Browsing by Author "Forster, Francisco"

Now showing 1 - 4 of 4
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    A new, faint population of X-ray transients.
    (2017) Bauer, Franz Erik; Treister, Ezequiel; Schulze, Steve.; Schawinski, Kevin; Luo, B.; Alexander, D. M.; Brandt, W N.; Comastri, A.; Forster, Francisco; Gilli, Roberto
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    Deep 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, Javier
    The 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/...
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    Multi-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 
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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.