论文标题
通过生成对抗网络在重力波检测器数据中生成瞬态噪声伪影
Generating transient noise artifacts in gravitational-wave detector data with generative adversarial networks
论文作者
论文摘要
重力波检测器数据中的瞬态噪声故障限制了搜索的灵敏度和污染检测的信号。在本文中，我们展示了如何使用生成对抗网络模拟小故障。我们为Ligo，Kagra和处女座探测器中看到的22种最常见的故障类型生成数百个合成图像。人造故障可用于改善搜索和参数估计算法的性能。我们执行神经网络分类，以表明我们的人造故障是真实故障的绝佳匹配，所有22种毛刺类型的99.0％的平均分类精度。
Transient noise glitches in gravitational-wave detector data limit the sensitivity of searches and contaminate detected signals. In this Paper, we show how glitches can be simulated using generative adversarial networks. We produce hundreds of synthetic images for the 22 most common types of glitches seen in the LIGO, KAGRA, and Virgo detectors. The artificial glitches can be used to improve the performance of searches and parameter-estimation algorithms. We perform a neural network classification to show that our artificial glitches are an excellent match for real glitches, with an average classification accuracy across all 22 glitch types of 99.0%.
