Clustering of Astronomical Transient Candidates Using Deep Variational Embedding
Nicolás Astorga, P. Huijse, P. A. Estévez, F. Förster · 2018
The exponential growth of the data collected by telescopes have turned astronomy into a data-drive science. The detection of astronomical transient events, short-lived and bright phenomena such as the Supernovae, is currently a main science driver of many astronomical surveys. There is an opportunity for the application of machine learning methods for the automatic detection of astronomical transients.In this paper we focus on the unsupervised learning case to perform an exploratory analysis on a dataset of 1,250,000 astronomical transient candidates from the High Cadence Transient Survey. Our contributions can be summarized in 1) The application of Deep Variational Embedding for latent space clustering of a large database of transient candidates obtaining a clustering accuracy of 95.33% and 2) The proposal of an auto-regularization term as a novel approach to solve the common problem of over-regularization in variational autoencoders, we show that using this term not only improves the convergence of the algorithm but also increases the clustering accuracy and reconstruction quality.