We present deepSIP ( deep learning of Supernova Ia Parameters ) , a software package for measuring the phase and — for the first time using deep learning — the light-curve shape of a Type Ia supernova ( SN Ia ) from an optical spectrum .
At its core , deepSIP consists of three convolutional neural networks trained on a substantial fraction of all publicly-available low-redshift SN Ia optical spectra , onto which we have carefully coupled photometrically-derived quantities .
We describe the accumulation of our spectroscopic and photometric datasets , the cuts taken to ensure quality , and our standardised technique for fitting light curves .
These considerations yield a compilation of 2754 spectra with photometrically characterised phases and light-curve shapes .
Though such a sample is significant in the SN community , it is small by deep-learning standards where networks routinely have millions or even billions of free parameters .
We therefore introduce a data-augmentation strategy that meaningfully increases the size of the subset we allocate for training while prioritising model robustness and telescope agnosticism .
We demonstrate the effectiveness of our models by deploying them on a sample unseen during training and hyperparameter selection , finding that Model I identifies spectra that have a phase between -10 and 18 d and light-curve shape , parameterised by \Delta m _ { 15 } , between 0.85 and 1.55 mag with an accuracy of 94.6 % .
For those spectra that do fall within the aforementioned region in phase– \Delta m _ { 15 } space , Model II predicts phases with a root-mean-square error ( RMSE ) of 1.00 d and Model III predicts \Delta m _ { 15 } values with an RMSE of 0.068 mag .