Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)

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




Julkaisun tekijätMong 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

KustantajaOXFORD UNIV PRESS

Julkaisuvuosi2023

JournalMonthly Notices of the Royal Astronomical Society

Tietokannassa oleva lehden nimiMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Lehden akronyymiMON NOT R ASTRON SOC

Volyymi518

Julkaisunumero1

Aloitussivu752

Lopetussivun numero762

Sivujen määrä11

ISSN0035-8711

eISSN1365-2966

DOIhttp://dx.doi.org/10.1093/mnras/stac3103

Verkko-osoitehttps://academic.oup.com/mnras/article/518/1/752/6794293


Tiivistelmä

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 2024-23-02 at 09:17