Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

Abstract

In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts ($0.2<z<1.1$). Our method consists of two stages: feature extraction (obtaining the SN redshift from photometry and estimating light-curve shape parameters) and machine learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of $0.98$. We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than $5\%$. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high-$z$ SN survey with application to real SN data.

v2: accepted to JCAP. v1: 27 pages, submitted to JCAP

References in corpus (4)

• The type Ia supernova SNLS-03D3bb from a super-Chandrasekhar-mass white dwarf star
• Results from the Supernova Photometric Classification Challenge
• The Progenitors of Type Ia Supernovae
• SNLS Spectroscopy: Testing for Evolution in Type Ia Supernovae

Cited by in corpus (18)

• A Fully Automated Integral Field Spectrograph Pipeline for the SEDMachine: pysedm
• PELICAN: deeP architecturE for the LIght Curve ANalysis
• The Dark Energy Survey 5-year photometrically identified Type Ia Supernovae
• Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves
• Fink: early supernovae Ia classification using active learning
• The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification
• Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot
• Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks
• A machine-learning photometric classifier for massive stars in nearby galaxies I. The method
• Quasar and galaxy classification using Gaia EDR3 and CatWise2020
• deepSIP: Linking Type Ia Supernova Spectra to Photometric Quantities with Deep Learning
• Identification of Grand-design and Flocculent Spirals from SDSS using Convolutional Neural network
• Forecasting Supernova Observations with the CSST: I. Photometric Samples
• Real-Time Active Learning for optimised spectroscopic follow-up: Enhancing early SN Ia classification with the Fink broker
• Optimizing Supernova Classification with Interpretable Machine Learning Models
• Dealing with the data imbalance problem on pulsar candidates sifting based on feature selection
• A Template-Based Approach to the Photometric Classification of SN 1991bg-like Supernovae in the SDSS-II Supernova Survey
• SCONE: Supernova Classification with a Convolutional Neural Network