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arXiv 2410.07855gr-qcastro-ph.IM
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深度学习模型在中等质量黑洞并合引力波搜索中对进动的鲁棒性

Robustness of Deep Learning Models to Precession in Gravitational-Wave Searches for Intermediate-Mass Black Hole Binaries

Quirijn Meijer, Marc van der Sluys, Sarah Caudill

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15 pages, 10 figures
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AI总结：

针对中等质量黑洞并合引力波搜索中信号易与探测器毛刺混淆、进动会扭曲波形的问题，本研究提出三种不同训练策略的神经网络分类器，其中课程学习训练的模型准确率达95%，并发现模型对进动的鲁棒性受训练策略影响。

摘要：

中等质量黑洞并合信号的引力波搜索受探测器毛刺（glitch）阻碍，由于这类系统质量大于恒星级质量系统，当前一代探测器无法观测到并合演化的旋近阶段，导致信号波形与持续时间、频段相近的毛刺高度相似。此外，双黑洞轨道平面的进动可能会进一步扭曲信号波形。本研究提出了三种用于区分信号与毛刺的神经网络分类器，每种分类器采用不同的训练策略，以研究进动对分类器的影响。尽管所有分类器都表现出极高的准确率，但表现最佳的分类器是基于课程学习（curriculum learning）原则训练的，即只有在掌握了较简单的前置样本后才引入新样本。该分类器在由注入O3 LIGO Hanford功率谱密度有色噪声的信号和毛刺组成的合成测试集上获得了约95%的准确率。研究将该模型与匹配滤波（matched filtering，即建模型引力波搜索的当前最优方法）进行了对比，并分析了模型对双黑洞参数的特定敏感性。结果发现，虽然分类器受系统总质量的影响，但误分类的预测结果最主要由信噪比决定的信号可见度决定。对三种分类器的分析表明，不同训练策略对进动的处理方式不同，这意味着该架构对进动的鲁棒性并不完备，可通过改进训练流程实现性能提升。

英文摘要：

Gravitational-wave searches for signals of intermediate-mass black hole binaries are hindered by detector glitches, as the increased masses from stellar-mass systems hinder current generation detectors from observing the inspiral phase of the binary evolution. This causes the waveforms to strongly resemble glitches, which are of similar duration within a similar frequency band. Additionally, precession of the orbital plane of a binary black hole may further warp signal waveforms. In this work three neural network-based classifiers for the task of distinguishing between signals and glitches are introduced, with each following different training regimes to study the impact of precession on the classifiers. Although all classifiers show highly accurate performance, the classifier found to perform best was trained following the principle of curriculum learning, where new examples are introduced only after the mastery of easier preceding examples. This classifier obtains an accuracy of approximately 95% on a synthetic test set consisting of signals and glitches injected into coloured noise from the O3 LIGO Hanford power spectral density. The model is compared to matched filtering, the state-of-the-art in modelled gravitational-wave searches, and analysed in search of particular sensitivities to black hole binary parameters. It was found that while the classifier is affected by the total mass of a system, the prediction of a misclassification is most strongly determined by visibility through the signal-to-noise ratio. The analysis of the three classifiers demonstrates that precession is handled differently depending on the training regime, meaning the architecture is not fully robust to precession and advancements can be made through the development of training routines.

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