A Convolutional Neural Network Approach to Supernova Time-Series Classification

• URL: http://arxiv.org/abs/2207.09440v1
• Date: Tue, 19 Jul 2022 17:55:22 GMT
• Title: A Convolutional Neural Network Approach to Supernova Time-Series Classification
• Authors: Helen Qu, Masao Sako, Anais Moller, Cyrille Doux
• Abstract summary: We present a convolutional neural network method for fast supernova time-series classification. We are able to differentiate between 6 SN types with 60% accuracy given only two nights of data and 98% accuracy retrospectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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