Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)
Self-supervised clustering on image-subtracted data with deep-embedded self-organizing map
Julkaisun tekijät: Mong 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
Kustantaja: OXFORD UNIV PRESS
Julkaisuvuosi: 2023
Journal: Monthly Notices of the Royal Astronomical Society
Tietokannassa oleva lehden nimi: MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Lehden akronyymi: MON NOT R ASTRON SOC
Volyymi: 518
Julkaisunumero: 1
Aloitussivu: 752
Lopetussivun numero: 762
Sivujen määrä: 11
ISSN: 0035-8711
eISSN: 1365-2966
DOI: http://dx.doi.org/10.1093/mnras/stac3103
Verkko-osoite: https://academic.oup.com/mnras/article/518/1/752/6794293
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.