Single-epoch supernova classification with deep convolutional neural networks

TL;DR

This paper introduces a deep convolutional neural network approach that classifies Type-Ia supernovae from single-epoch images, eliminating the need for multi-epoch luminance measurements and complex data processing.

Contribution

It presents a novel integrated neural network model that estimates luminance and classifies supernovae from a single observation, streamlining the classification process.

Findings

Achieves classification accuracy comparable to multi-epoch photometric methods.

Reduces the need for complex luminance measurements and multiple observations.

Demonstrates effectiveness of deep learning in astronomical transient classification.

Abstract

Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accurately measure the distance to the objects. Finding large samples of SNeIa and investigating their detailed characteristics have become an important issue in cosmology and astronomy. Existing methods relied on a photometric approach that first measures the luminance of supernova candidates precisely and then fits the results to a parametric function of temporal changes in luminance. However, it inevitably requires multi-epoch observations and complex luminance measurements. In this work, we present a novel method for classifying SNeIa simply from single-epoch observation images without any complex measurements, by effectively integrating the state-of-the-art computer vision methodology into the standard photometric approach. Our method first builds a convolutional neural network for estimating the luminance of supernovae from telescope images, and then constructs another neural network for the classification, where the estimated luminance and observation dates are used as features for classification. Both of the neural networks are integrated into a single deep neural network to classify SNeIa directly from observation images. Experimental results show the effectiveness of the proposed method and reveal classification performance comparable to existing photometric methods with multi-epoch observations.