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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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SNIascore Deep-learning Classification of Low-resolution Supernova Spectra.pdf - Accepted Version Download (1MB) | Preview |
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 |
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| 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: | 05 Aug 2022 00:50 |
| DOI or ID number: | 10.3847/2041-8213/ac116f |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/15907 |
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