DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics. (arXiv:2209.13592v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Dooney_T/0/1/0/all/0/1">Tom Dooney</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bromuri_S/0/1/0/all/0/1">Stefano Bromuri</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Curier_L/0/1/0/all/0/1">Lyana Curier</a>
Simulating time-domain observations of gravitational wave (GW) detector
environments will allow for a better understanding of GW sources, augment
datasets for GW signal detection and help in characterizing the noise of the
detectors, leading to better physics. This paper presents a novel approach to
simulating fixed-length time-domain signals using a three-player Wasserstein
Generative Adversarial Network (WGAN), called DVGAN, that includes an auxiliary
discriminator that discriminates on the derivatives of input signals. An
ablation study is used to compare the effects of including adversarial feedback
from an auxiliary derivative discriminator with a vanilla two-player WGAN. We
show that discriminating on derivatives can stabilize the learning of GAN
components on 1D continuous signals during their training phase. This results
in smoother generated signals that are less distinguishable from real samples
and better capture the distributions of the training data. DVGAN is also used
to simulate real transient noise events captured in the advanced LIGO GW
detector.
Simulating time-domain observations of gravitational wave (GW) detector
environments will allow for a better understanding of GW sources, augment
datasets for GW signal detection and help in characterizing the noise of the
detectors, leading to better physics. This paper presents a novel approach to
simulating fixed-length time-domain signals using a three-player Wasserstein
Generative Adversarial Network (WGAN), called DVGAN, that includes an auxiliary
discriminator that discriminates on the derivatives of input signals. An
ablation study is used to compare the effects of including adversarial feedback
from an auxiliary derivative discriminator with a vanilla two-player WGAN. We
show that discriminating on derivatives can stabilize the learning of GAN
components on 1D continuous signals during their training phase. This results
in smoother generated signals that are less distinguishable from real samples
and better capture the distributions of the training data. DVGAN is also used
to simulate real transient noise events captured in the advanced LIGO GW
detector.
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