cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation
CoRR(2024)
摘要
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 generate generalized hybrid samples that
span the variation between classes through interpolation in the conditioned
class vector. cDVGAN introduces an additional player into the typical 2-player
adversarial game of GANs, where an auxiliary discriminator analyzes the
first-order derivative time-series. Our results show that this provides
synthetic data that better captures the features of the original data. cDVGAN
conditions on three classes, two denoised from LIGO blip and tomte glitch
events from its 3rd observing run (O3), and the third representing binary black
hole (BBH) mergers. Our proposed cDVGAN outperforms 4 different baseline GAN
models in replicating the features of the three classes. Specifically, our
experiments show that training convolutional neural networks (CNNs) with our
cDVGAN-generated data improves the detection of samples embedded in detector
noise beyond the synthetic data from other state-of-the-art GAN models. Our
best synthetic dataset yields as much as a 4.2
area-under-the-curve (AUC) performance compared to synthetic datasets from
baseline GANs. Moreover, training the CNN with hybrid samples from our cDVGAN
outperforms CNNs trained only on the standard classes, when identifying real
samples embedded in LIGO detector background (4
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