A Convolutional Neural Network Approach to Supernova Time-Series Classification

TL;DR

This paper introduces a convolutional neural network approach for classifying supernovae types using time-series brightness data, achieving high accuracy in both retrospective and real-time scenarios, which is vital for upcoming large-scale telescopic surveys.

Contribution

The paper presents a novel CNN-based method for supernova classification from time-series data, including smoothing techniques and performance evaluation on both full and truncated datasets.

Findings

Achieves >99% accuracy in distinguishing Type Ia supernovae from other types.

Classifies 6 supernova types with 60% accuracy using only two nights of data.

Retrospective classification accuracy reaches 98% for full datasets.

Abstract

One of the brightest objects in the universe, supernovae (SNe) are powerful explosions marking the end of a star's lifetime. Supernova (SN) type is defined by spectroscopic emission lines, but obtaining spectroscopy is often logistically unfeasible. Thus, the ability to identify SNe by type using time-series image data alone is crucial, especially in light of the increasing breadth and depth of upcoming telescopes. We present a convolutional neural network method for fast supernova time-series classification, with observed brightness data smoothed in both the wavelength and time directions with Gaussian process regression. We apply this method to full duration and truncated SN time-series, to simulate retrospective as well as real-time classification performance. Retrospective classification is used to differentiate cosmologically useful Type Ia SNe from other SN types, and this method achieves >99% accuracy on this task. We are also able to differentiate between 6 SN types with 60% accuracy given only two nights of data and 98% accuracy retrospectively.