activity
20162026
most citedGravitational Waves and Gamma-rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A

3.6k citations · 12.8k across the 81 of their papers we have counts for

collaborators
Showing astro-ph.IMShow all

13 papers · 1 filter

astro-ph.IM2026

LIGO A: Detector Design and Science Prospects Beyond A+

L. Sun, K. Kuns, B. J. J. Slagmolen +1153

We present the LIGO A detector concept, an upgrade for the LIGO observatories based on room-temperature interferometers beyond the fifth observing run (O5). Building on th…

astro-ph.IM2024

GERry: A Code to Optimise the Hunt for the Electromagnetic Counter-parts to Gravitational Wave Events

David O'Neill, Joseph Lyman, Kendall Ackley +19

The search for the electromagnetic counterparts to gravitational wave (GW) events has been rapidly gathering pace in recent years thanks to the increasing number and capabilities o…

astro-ph.IM2024

The Gravitational-wave Optical Transient Observer (GOTO)

Martin J. Dyer, Kendall Ackley, Felipe Jiménez-Ibarra +19

The Gravitational-wave Optical Transient Observer (GOTO) is a project dedicated to identifying optical counter-parts to gravitational-wave detections using a network of dedicated,…

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…