Browsing by Author "Bayo, Amelia"
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- ItemAlert 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, GiulianoAstronomical 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.
- ItemDeep Learning Identification of Galaxy Hosts in Transients (DELIGHT)(2022) Forster, Francisco; Muñoz Arancibia, Alejandra M.; Reyes, Ignacio; Gagliano, Alexander; Britt, Dylan J.; Cuellar-Carrillo, Sara; Figueroa-Tapia, Felipe; Polzin, Ava; Yousef, Yara; Arredondo, Javier; Rodríguez-Mancini, Diego; Correa-Orellana, Javier; Bayo, Amelia; Bauer, Franz E.; Catelan, Márcio; Cabrera-Vives, Guillermo; Dastidar, Raya; Estévez, Pablo A.; Pignata, Giuliano; Hernández-Garcia, Lorena; Huijse, Pablo; Reyes, Esteban; Sánchez-Sáez, Paula; Ramírez, Mauricio; Grandón, Daniela; Pineda-García, Jonathan; Chabour-Barra, Francisca; Silva-Farfán, JavierThe Deep Learning Identification of Galaxy Hosts in Transients (DELIGHT, Förster et al. 2022, submitted) is a library created by the ALeRCE broker to automatically identify host galaxies of transient candidates using multi-resolution images and a convolutional neural network (you can test it with our example notebook, that you can run in Colab). The initial idea for DELIGHT started as a project proposed for the La Serena School of Data Science in 2021. You can install it using pip install astro-delight, but we recommend cloning this repository and pip install . from there. The library has a class with several methods that allow you to get the most likely host coordinates starting from given transient coordinates. In order to do this, the delight object needs a list of object identifiers and coordinates (oid, ra, dec). With this information, it downloads PanSTARRS images centered around the position of the transients (2 arcmin x 2 arcmin), gets their WCS solutions, creates the multi-resolution images, does some extra preprocessing of the data, and finally predicts the position of the hosts using a multi-resolution image and a convolutional neural network. It can also estimate the host's semi-major axis if requested taking advantage of the multi-resolution images. Note that DELIGHT's prediction time is currently dominated by the time to download PanSTARRS images using the panstamps service. In the future, we expect that there will be services that directly provide multi-resolution images, which should be more lightweight with no significant loss of information. Once these images are obtained, the processing times are only milliseconds per host. If you cannot install some of the dependencies, e.g. tensorflow, you can try running DELIGHT directly from Google Colab (test in this link). Github link: https://github.com/fforster/delight PyPi link: https://pypi.org/project/astro-delight/...
- ItemDELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multiresolution Images(2022) Förster, Francisco; Muñoz Arancibia, Alejandra M.; Reyes-Jainaga, Ignacio; Gagliano, Alexander; Britt, Dylan; Cuellar-Carrillo, Sara; Figueroa-Tapia, Felipe; Polzin, Ava; Yousef, Yara; Arredondo, Javier; Rodríguez-Mancini, Diego; Correa-Orellana, Javier; Bayo, Amelia; Bauer, Franz E.; Catelan, Márcio; Cabrera-Vives, Guillermo; Dastidar, Raya; Estévez, Pablo A.; Pignata, Giuliano; Hernández-García, Lorena; Huijse, Pablo; Reyes, Esteban; Sánchez-Sáez, Paula; Ramírez, Mauricio; Grandón, Daniela; Pineda-García, Jonathan; Chabour-Barra, Francisca; Silva-Farfán, JavierWe present DELIGHT, or Deep Learning Identification of Galaxy Hosts of Transients, a new algorithm designed to automatically and in real time identify the host galaxies of extragalactic transients. The proposed algorithm receives as input compact, multiresolution images centered at the position of a transient candidate and outputs two-dimensional offset vectors that connect the transient with the center of its predicted host. The multiresolution input consists of a set of images with the same number of pixels, but with progressively larger pixel sizes and fields of view. A sample of 16,791 galaxies visually identified by the Automatic Learning for the Rapid Classification of Events broker team was used to train a convolutional neural network regression model. We show that this method is able to correctly identify both relatively large (10″ < r < 60″) and small (r ≤ 10″) apparent size host galaxies using much less information (32 kB) than with a large, single-resolution image (920 kB). The proposed method has fewer catastrophic errors in recovering the position and is more complete and has less contamination (<0.86%) recovering the crossmatched redshift than other state-of-the-art methods. The more efficient representation provided by multiresolution input images could allow for the identification of transient host galaxies in real time, if adopted in alert streams from new generation of large -etendue telescopes such as the Vera C. Rubin Observatory....
