Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/27206
Title: | A package for the automated classification of images containing supernova light echoes |
Authors: | Bhullar A Ali RA Welch DL |
Department: | School of Graduate Studies |
Publication Date: | Nov-2021 |
Publisher: | EDP Sciences |
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> |
URI: | http://hdl.handle.net/11375/27206 |
metadata.dc.identifier.doi: | https://doi.org/10.1051/0004-6361/202039755 |
ISSN: | 0004-6361 1432-0746 |
Appears in Collections: | Faculty Publications (via McMaster Experts) |
Files in This Item:
File | Description | Size | Format | |
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aa39755-20.pdf | Published version | 4.48 MB | Adobe PDF | View/Open |
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