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

August 2021 • 2021ApJ...917L...2F

Authors • Fremling, Christoffer • Hall, Xander J. • Coughlin, Michael W. • Dahiwale, Aishwarya S. • Duev, Dmitry A. • Graham, Matthew J. • Kasliwal, Mansi M. • Kool, Erik C. • Mahabal, Ashish A. • Miller, Adam A. • Neill, James D. • Perley, Daniel A. • Rigault, Mickael • Rosnet, Philippe • Rusholme, Ben • Sharma, Yashvi • Shin, Kyung Min • Shupe, David L. • Sollerman, Jesper • Walters, Richard S. • Kulkarni, S. R.

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.

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Ben Rusholme

Chief Engineer

Dave Shupe

Senior Scientist