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Self-supervised clustering on image-subtracted data with deep-embedded self-organizing map

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posted on 2024-03-26, 16:39 authored by Y-L Mong, K Ackley, TL Killestein, DK Galloway, C Vassallo, M Dyer, R Cutter, MJ Brown, J Lyman, K Ulaczyk, D Steeghs, V Dhillon, P O'Brien, G Ramsay, K Noysena, R Kotak, R Breton, L Nuttall, E Palle, D Pollacco, E Thrane, S Awiphan, U Burhanudin, P Chote, A Chrimes, E Daw, C Duffy, R Eyles-Ferris, BP Gompertz, T Heikkila, P Irawati, M Kennedy, A Levan, S Littlefair, L Makrygianni, T Marsh, D Mata Sanchez, S Mattila, JR Maund, J McCormac, D Mkrtichian, J Mullaney, E Rol, U Sawangwit, E Stanway, R Starling, P Strom, S Tooke, K Wiersema

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 process 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 $6.6{{\ \rm per\,cent}}$ with a false-positive rate of $1.5{{\ \rm per\,cent}}$. 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, e.g. built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations.

History

Author affiliation

School of Physics & Astronomy, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

Monthly Notices of the Royal Astronomical Society

Volume

518

Issue

1

Pagination

752 - 762

Publisher

Oxford University Press (OUP) for Royal Astronomical Society

issn

0035-8711

eissn

1365-2966

Copyright date

2022

Available date

2024-03-26

Language

English