- ItemMulti-Class Deep SVDD: Anomaly Detection Approach in Astronomy with Distinct Inlier Categories(Cornell Univ., 2023) Perez-Carrasco, Manuel; Bauer, Franz Erik; Hernandez-Garcia, Lorena; Forster, Francisco; Sanchez-Saez, Paula; Arancibia, Alejandra Munoz; Astorga, Nicolas; Bayo, Amelia; Cadiz-Leyton, Martina; Catelan, Marcio; Estevez, P. A.With the increasing volume of astronomical data generated by modern survey telescopes, automated pipelines and machine learning techniques have become crucial for analyzing and extracting knowledge from these datasets. Anomaly detection, i.e. the task of identifying irregular or unexpected patterns in the data, is a complex challenge in astronomy. In this paper, we propose Multi-Class Deep Support Vector Data Description (MCDSVDD), an extension of the state-of-the-art anomaly detection algorithm One-Class Deep SVDD, specifically designed to handle different inlier categories with distinct data distributions. MCDSVDD uses a neural network to map the data into hyperspheres, where each hypersphere represents a specific inlier category. The distance of each sample from the centers of these hyperspheres determines the anomaly score. We evaluate the effectiveness of MCDSVDD by comparing its performance with several anomaly detection algorithms on a large dataset of astronomical light-curves obtained from the Zwicky Transient Facility. Our results demonstrate the efficacy of MCDSVDD in detecting anomalous sources while leveraging the presence of different inlier categories. The code and the data needed to reproduce our results are publicly available at
- ItemMultiscale 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, DiegoIn 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.
- ItemStellar Astrophysics and Exoplanet Science with the Maunakea Spectroscopic Explorer (MSE)(2019) Bergemann, Maria; Huber, Daniel; Adibekyan, Vardan; Angelou, George; Barría, Daniela; Beers, Timothy C.; Beck, Paul G.; Bellinger, Earl P.; Bestenlehner, Joachim M.; Bitsch, Bertram; Burgasser, Adam; Buzasi, Derek; Cassisi, Santi; Catelan, Marcio; Escorza, Ana; Fleming, Scott W.; Gänsicke, Boris T.; Gandolfi, Davide; García, Rafael A.; Gieles, Mark; Karakas, Amanda; Lebreton, Yveline; Lodieu, Nicolas; Melis, Carl; Merle, Thibault; Mészáros, Szabolcs; Miglio, Andrea; Molaverdikhani, Karan; Monier, Richard; Morel, Thierry; Neilson, Hilding R.; Oshagh, Mahmoudreza; Rybizki, Jan; Serenelli, Aldo; Smiljanic, Rodolfo; Szabó, Gyula M.; Toonen, Silvia; Tremblay, Pier-Emmanuel; Valentini, Marica; Van Eck, Sophie; Zwintz, Konstanze; Bayo, Amelia; Cami, Jan; Casagrande, Luca; Gabdeev, Maksim; Gaulme, Patrick; Guiglion, Guillaume; Handler, Gerald; Hillenbrand, Lynne; Yildiz, Mutlu; Marley, Mark; Mosser, Benoit; Price-Whelan, Adrian M.; Prsa, Andrej; Hernández Santisteban, Juan V.; Silva Aguirre, Victor; Sobeck, Jennifer; Stello, Dennis; Szabo, Robert; Tsantaki, Maria; Villaver, Eva; Wright, Nicholas J.; Xu, Siyi; Zhang, Huawei; Anguiano, Borja; Bedell, Megan; Chaplin, Bill; Collet, Remo; Kamath, Devika; Martell, Sarah; Sousa, Sérgio G.; Ting, Yuan-Sen; Venn, KimThe Maunakea Spectroscopic Explorer (MSE) is a planned 11.25-m aperture facility with a 1.5 square degree field of view that will be fully dedicated to multi-object spectroscopy. A rebirth of the 3.6m Canada-France-Hawaii Telescope on Maunakea, MSE will use 4332 fibers operating at three different resolving powers (R ~ 2500, 6000, 40000) across a wavelength range of 0.36-1.8mum, with dynamical fiber positioning that allows fibers to match the exposure times of individual objects. MSE will enable spectroscopic surveys with unprecedented scale and sensitivity by collecting millions of spectra per year down to limiting magnitudes of g ~ 20-24 mag, with a nominal velocity precision of ~100 m/s in high-resolution mode. This white paper describes science cases for stellar astrophysics and exoplanet science using MSE, including the discovery and atmospheric characterization of exoplanets and substellar objects, stellar physics with star clusters, asteroseismology of solar-like oscillators and opacity-driven pulsators, studies of stellar rotation, activity, and multiplicity, as well as the chemical characterization of AGB and extremely metal-poor stars....
