[ascl:2208.012]
DELIGHT: Identify host galaxies of transient candidates
Förster, Francisco;
Muñoz Arancibia, Alejandra M.;
Reyes, 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;
Hernandez-Garcia, Lorena;
Huijse, Pablo;
Reyes, Esteban;
Sánchez-Sáez, Paula;
Ramirez, Mauricio;
Grandón, Daniela;
Pineda-García, Jonathan;
Chabour-Barra, Francisca;
Silva-Farfán, Javier
DELIGHT (Deep Learning Identification of Galaxy Hosts of Transients) automatically identifies host galaxies of transient candidates using multi-resolution images and a convolutional neural network. This library has a class with several methods 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. DELIGHT can also estimate the host's semi-major axis if requested, taking advantage of the multi-resolution images.
- Code site:
-
https://github.com/fforster/delight
- Described in:
-
https://ui.adsabs.harvard.edu/abs/2022arXiv220804310F
- Bibcode:
- 2022ascl.soft08012F