6 citations · 10 across the 3 of their papers we have counts for
3 papers
astro-ph.IM2026
Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks
Tom Dooney, Mees de Boer, Harsh Narola +4
Gravitational-wave detectors such as LIGO, Virgo, and KAGRA are highly sensitive instruments susceptible to many noise sources. Short-duration transient noise events, known as glit…
gr-qc2025★ 4 cited
DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning
Tom Dooney, Harsh Narola, Stefano Bromuri +4
Gravitational wave (GW) detectors, such as LIGO, Virgo, and KAGRA, detect faint signals from distant astrophysical events. However, their high sensitivity also makes them susceptib…
physics.ins-det2024★ 6 cited
cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation
Tom Dooney, Lyana Curier, Daniel Tan +3
Simulating realistic time-domain observations of gravitational waves (GWs) and GW detector glitches can help in advancing GW data analysis. Simulated data can be used in downstream…