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

40 citations · 85 across the 4 of their papers we have counts for

collaborators

7 papers

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.IM20205 cited

Processing GOTO data with the Rubin Observatory LSST Science Pipelines I : Production of coadded frames

J. R. Mullaney, L. Makrygianni, V. Dhillon +44

The past few decades have seen the burgeoning of wide field, high cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted b…

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…