Latent representations of transient candidates from an astronomical image difference pipeline using Variational Autoencoders

Pablo Huijse, Nicolas Astorga, Pablo Estévez, Giuliano Pignata

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

Resumen

The Chilean Automatic Supernovae SEarch (CHASE) is a survey designed to detect early Supernovae. In this paper we explore deep autoencoders to obtain a compressed latent space for a large transient candidate database from the CHASE image difference pipeline. Compared to conventional methods, the latent variables obtained with variational au-toencoders preserve more information and are more discriminative towards real astronomical transients.

Idioma originalInglés
Título de la publicación alojadaESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Editoriali6doc.com publication
Páginas321-326
Número de páginas6
ISBN (versión digital)9782875870476
EstadoPublicada - 1 ene 2018
Evento26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 - Bruges, Bélgica
Duración: 25 abr 201827 abr 2018

Serie de la publicación

NombreESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conferencia

Conferencia26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018
PaísBélgica
CiudadBruges
Período25/04/1827/04/18

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial
  • Sistemas de información

Huella Profundice en los temas de investigación de 'Latent representations of transient candidates from an astronomical image difference pipeline using Variational Autoencoders'. En conjunto forman una huella única.

  • Citar esto

    Huijse, P., Astorga, N., Estévez, P., & Pignata, G. (2018). Latent representations of transient candidates from an astronomical image difference pipeline using Variational Autoencoders. En ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 321-326). (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication.