论文标题
深度学习波形异常检测器的数值相对性目录
Deep learning waveform anomaly detector for numerical relativity catalogs
论文作者
论文摘要
数值相对论对于研究紧凑的二元合并动力学，波形建模以及重力波观测至关重要。随着检测器网络的敏感性的提高，需要更精确的模板建模，以确保对天体物理参数的更准确估计。为了帮助提高数字相对性目录的准确性，我们开发了一个能够检测异常波形的深度学习模型。我们分析了来自SXS目录的1341个二进制黑洞模拟，并考虑了波形优势和较高模式的各种质量比率和自旋。在分析的波形集合中，我们发现并分类了在聚结阶段出现的七种类型的异常。
Numerical Relativity has been of fundamental importance for studying compact binary coalescence dynamics, waveform modelling, and eventually for gravitational waves observations. As the sensitivity of the detector network improves, more precise template modelling will be necessary to guarantee a more accurate estimation of astrophysical parameters. To help improve the accuracy of numerical relativity catalogs, we developed a deep learning model capable of detecting anomalous waveforms. We analyzed 1341 binary black hole simulations from the SXS catalog with various mass-ratios and spins, considering waveform dominant and higher modes. In the set of waveform analyzed, we found and categorised seven types of anomalies appearing in the coalescence phases.
