论文标题
Dvgan：稳定Wasserstein GAN训练时间域重力波物理
DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics
论文作者
论文摘要
模拟重力波（GW）检测器环境的时间域观察结果将使GW源有更好的了解，增强用于GW信号检测的数据集并有助于表征探测器的噪声，从而提供更好的物理学。本文提出了一种新的方法，该方法是使用三人瓦斯汀生成对抗网络（WGAN）（称为dvgan）模拟固定长度的时间域信号，其中包括一个辅助歧视器，该辅助歧视器对输入信号的衍生物进行区分。消融研究用于比较与辅助导数歧视者一起使用带有香草两种玩家wgan的对抗反馈的效果。我们表明，在训练阶段，对衍生物的区分可以稳定在1D连续信号上学习GAN组件。这会导致更顺畅地产生的信号，这些信号与真实样品的区别较小，并且更好地捕获了训练数据的分布。 DVGAN还用于模拟高级LIGO GW检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 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.
