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SNIascore: Deep-learning Classification of Low-resolution Supernova Spectra

Fremling, C, Hall, XJ, Coughlin, MW, Dahiwale, AS, Duev, DA, Graham, MJ, Kasliwal, MM, Kool, EC, Mahabal, AA, Miller, AA, Neill, JD, Perley, DA, Rigault, M, Rosnet, P, Rusholme, B, Sharma, Y, Shin, KM, Shupe, DL, Sollerman, J, Walters, RS and Kulkarni, SR (2021) SNIascore: Deep-learning Classification of Low-resolution Supernova Spectra. Astrophysical Journal Letters, 917 (1). ISSN 2041-8205

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Abstract

We present SNIascore, a deep-learning-based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R ∼ 100) data. The goal of SNIascore is the fully automated classification of SNe Ia with a very low false-positive rate (FPR) so that human intervention can be greatly reduced in large-scale SN classification efforts, such as that undertaken by the public Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS). We utilize a recurrent neural network architecture with a combination of bidirectional long short-term memory and gated recurrent unit layers. SNIascore achieves a <0.6% FPR while classifying up to 90% of the low-resolution SN Ia spectra obtained by the BTS. SNIascore simultaneously performs binary classification and predicts the redshifts of secure SNe Ia via regression (with a typical uncertainty of <0.005 in the range from z = 0.01 to z = 0.12). For the magnitude-limited ZTF BTS survey (≈70% SNe Ia), deploying SNIascore reduces the amount of spectra in need of human classification or confirmation by ≈60%. Furthermore, SNIascore allows SN Ia classifications to be automatically announced in real time to the public immediately following a finished observation during the night.

Item Type: Article
Uncontrolled Keywords: 0201 Astronomical and Space Sciences
Subjects: Q Science > QB Astronomy
Q Science > QC Physics
Divisions: Astrophysics Research Institute
Publisher: American Astronomical Society; IOP Publishing
Date Deposited: 16 Dec 2021 10:27
Last Modified: 16 Dec 2021 10:27
DOI or Identification number: 10.3847/2041-8213/ac116f
URI: https://researchonline.ljmu.ac.uk/id/eprint/15907

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