DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics
Tom Dooney, Stefano Bromuri, Lyana Curier
模拟引力波(GW)探测器环境的时间域观测将使人们更好地了解GW源,增加GW信号检测的数据集,并帮助表征探测器的噪声,从而获得更好的物理学。 本文介绍了一种使用名为DVGAN的三人制Wasserstein生成对抗网络(WGAN)模拟固定长度时间域信号的新方法,其中包括对输入信号的衍生物进行区分的辅助鉴别器。 消融研究用于比较将辅助衍生物鉴别器与香草双玩家WGAN的对抗反馈的效果进行比较。 我们表明,对衍生物进行区分可以稳定1D连续信号在训练阶段对1D连续信号的GAN组件的学习。 这导致生成的信号更平滑,与真实样本的区分较少,并更好地捕获训练数据的分布。 DVGAN还用于模拟在先进的LIGO GW探测器中捕获的真实瞬态噪声事件。
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 de...