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：模拟引力波（GW）及探测器毛刺的时域观测数据有助于推动引力波数据分析的发展。模拟数据可服务于下游任务，包括扩充信号搜索数据集、平衡机器学习数据集以及验证检测方案。本文提出条件导数生成对抗网络（cDVGAN），这是一种基于生成对抗网络框架的新型条件模型，用于模拟代表引力波和探测器毛刺的多类时域观测数据。cDVGAN还能通过对条件类别向量进行插值，生成跨越类别间变化的广义混合样本。cDVGAN在标准GAN的双玩家对抗博弈中引入额外角色，通过辅助鉴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特征。cDVGAN对三类数据施加条件：其中两类来自LIGO第三次观测运行（O3）中经降噪处理的blip和tomte毛刺事件，第三类代表双黑洞（BBH）并合。我们提出的cDVGAN在复制三类数据特征方面优于四种基准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（CNN），在检测嵌入探测器噪声的样本时，其性能优于其他先进GAN模型生成的合成数据。与基准GAN的合成数据集相比，最优合成数据集的AUC（曲线下面积）性能提升高达4.2%。此外，使用cDVGAN生成的混合样本训练CNN，在识别嵌入LIGO探测器背景的真实样本时，优于仅使用标准类别训练的CNN（cDVGAN的AUC提升4%）。