Deep Learning Identification of Galaxy Hosts in Transients (DELIGHT)

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dc.catalogadoryvc
dc.contributor.authorForster, Francisco
dc.contributor.authorMuñoz Arancibia, Alejandra M.
dc.contributor.authorReyes, Ignacio
dc.contributor.authorGagliano, Alexander
dc.contributor.authorBritt, Dylan J.
dc.contributor.authorCuellar-Carrillo, Sara
dc.contributor.authorFigueroa-Tapia, Felipe
dc.contributor.authorPolzin, Ava
dc.contributor.authorYousef, Yara
dc.contributor.authorArredondo, Javier
dc.contributor.authorRodríguez-Mancini, Diego
dc.contributor.authorCorrea-Orellana, Javier
dc.contributor.authorBayo, Amelia
dc.contributor.authorBauer, Franz E.
dc.contributor.authorCatelan, Márcio
dc.contributor.authorCabrera-Vives, Guillermo
dc.contributor.authorDastidar, Raya
dc.contributor.authorEstévez, Pablo A.
dc.contributor.authorPignata, Giuliano
dc.contributor.authorHernández-Garcia, Lorena
dc.contributor.authorHuijse, Pablo
dc.contributor.authorReyes, Esteban
dc.contributor.authorSánchez-Sáez, Paula
dc.contributor.authorRamírez, Mauricio
dc.contributor.authorGrandón, Daniela
dc.contributor.authorPineda-García, Jonathan
dc.contributor.authorChabour-Barra, Francisca
dc.contributor.authorSilva-Farfán, Javier
dc.date.accessioned2024-02-28T13:37:38Z
dc.date.available2024-02-28T13:37:38Z
dc.date.issued2022
dc.description.abstractThe 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/...
dc.fechaingreso.objetodigitalNo aplica
dc.fuente.origenORCID
dc.identifier.doi10.5281/zenodo.7049083
dc.identifier.urihttps://doi.org/10.5281/zenodo.7049083
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/81395
dc.information.autorucInstituto de Astrofísica ; Catelan, Márcio ; 0000-0001-6003-8877 ; 1001556
dc.language.isoen
dc.nota.accesosin adjunto
dc.rightsacceso abierto
dc.titleDeep Learning Identification of Galaxy Hosts in Transients (DELIGHT)
dc.typepreprint
sipa.codpersvinculados1001556
sipa.trazabilidadORCID;2024-01-22
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