http://arxiv.org/abs/2208.07260
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 compare the performance of the capsule network implemented in ALED (ALED-m) to several capsule and convolutional neural networks of different architectures. We also apply ALED to a large catalogue of astronomical difference images and manually inspect 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 images, ALED identified a set of light echoes that had been overlooked in manual classification. ALED is available via github.com/LightEchoDetection/ALED.
A. Bhullar, R. Ali and D. Welch
Tue, 16 Aug 22
43/74
Comments: 11 pages, 7 figures, 4 tables, 3 appendices (1 appendix table, 1 appendix figure)