A1 Refereed original research article in a scientific journal

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




AuthorsMong Y-L, Ackley K, Killestein T L, Galloway D K, Vassallo C, Dyer M, Cutter R, Brown M J I, Lyman J, Ulaczyk K, Steeghs D, Dhillon V, O’Brien P, Ramsay G, Noysena K, Kotak R, Breton R, Nuttall L, Pallé E, Pollacco D, Thrane E, Awiphan S, Burhanudin U, Chote P, Chrimes A, Daw E, Duffy C, Eyles-Ferris R, Gompertz B P, Heikkilä T, Irawati P, Kennedy M, Levan A, Littlefair S, Makrygianni L, Marsh T, Mata Sánchez D, Mattila S, Maund J R, McCormac J, Mkrtichian D, Mullaney J, Rol E, Sawangwit U, Stanway E, Starling R, Strøm P, Tooke S, Wiersema K

PublisherOXFORD UNIV PRESS

Publication year2023

JournalMonthly Notices of the Royal Astronomical Society

Journal name in sourceMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Journal acronymMON NOT R ASTRON SOC

Volume518

Issue1

First page 752

Last page762

Number of pages11

ISSN0035-8711

eISSN1365-2966

DOIhttps://doi.org/10.1093/mnras/stac3103(external)

Web address https://academic.oup.com/mnras/article/518/1/752/6794293(external)

Preprint addresshttps://arxiv.org/pdf/2209.06375.pdf(external)


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 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 x 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 per cent with a false-positive rate of 1.5 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.



Last updated on 2024-26-11 at 11:41