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% increase in 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% AUC improvement for cDVGAN).

翻译：摘要：模拟引力波与引力波探测器干扰信号的真实时域观测，有助于推进引力波数据分析。模拟数据可通过扩充信号搜索数据集、平衡机器学习数据集以及验证探测方案等途径用于下游任务。本文提出条件导数生成对抗网络（cDVGAN）——生成对抗网络框架下的一种新型条件模型，用于模拟代表引力波与探测器干扰信号的多类时域观测。cDVGAN还可通过条件类别向量的插值，生成跨越各类别变异的广义混合样本。cDVGAN在标准GAN的两人对抗博弈中引入了一个额外参与者，其中辅助判别器分析一阶导数时间序列。结果表明，该方法生成的合成数据能更好地捕捉原始数据的特征。cDVGAN以三个类别为条件：其中两类来自对LIGO第三轮观测运行中blip与tomte干扰事件进行降噪处理后的数据，第三类代表双黑洞合并。我们提出的cDVGAN在复现三个类别特征方面优于四种不同的基线GAN模型。具体而言，实验表明，使用cDVGAN生成数据训练卷积神经网络，在检测嵌入探测器噪声中的样本方面，其表现优于使用其他最先进GAN模型合成数据的结果。与基线GAN的合成数据集相比，我们最优合成数据集的曲线下面积性能提升了高达4.2%。此外，在识别嵌入LIGO探测器背景的真实样本时，使用cDVGAN混合样本训练的CNN表现优于仅使用标准类别训练的CNN（cDVGAN的AUC提升4%）。