Abstract

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.

中文翻译：

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用于数值相对论目录的深度学习波形异常检测器

摘要

数值相对论对于研究紧凑双星聚结动力学、波形建模以及最终的引力波观测具有根本性的重要性。随着探测器网络灵敏度的提高，需要更精确的模板建模来保证更准确地估计天体物理参数。为了帮助提高数值相对论目录的准确性，我们开发了一种能够检测异常波形的深度学习模型。我们分析了 SXS 目录中具有不同质量比和自旋的 1341 个二元黑洞模拟，考虑到波形主导模式和更高模式。在分析的一组波形中，我们发现并分类了合并阶段中出现的七种异常类型。