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

Abstract

<jats:p><jats:italic>Context.</jats:italic> 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.</jats:p> <jats:p><jats:italic>Aims.</jats:italic> 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.</jats:p> <jats:p><jats:italic>Methods.</jats:italic> 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.</jats:p> <jats:p><jats:italic>Results.</jats:italic> 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.</jats:p>