Photometric classification of HSC transients using machine learning

TL;DR

This paper presents a deep neural network-based machine learning method for rapid photometric classification of supernovae in the HSC survey, achieving high accuracy and AUC scores, facilitating efficient follow-up observations.

Contribution

It introduces a DNN model trained on actual observed data without interpretation, optimized for real-time supernova classification in large surveys.

Findings

Achieves 0.996 AUC in binary classification on LSST data

Reaches 95.3% accuracy in three-class classification

Attains 78.1% binary classification accuracy with two weeks of HSC data

Abstract

The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to the need for fast type classification for spectroscopic follow-up and prompted us to develop a machine learning algorithm using a deep neural network (DNN) with highway layers. This machine is trained by actual observed cadence and filter combinations such that we can directly input the observed data array into the machine without any interpretation. We tested our model with a dataset from the LSST classification challenge (Deep Drilling Field). Our classifier scores an area under the curve (AUC) of 0.996 for binary classification (SN Ia or non-SN Ia) and 95.3% accuracy for three-class classification (SN Ia, SN Ibc, or SN II). Application of our binary classification to HSC transient data yields an AUC score of 0.925. With two weeks of HSC data since the first detection, this classifier achieves 78.1% accuracy for binary classification, and the accuracy increases to 84.2% with the full dataset. This paper discusses the potential use of machine learning for SN type classification purposes.