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arXiv 2407.10263gr-qcastro-ph.HEastro-ph.IM
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基于神经后验估计的并合前旋进双中子星探测与表征

Pre-Merger Detection and Characterization of Inspiraling Binary Neutron Stars Derived from Neural Posterior Estimation

Wouter van Straalen, Alex Kolmus, Justin Janquart, Chris Van Den Broeck

AI总结:

本研究提出基于机器学习的双中子星并合前旋进阶段探测与分析框架,结合残差网络、分类器与归一化流网络实现神经后验估计,可随并合临近提升参数与天区定位精度,为多信使搜寻提供早期精确信息。

摘要:

随着国际引力波探测器网络灵敏度的提升,观测到双中子星信号的情况将愈发普遍。此外,由于这些信号的强度会更高,在其并合前探测到它们的概率也随之增加,但这需要一套高效的框架。本研究提出了一种基于机器学习的框架,能够在双中子星并合的旋进阶段对其进行探测与分析。该框架利用残差网络对应变数据进行特征提取,将其输出分别输入分类器以给出数据中存在信号的概率,同时输入归一化流网络以执行神经后验估计。我们针对信号达到的多个最大频率训练网络,以随时间推移提升估计效果。该框架在探测和表征方面均表现良好,且随着接近并合时刻,参数估计精度不断提升,从而能在并合临近时有效提高天区定位的精度。这种设置对于未来的多信使搜寻工作十分重要,此类研究需要尽可能早地获取最精确的信息。

英文摘要:

As the sensitivity of the international gravitational wave detector network increases, observing binary neutron star signals will become more common. Moreover, since these signals will be louder, the chances of detecting them before their mergers increase. However, this requires an efficient framework. In this work, we present a machine-learning-based framework capable of detecting and analyzing binary neutron star mergers during their inspiral. Using a residual network to summarize the strain data, we use its output as input to a classifier giving the probability of having a signal in the data, and to a normalizing-flow network to perform neural posterior estimation. We train a network for several maximum frequencies reached by the signal to improve the estimate over time. Our framework shows good results both for detection and characterization, with improved parameter estimation as we get closer to the merger time. Thus, we can effectively evolve the precision of the sky location as the merger draws closer. Such a setup would be important for future multi-messenger searches where one would like to have the most precise information possible, as early as possible.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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