cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation. (arXiv:2401.16356v2 [physics.ins-det] UPDATED)

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 tasks by augmenting datasets for signal searches,
balancing data sets for machine learning, and validating detection schemes. In
this work, we present Conditional Derivative GAN (cDVGAN), a novel conditional
model in the Generative Adversarial Network framework for simulating multiple
classes of time-domain observations that represent gravitational waves (GWs)
and detector glitches. cDVGAN can also …

analysis arxiv class data data analysis data sets datasets detection domain glitch machine machine learning physics physics.ins-det signal simulated data tasks