A package for the automated classification of images containing supernova light echoes
| Autor: | A. Bhullar, R. A. Ali, Douglas L. Welch |
|---|---|
| Přispěvatelé: | School of Graduate Studies |
| Rok vydání: | 2021 |
| Předmět: |
Physics
business.industry FOS: Physical sciences Astronomy and Astrophysics Context (language use) Astrophysics Convolutional neural network Visualization Set (abstract data type) Supernova Astrophysics - Solar and Stellar Astrophysics Space and Planetary Science Light echo Test set Computer vision Artificial intelligence Routing (electronic design automation) Astrophysics - Instrumentation and Methods for Astrophysics business Instrumentation and Methods for Astrophysics (astro-ph.IM) Solar and Stellar Astrophysics (astro-ph.SR) |
| Zdroj: | Astronomy & Astrophysics. 655:A82 |
| ISSN: | 1432-0746 0004-6361 |
| DOI: | 10.1051/0004-6361/202039755 |
| Popis: | 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. 11 pages, 7 figures, 4 tables, 3 appendices (1 appendix table, 1 appendix figure) |
| Databáze: | OpenAIRE |
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