A package for the automated classification of images containing supernova light echoes^⋆

¹ University of Guelph, Department of Mathematics and Statistics, Guelph, Canada
e-mail: bhullara@uoguelph.ca, aali@uoguelph.ca
² McMaster University, Department of Physics and Astronomy, Hamilton, Canada
e-mail: welch@physics.mcmaster.ca

Received: 24 October 2020
Accepted: 18 October 2021

Abstract

Context. The so-called light echoes of supernovae – the apparent motion of outburst-illuminated interstellar dust – can be detected in astronomical difference images; however, light echoes are extremely rare which makes manual detection an arduous task. Surveys for centuries-old supernova light echoes can involve hundreds of pointings of wide-field imagers wherein the subimages from each CCD amplifier require examination.

Aims. We introduce ALED, a Python package that implements (i) a capsule network trained to automatically identify images with a high probability of containing at least one supernova light echo and (ii) routing path visualization to localize light echoes and/or light echo-like features in the identified images.

Methods. We compared the performance of the capsule network implemented in ALED (ALED-m) to several capsule and convolutional neural networks of different architectures. We also applied ALED to a large catalogue of astronomical difference images and manually inspected candidate light echo images for human verification.

Results. ALED-m was found to achieve 90% classification accuracy on the test set and to precisely localize the identified light echoes via routing path visualization. From a set of 13 000+ astronomical difference images, ALED identified a set of light echoes that had been overlooked in manual classification.