activity
20202022
most citedTransient-optimised real-bogus classification with Bayesian Convolutional Neural Networks -- sifting the GOTO candidate stream

40 citations · 86 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV20226 cited

Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map

Y. -L. Mong, K. Ackley, T. L. Killestein +45

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 t…

astro-ph.HE202110 cited

Searching For Fermi GRB Optical Counterparts With The Prototype Gravitational-Wave Optical Transient Observer (GOTO)

Y. -L. Mong, K. Ackley, D. K. Galloway +45

The typical detection rate of gamma-ray burst (GRB) per day by the \emph{Fermi} Gamma-ray Burst Monitor (GBM) provides a valuable opportunity to further our understanding o…

astro-ph.IM202130 cited

Light curve classification with recurrent neural networks for GOTO: dealing with imbalanced data

U. F. Burhanudin, J. R. Maund, T. Killestein +42

The advent of wide-field sky surveys has led to the growth of transient and variable source discoveries. The data deluge produced by these surveys has necessitated the use of machi…

astro-ph.IM2021

Processing GOTO data with the Rubin Observatory LSST Science Pipelines II: Forced Photometry and light curves

L. Makrygianni, J. Mullaney, V. Dhillon +45

We have adapted the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Science Pipelines to process data from the Gravitational-Wave Optical Transient Observer (GOTO)…

astro-ph.IM202140 cited

Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks -- sifting the GOTO candidate stream

T. L. Killestein, J. Lyman, D. Steeghs +45

Large-scale sky surveys have played a transformative role in our understanding of astrophysical transients, only made possible by increasingly powerful machine learning-based filte…

astro-ph.IM2020

Machine Learning for Transient Recognition in Difference Imaging With Minimum Sampling Effort

Yik-Lun Mong, Kendall Ackley, Duncan Galloway +45

The amount of observational data produced by time-domain astronomy is exponentially in-creasing. Human inspection alone is not an effective way to identify genuine transients fromt…