标题： 使用循环去噪自编码器进行深度学习的引力波去噪 显示英文标题

标题： Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders

摘要： 引力波天文学是现代天体物理学中迅速发展的领域，经常由LIGO探测器进行观测。 引力波信号通常非常微弱，探测器（如LIGO）的数据中存在非高斯和非平稳的噪声，通常包含瞬态干扰，这些干扰可能会掩盖真实的信号。 传统的去噪方法，如主成分分析和字典学习，对于处理这种非高斯噪声并不理想，尤其是对于低信噪比的引力波信号。 此外，这些方法在大规模数据集上计算成本较高。 为了解决这些问题，我们应用了基于深度学习最新突破性进展的先进信号处理技术，以去除嵌入在高斯噪声或真实LIGO噪声中的引力波信号。 我们介绍了SMTDAE，一种基于序列到序列双向长短期记忆循环神经网络的阶梯式多时间步去噪自动编码器。 我们展示了使用我们无监督深度学习方法的优势，并表明，在仅使用模拟高斯噪声进行训练后，SMTDAE在嵌入真实非高斯LIGO噪声中的引力波信号中实现了优越的恢复性能。 显示英文摘要

摘要： Gravitational wave astronomy is a rapidly growing field of modern astrophysics, with observations being made frequently by the LIGO detectors. Gravitational wave signals are often extremely weak and the data from the detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary noise, often containing transient disturbances which can obscure real signals. Traditional denoising methods, such as principal component analysis and dictionary learning, are not optimal for dealing with this non-Gaussian noise, especially for low signal-to-noise ratio gravitational wave signals. Furthermore, these methods are computationally expensive on large datasets. To overcome these issues, we apply state-of-the-art signal processing techniques, based on recent groundbreaking advancements in deep learning, to denoise gravitational wave signals embedded either in Gaussian noise or in real LIGO noise. We introduce SMTDAE, a Staired Multi-Timestep Denoising Autoencoder, based on sequence-to-sequence bi-directional Long-Short-Term-Memory recurrent neural networks. We demonstrate the advantages of using our unsupervised deep learning approach and show that, after training only using simulated Gaussian noise, SMTDAE achieves superior recovery performance for gravitational wave signals embedded in real non-Gaussian LIGO noise.