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