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Self-supervised clustering on image-subtracted data with Deep-Embedded Self-Organizing Map
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(2023) Self-supervised clustering on image-subtracted data with Deep-Embedded Self-Organizing Map. Monthly Notices of the Royal Astronomical Society, 518 (1). pp. 752-762. doi:10.1093/mnras/stac3103 ISSN 1365-2966.
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Official URL: https://doi.org/10.1093/mnras/stac3103
Abstract
Developing an effective automatic classifier to separate genuine sources from artifacts is essential for transient follow-ups in wide-field optical surveys. The identification of transient detections from the subtraction artifacts after the image differencing proccess is a key step in such classifiers, known as real-bogus classification problem. We apply a self-supervised machine learning model, the deep-embedded self-organizing map (DESOM) to this ‘real-bogus’ classification problem. DESOM combines an autoencoder and a self-organizing map to perform clustering in order to distinguish between real and bogus detections, based on their dimensionality-reduced representations. We use 32 × 32 normalized detection thumbnails as the input of DESOM. We demonstrate different model training approaches, and find that our best DESOM classifier shows a missed detection rate of $6.6{{\%}}$ with a false positive rate of $1.5{{\%}}$. DESOM offers a more nuanced way to fine-tune the decision boundary identifying likely real detections when used in combination with other types of classifiers, for example built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Journal or Publication Title: | Monthly Notices of the Royal Astronomical Society | ||||||||
Publisher: | Oxford University Press (OUP) | ||||||||
ISSN: | 1365-2966 | ||||||||
Official Date: | January 2023 | ||||||||
Dates: |
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Volume: | 518 | ||||||||
Number: | 1 | ||||||||
Page Range: | pp. 752-762 | ||||||||
DOI: | 10.1093/mnras/stac3103 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Free Access (unspecified licence, 'bronze OA') | ||||||||
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