DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification
Abstract
Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey datasets, we designed ZipperNet, a multi-branch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory (LSTM) layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories -- no lens, galaxy-galaxy lens, lensed type Ia supernova, lensed core-collapse supernova -- within high-fidelity simulations of three cosmic survey data sets -- the Dark Energy Survey (DES), Rubin Observatory's Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like dataset, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79\% accuracy, and demonstrates similarly high performance for LSNe 1-2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments.
Cite
@article{arxiv.2112.01541,
title = {DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification},
author = {Robert Morgan and B. Nord and K. Bechtol and S. J. González and E. Buckley-Geer and A. Möller and J. W. Park and A. G. Kim and S. Birrer and M. Aguena and J. Annis and S. Bocquet and D. Brooks and A. Carnero Rosell and M. Carrasco Kind and J. Carretero and R. Cawthon and L. N. da Costa and T. M. Davis and J. De Vicente and P. Doel and I. Ferrero and D. Friedel and J. Frieman and J. García-Bellido and M. Gatti and E. Gaztanaga and G. Giannini and D. Gruen and R. A. Gruendl and G. Gutierrez and D. L. Hollowood and K. Honscheid and D. J. James and K. Kuehn and N. Kuropatkin and M. A. G. Maia and R. Miquel and A. Palmese and F. Paz-Chinchón and M. E. S. Pereira and A. Pieres and A. A. Plazas Malagón and K. Reil and A. Roodman and E. Sanchez and M. Smith and E. Suchyta and M. E. C. Swanson and G. Tarle and C. To},
journal= {arXiv preprint arXiv:2112.01541},
year = {2022}
}
Comments
Published in ApJ