Browsing by Author "Gagliano, Alexander"

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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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    DELIGHT: 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, Javier
    We 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....
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    SN 2019tsf: Evidence for Extended Hydrogen-poor CSM in the Three-peaked Light Curve of Stripped Envelope of a Type Ib Supernova
    (2025) Zenati, Yossef; Wang, Qinan; Bobrick, Alexey; DeMarchi, Lindsay; Glanz, Hila; Rozner, Mor; Jencson, Jacob E.; Rest, Armin; Metzger, Brian D.; Margutti, Raffaella; Gómez, Sebastián; Smith, Nathan; Toonen, Silvia; Bright, Joe S.; Norman, Colin; Foley, Ryan J.; Gagliano, Alexander; Krolik, Julian H.; Smartt, Stephen J.; Villar, Ashley V.; Narayan, Gautham; Fox, Ori; Auchettl, Katie; Brethauer, Daniel; Clocchiatti, Alejandro; Coelln, Sophie V.; Coppejans, Deanne L.; Dimitriadis, Georgios; Dorozsmai, Andris; Drout, Maria; Jacobson-Galan, Wynn; Gao, Bore; Ridden-Harper, Ryan; Kilpatrick, Charles Donald; Laskar, Tanmoy; Matthews, David; Smith, Ken W.; Stauffer, Candice McKenzie; Rest, Sofia; Stroh, Michael C.; Strolger, Louis-Gregory; Terreran, Giacomo; Pierel, Justin D. R.; Piro, Anthony L.
    We present multiband ATLAS and ZTF photometry for SN 2019tsf, a Type Ib stripped-envelope supernova (SESN). The slow spectral evolution could be associated with an uncommon explosion mechanism specific to this SN. Possible explanations include fallback accretion onto a compact remnant or a long-lived central engine, both of which could provide extended energy injection responsible for the late-time rebrightening and unusual spectral features. The rebrightening observations represent the latest photometric measurements of a multipeaked Type Ib SN. As late-time photometry and spectroscopy suggest no hydrogen, the potential circumstellar material (CSM) must be H-poor. The absence of a nebular phase and the lack of narrow emission lines in the late-time spectra (>142 days) of the SNe suggest that any CSM interaction is likely asymmetric and enveloped by the SN ejecta. However, an extended CSM structure is evident through a follow-up radio campaign with the Karl G. Jansky Very Large Array (VLA), indicating a source of bright optically thick radio emission at late times, which is highly unusual among H-poor SESNe. We attribute this phenomenology to an interaction of the supernova ejecta with asymmetric CSM, potentially disk-like, and we present several models that may explain the origin of this rare Type Ib supernova. We propose a warped disk model in which a tertiary companion—commonly present around massive stars—perturbs the progenitor's CSM, producing density enhancements that may explain the observed multipeaked SN 2019tsf light curve. This SN 2019tsf is a unique SN Type Ib among the recently discovered class of SNe that undergo mass transfer at the moment of explosion....