Burhanudin, U.F., Maund, J.R. orcid.org/0000-0003-0733-7215, Killestein, T. et al. (42 more authors)
(2021)
Light curve classification with recurrent neural networks for GOTO: dealing with imbalanced data.
Monthly Notices of the Royal Astronomical Society, 505 (3).
pp. 4345-4361.
ISSN 0035-8711
Abstract
The advent of wide-field sky surveys has led to the growth of transient and variable source discoveries. The data deluge produced by these surveys has necessitated the use of machine learning (ML) and deep learning (DL) algorithms to sift through the vast incoming data stream. A problem that arises in real-world applications of learning algorithms for classification is imbalanced data, where a class of objects within the data is underrepresented, leading to a bias for over-represented classes in the ML and DL classifiers. We present a recurrent neural network (RNN) classifier that takes in photometric time-series data and additional contextual information (such as distance to nearby galaxies and on-sky position) to produce real-time classification of objects observed by the Gravitational-wave Optical Transient Observer (GOTO), and use an algorithm-level approach for handling imbalance with a focal loss function. The classifier is able to achieve an Area Under the Curve (AUC) score of 0.972 when using all available photometric observations to classify variable stars, supernovae, and active galactic nuclei. The RNN architecture allows us to classify incomplete light curves, and measure how performance improves as more observations are included. We also investigate the role that contextual information plays in producing reliable object classification.
Metadata
Item Type: | Article |
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Authors/Creators: | This paper has 45 authors. You can scroll the list below to see them all or them all.
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Copyright, Publisher and Additional Information: | © 2021 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. This is an author-produced version of a paper subsequently published in Monthly Notices of the Royal Astronomical Society. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | methods: data analysis; techniques: photometric; survey |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Physics and Astronomy (Sheffield) |
Funding Information: | Funder Grant number ROYAL SOCIETY UF150689 SCIENCE AND TECHNOLOGY FACILITIES COUNCIL ST/R000964/1 ROYAL SOCIETY RGF\EA\180234 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Jun 2021 16:01 |
Last Modified: | 01 Mar 2022 15:10 |
Status: | Published |
Publisher: | Oxford University Press (OUP) |
Refereed: | Yes |
Identification Number: | 10.1093/mnras/stab1545 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:174727 |