Automated supernova Ia classification using adaptive learning techniques

Abstract

While the current supernova (SN) photometric classification system is based on high resolution spectroscopic observations, the next generation of large scale surveys will be based on photometric light curves of supernovae gathered at an unprecedented rate. Developing an efficient method for SN photometric classification is critical to cope with the rapid growth of data volumes in current astronomical surveys. In this work, we present an adaptive mechanism that generates a predictive model to identify a particular class of SN known as Type Ia, when the source set is made of spectroscopic data, while the target set is made of photometric data. The method is applied to simulated data sets derived from the Supernova Photometric Classification Challenge, and preprocessed using Gaussian Process Regression for all objects with at least 1 observational epoch before -3 and after +24 days since the SN maximum brightness. The main difficulty lies in the compatibility of models between spectroscopic (source) data and photometric (target) data, since the underlying distributions on both, source and target domains, are expected to be significantly different. A solution is to adapt predictive models across domains. Our methodology exploits machine learning techniques by combining two concepts: 1) domain adaptation is used to transfer properties from the source domain to the target domain; and 2) active learning is used as a means to rely on a set of confident labels on the target domain. We show how a combination of both concepts leads to high generalization (i.e., predictive) performance.