论文标题
波形：重力波数据的基于变压器的denoising方法
WaveFormer: transformer-based denoising method for gravitational-wave data
论文作者
论文摘要
随着引力波天文学的出现以及发现更紧凑的二元融合的发现，需要数据质量改进技术来处理重力波（GW）观察数据中的复杂噪音。尽管最近基于机器学习的研究显示了数据降解的有希望的结果，但它们无法精确地恢复GW信号振幅和相位。为了解决这一问题，我们开发了一个深神经网络中心的工作流，波形器，以从激光干涉仪重力 - 重力波天文台（LIGO）对观测数据进行明显的噪声抑制和信号恢复。波形器具有科学驱动的架构设计，并在广泛的频率范围内具有分层特征提取。结果，整体噪声和故障降低了一个以上的数量级，并且信号恢复误差分别为1％和7％，分别为7％。此外，在75个报道的Ligo二进制黑洞（BBH）事件上，我们获得了反向误报率的显着提高。我们的工作强调了大型神经网络在引力波数据分析中的潜力，而主要在Ligo数据上证明了其适应性设计，但它表明了在未来的观察过程中在国际重力波观测网络（IGWN）中更广泛应用的希望。
With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and overwhelming noise in gravitational wave (GW) observational data. Though recent machine learning-based studies have shown promising results for data denoising, they are unable to precisely recover both the GW signal amplitude and phase. To address such an issue, we develop a deep neural network centered workflow, WaveFormer, for significant noise suppression and signal recovery on observational data from the Laser Interferometer Gravitational-Wave Observatory (LIGO). The WaveFormer has a science-driven architecture design with hierarchical feature extraction across a broad frequency spectrum. As a result, the overall noise and glitch are decreased by more than one order of magnitude and the signal recovery error is roughly 1% and 7% for the phase and amplitude, respectively. Moreover, on 75 reported binary black hole (BBH) events of LIGO we obtain a significant improvement of inverse false alarm rate. Our work highlights the potential of large neural networks in gravitational wave data analysis and, while primarily demonstrated on LIGO data, its adaptable design indicates promise for broader application within the International Gravitational-Wave Observatories Network (IGWN) in future observational runs.
