标题： 卷积神经网络在真实LIGO数据中的信号检测应用 显示英文标题

标题： Convolutional Neural Networks for signal detection in real LIGO data

摘要： 在引力波探测器的数据中搜索来自致密双星合并的信号是一项计算需求极高的任务。最近，机器学习算法被提出以应对当前和未来的挑战。然而，由于评估程序的选择不同，这些出版物的结果往往差异很大。组织了机器学习引力波搜索挑战，以解决这些问题，并为机器学习搜索评估创建一个统一的框架。六个团队提交了贡献，其中四个基于机器学习方法，两个是最先进的生产分析。本文描述了来自耶拿弗里德里希·席勒大学（TPI FSU Jena）团队的提交及其更新版本。我们还将其算法应用于真实的O3b数据，并恢复了GWTC-3目录中的相关事件。 显示英文摘要

摘要： Searching the data of gravitational-wave detectors for signals from compact binary mergers is a computationally demanding task. Recently, machine learning algorithms have been proposed to address current and future challenges. However, the results of these publications often differ greatly due to differing choices in the evaluation procedure. The Machine Learning Gravitational-Wave Search Challenge was organized to resolve these issues and produce a unified framework for machine-learning search evaluation. Six teams submitted contributions, four of which are based on machine learning methods and two are state-of-the-art production analyses. This paper describes the submission from the team TPI FSU Jena and its updated variant. We also apply our algorithm to real O3b data and recover the relevant events of the GWTC-3 catalog.