Browsing by Author "Forster, F."

Now showing 1 - 7 of 7
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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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    AT 2021hdr: A candidate tidal disruption of a gas cloud by a binary super massive black hole system
    (EDP Sciences, 2024) Hernández-García, L.; Muñoz-Arancibia, A. M.; Lira, P.; Bruni, G.; Cuadra, J.; Arévalo, P.; Sánchez-Sáez, P.; Bernal, S.; Bauer, Franz Erik; Catelan, Márcio; Panessa, F.; Pávez-Herrera, M.; Ricci, C.; Reyes-Jainaga, I.; Ailawadhi, B.; Chavushyan, V.; Dastidar, R.; Deconto-Machado, A.; Forster, F.; Gangopadhyay, A.; García-Pérez, A.; Márquez, I.; Masegosa, J.; Misra, K.; Patiño-Alvarez, V. M.; Puig-Subira, M.; Rodi, J.; Singh, M.
    With a growing number of facilities able to monitor the entire sky and produce light curves with a cadence of days, in recent years there has been an increased rate of detection of sources whose variability deviates from standard behavior, revealing a variety of exotic nuclear transients. The aim of the present study is to disentangle the nature of the transient AT 2021hdr, whose optical light curve used to be consistent with a classic Seyfert 1 nucleus, which was also confirmed by its optical spectrum and high-energy properties. From late 2021, AT 2021hdr started to present sudden brightening episodes in the form of oscillating peaks in the Zwicky Transient Facility (ZTF) alert stream, and the same shape is observed in X-rays and UV from Swift data. The oscillations occur every ≈60-90 days with amplitudes of ≈0.2 mag in the g and r bands. Very Long Baseline Array (VLBA) observations show no radio emission at milliarcseconds scale. It is argued that these findings are inconsistent with a standard tidal disruption event (TDE), a binary supermassive black hole (BSMBH), or a changing-look active galactic nucleus (AGN); neither does this object resemble previous observed AGN flares, and disk or jet instabilities are an unlikely scenario. Here, we propose that the behavior of AT 2021hdr might be due to the tidal disruption of a gas cloud by a BSMBH. In this scenario, we estimate that the putative binary has a separation of ≈0.83 mpc and would merge in ≈7 × 104 years. This galaxy is located at 9 kpc from a companion galaxy, and in this work we report this merger for the first time. The oscillations are not related to the companion galaxy.
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    Multiwavelength monitoring of the nucleus in PBC?J2333.9-2343: the giant radio galaxy with a blazar-like core
    (2023) Hernandez-Garcia, L.; Panessa, F.; Bruni, G.; Bassani, L.; Arevalo, P.; Patino-Alvarez, V. M.; Tramacere, A.; Lira, P.; Sanchez-Saez, P.; Bauer, F. E.; Chavushyan, V; Carraro, R.; Forster, F.; Arancibia, A. M. Munoz; Ubertini, P.
    PBC J2333.9-2343 is a giant radio galaxy at z = 0.047 with a bright central core associated to a blazar nucleus. If the nuclear blazar jet is a new phase of the jet activity, then the small orientation angle suggests a dramatic change of the jet direction. We present observations obtained between 2018 September and 2019 January (cadence larger than three days) with Effeslberg, SMARTS-1.3m, ZTF, ATLAS, Swift, and Fermi-LAT, and between 2019 April and 2019 July (daily cadence) with SMARTS-1.3 m and ATLAS. Large (>2 x) flux increases are observed on time-scales shorter than a month, which are interpreted as flaring events. The cross correlation between the SMARTS-1.3 m monitoring in the NIR and optical shows that these data do not show significant time lag within the measured errors. A comparison of the optical variability properties between non-blazars and blazars AGN shows that PBC J2333.9-2343 has properties more comparable to the latter. The SED of the nucleus shows two peaks, that were fitted with a one-zone leptonic model. Our data and modelling show that the high energy peak is dominated by External Compton from the dusty torus with mild contribution from Inverse Compton from the jet. The derived jet angle of 3 deg is also typical of a blazar. Therefore, we confirm the presence of a blazar-like core in the centre of this giant radio galaxy, likely a Flat Spectrum Radio Quasar with peculiar properties.
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    Searching for Changing-state AGNs in Massive Data Sets. I. Applying Deep Learning and Anomaly-detection Techniques to Find AGNs with Anomalous Variability Behaviors
    (2021) Sanchez-Saez, P.; Lira, H.; Marti, L.; Sanchez-Pi, N.; Arredondo, J.; Bauer, F. E.; Bayo, A.; Cabrera-Vives, G.; Donoso-Oliva, C.; Estevez, P. A.; Eyheramendy, S.; Forster, F.; Hernandez-Garcia, L.; Arancibia, A. M. Munoz; Perez-Carrasco, M.; Sepulveda, 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.
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    The delay of shock breakout due to circumstellar material evident in most type II supernovae
    (2018) Forster, F.; Moriya, T. J.; Maureira, J. C.; Anderson, J. P.; Blinnikov, S.; Bufano, F.; Cabrera Vives, G.; Clocchiatti, Alejandro; De Jaeger, T.; Estevez, P. A.; Galbany, L.; González -Gaitán, S.; Grafener, G.; Hamuy, M.; Hsiao, E. Y.; Huentelemu, P.; Huijse, P.; Kuncarayakti, H.; Martínez, J.; Medina, G.; Olivares, F.; Pignata, Giuliano; Razza, A.; Reyes, I.; San Martín, J.; Smith, R. C.; Vera, E.; Vivas, A. K.; Postigo, A. D.; Yoon, S. C.; Ashall, C.; Fraser, M.; Gal-Yam, A.; Kankare, E.; Le Guillou, L.; Mazzali, P. A.; Walton, N. A.; Young, D. R.
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    The Emergence of the Infrared Transient VVV-WIT-06
    (2017) Minniti, D.; Saito, R. K.; Forster, F.; Pignata, Giuliano; Ivanov, V. D.; Lucas, P. W.; Beamin, J. C.; Borissova, J.; Catelan, Márcio; Hempel, Maren
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    Transient Classification Report for 2020-12-01
    (2020) Dodin, A.; Tsvetkov, D.; Shatski, N.; Belinski, A.; Galbany, L.; Munoz-Arancibia, A.; Forster, F.; Bauer, F. E.; Hernandez-Garcia, L.; Pignata, G.; Camacho, E.; Silva-Farfan, J.; Mourao, A.; Arredondo, J.; Cabrera-Vives, G.; Carrasco-Davis, R.; Estevez, P. A.; Huijse, P.; Reyes, E.; Reyes, I.; Sanchez-Saez, P.; Valenzuela, C.; Castillo, E.; Ruz-Mieres, D.; Rodriguez-Mancini, D.; Catelan, Marcio; Eyheramendy, S.; Graham, M. J.
    F. Forster, F.E. Bauer, G. Pignata, J. Arredondo, G. Cabrera-Vives, R. Carrasco-Davis, P.A. Estevez, P. Huijse, E. Reyes, I. Reyes, P. Sanchez-Saez, C. Valenzuela, E. Castillo, D. Ruz-Mieres, D. Rodriguez-Mancini, F.E. Bauer, M. Catelan, S. Eyheramendy, M.J. Graham on behalf of the ALeRCE broker report/s the discovery of a new astronomical transient.