A New Method to Classify Type IIP/IIL Supernovae Based on their Spectra

Abstract

Type IIP and type IIL supernovae (SNe) are defined on their light curves, but the spectrum criteria in distinguishing these two type SNe remains unclear. We propose a new classification method. Firstly, we subtract the principal components of different wavelength bands in the spectra based on Functional Principal Components Analysis (FPCA) method. Then, we use Support Vector Machine (SVM) and Artificial Neural Network (ANN) to classify these two types of SNe. The best F1-Score of our classifier is 0.881, and we found that solely using Halpha line at 6150-6800 A for classification can reach a F1-Score up to 0.849. Our result indicates that the profile of the Halpha is the key to distinguish the two type SNe.

arXiv commentSubmitted to apj. Comments are welcome!

References in corpus (12)

• Adam: A Method for Stochastic Optimization
• Observational constraints on the progenitors of core-collapse supernovae : the case for missing high mass stars
• Photometric and Spectroscopic Properties of Type II-P Supernovae
• Optical and near infrared coverage of SN 2004et: physical parameters and comparison with other type IIP supernovae
• Supernova 2013by: A Type IIL Supernova with a IIP-like light curve drop
• Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach
• The Evolution of Temperature and Bolometric Luminosity in Type-II Supernovae
• Hydrogen-rich Core Collapse Supernovae
• SN 2016esw: a luminous Type II supernova observed within the first day after the explosion
• Quantitative Classification of Type I Supernovae Using Spectroscopic Features at Maximum Brightness
• Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM
• A geometric approach to maximum likelihood estimation of the functional principal components from sparse longitudinal data