Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded
Y. -L. Mong, K. Ackley, T. L. Killestein, D. K. Galloway, M. Dyer, R. Cutter, M. J. I. 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, B. P. Gompertz, T. Heikkila, P. Irawati, M. Kennedy, A. Levan, S. Littlefair, L. Makrygianni, T. Marsh, D. Mata Sanchez, S. Mattila, J. R. 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 32x32
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.
Subjects: Computer Science - Computer Vision and Pattern Recognition; Astrophysics - Instrumentation and Methods for Astrophysics