- ItemThe most variable VVV sources: eruptive protostars, dipping giants in the Nuclear Disc and others(2023) Lucas, Phil; Smith, Leigh; Guo, Zhen; Contreras Peña, Carlos; Minniti, Dante; Miller, Niall; Alonso-Garcia, Javier; Catelan, Marcio; Borissova, J.; Saito, Roberto; Kurtev, Radostin; Navarro, M. G.; Morris, Calum; Muthu, Hariharan; Froebrich, Dirk; Ivanov, Valentin; Bayo, Amelia; Caratti, Alessio; Sanders, JasonWe have performed a comprehensive search of a VISTA Variables in the Via Lactea (VVV) data base of 9.5 yr light curves for variable sources with ΔKs ≥ 4 mag, aiming to provide a large sample of high amplitude eruptive young stellar objects (YSOs) and detect unusual or new types of infrared variable source. We find 222 variable or transient sources in the Galactic bulge and disc, most of which are new discoveries. The sample mainly comprises novae, YSOs, microlensing events, Long Period Variable stars (LPVs), and a few rare or unclassified sources. Additionally, we report the discovery of a significant population of aperiodic late-type giant stars suffering deep extinction events, strongly clustered in the Nuclear Disc of the Milky Way. We suggest that these are metal-rich stars in which radiatively driven mass loss has been enhanced by super-solar metallicity. Among the YSOs, 32/40 appear to be undergoing episodic accretion. Long-lasting YSO eruptions have a typical rise time of ∼2 yr, somewhat slower than the 6–12 month time-scale seen in the few historical events observed on the rise. The outburst durations are usually at least 5 yr, somewhat longer than many lower amplitude VVV events detected previously. The light curves are diverse in nature, suggesting that multiple types of disc instability may occur. Eight long-duration extinction events are seen wherein the YSO dims for a year or more, attributable to inner disc structure. One binary YSO in NGC 6530 displays periodic extinction events (P=59 d) similar to KH 15D.
- ItemYoung stellar objects in star-forming regions towards the galactic bulge(2024) Órdenes Huanca, Camila Constanza; Zoccali, Manuela; Cuadra, Jorge; Bayo, Amelia; Pontificia Universidad Católica de Chile. Instituto de AstrofísicaLa evolución de objetos estelares jóvenes está afectada por procesos físicos que originan cambios en el brillo de estas estrellas. Variaciones eruptivas e irregulares han sido observados en protoestrellas, los cuales se han relacionado a procesos de acreción. Por otro lado, estrellas de tipo T Tauri han demostrado ser intrínsecamente variables. Debido a los intensos campos magnéticos que poseen, desarrollan manchas oscuras en su superficie que, acopladas a la rotación de la estrella, introducen una variación periódica de brillo. Además, la presencia de discos puede generar variaciones del flujo debido a extinción variable o acreción. Éstas pueden provocar una disminución o un aumento del brillo, respectivamente. En este trabajo, hemos aprovechado las capacidades del VVVX survey para, en primer lugar, compilar catálogos de curvas de luz de estrellas jóvenes en la región de la nebulosa de la Laguna (M8) y NGC 6357. Dos sitios de formación estelar, situados hacia el Bulbo de la Vía Láctea, que contienen miles de estrellas en formación. Todas las estrellas de nuestros catálogos ya estaban clasificadas como miembros jóvenes de cada región en la literatura. Los datos presentados aquí se extienden a lo largo de un período de alrededor de ocho años, lo que nos proporciona un tiempo de seguimiento único para este tipo de estrellas en el infrarrojo, particularmente, en la banda 𝐾𝑠 . Esto último también permite sondear regiones más extintas de nuestra Galaxia, como NGC 6357. Cada curva de luz fue clasificada según su grado de periodicidad y asimetría, dos parámetros que nos permiten inferir los procesos físicos responsables de la variación observada. Además, considerando los movimientos propios obtenidos con los datos de VVVX, confirmamos que las estrellas de nuestro catálogo de curvas de luz son miembros de M8, ya que tienen movimientos coherentes. Sin embargo, para NGC 6357 se observó que se agrupan en torno a dos valores medios de movimientos propios, dando lugar a dos poblaciones de estrellas inemáticamente diferentes. Una de ellas está espacialmente relacionada con regiones ricas en polvo y cuyas componentes tienen movimientos proyectados a lo largo de los filamentos de la zona. Esto sugiere que se trata de una población más joven y que podría estar relacionada con un proceso de formación estelar subsecuente. Esto último desarrollado en el material molecular e impulsado por la expansión del gas ionizado. Estos resultados no habían sido encontrados anteriormente en la literatura, principalmente porque se realizaron en el óptico, longitud de onda severamente afectada por la extinción. Esto nuevamente destaca las grandes capacidades de los datos de VVVX y la importancia de estudiar regiones de formación estelar utilizando el IR.