years, automatic classifiers of image cutouts (also called "stamps") have been shown to be key for fast supernova discovery. The Vera C. Rubin Observatory will distribute about ten million alerts with their respective stamps each night, enabling the discovery of approximately one million supernovae each year. A growing source of confusion for these classifiers is the presence of satellite glints, sequences of point-like sources produced by rotating satellites or debris. The currently planned Rubin stamps will have a size smaller than the typical separation between these point sources. Thus, a larger field-of-view stamp could enable the automatic identification of these sources. However, the distribution of larger stamps would be limited by network bandwidth restrictions. We evaluate the impact of using image stamps of different angular sizes and resolutions for the fast classification of events (active galactic nuclei, asteroids, bogus, satellites, supernovae, and variable stars), using data from the Zwicky Transient Facility. We compare four scenarios: three with the same number of pixels (small field of view with high resolution, large field of view with low resolution, and a multiscale proposal) and a scenario with the full stamp that has a larger field of view and higher resolution. Compared to small field-of-view stamps, our multiscale strategy reduces misclassifications of satellites as asteroids or supernovae, performing on par with high-resolution stamps that are 15 times heavier. We encourage Rubin and its Science Collaborations to consider the benefits of implementing multiscale stamps as a possible update to the alert specification.