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

Pablo Huijse, Nicolas Astorga, Pablo Estévez, Giuliano Pignata

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisheri6doc.com publication
Pages321-326
Number of pages6
ISBN (Electronic)9782875870476
Publication statusPublished - 1 Jan 2018
Event26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 - Bruges, Belgium
Duration: 25 Apr 201827 Apr 2018

Publication series

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

Conference

Conference26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018
Country/TerritoryBelgium
CityBruges
Period25/04/1827/04/18

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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