标题： 一种集成到引力波爆发搜索中的自编码器神经网络，以提高噪声瞬态的排斥能力 显示英文标题

标题： An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients

摘要： 引力波（GW）探测器数据会受到短时的仪器或地球瞬变现象的影响，称为故障，这些故障可以模拟GW信号。 对于针对短持续时间GW瞬变源（GWT）的算法来说，减轻故障尤其困难，这些算法不依赖于GW波形模型来区分天体物理信号和噪声，例如相干波突发（cWB）。 这项工作是长期努力的一部分，旨在减轻cWB中的瞬变噪声，这导致引入了特定的估计器，以及基于机器学习的信号-噪声分类算法。 在这里，我们提出了一种集成到cWB中的自编码神经网络，它从GW时间序列中学习瞬变噪声的形态。 我们在已知的称为blip的故障家族上测试其性能。 当在最近观测期（O3b）的LIGO探测器数据上进行测试时，对通用GWT和双黑洞合并的灵敏度显著提高。 在每50年一次的误报率下，对于类似于blip故障的信号形态，灵敏度体积最多增加30%。 展望未来，这种工具可以适应分类可能影响未来观测运行的不同瞬变噪声类别，从而增强GWT搜索。 显示英文摘要

摘要： The gravitational-wave (GW) detector data are affected by short-lived instrumental or terrestrial transients, called glitches, which can simulate GW signals. Mitigation of glitches is particularly difficult for algorithms which target generic sources of short-duration GW transients (GWT), and do not rely on GW waveform models to distinguish astrophysical signals from noise, such as Coherent WaveBurst (cWB). This work is part of the long-term effort to mitigate transient noises in cWB, which led to the introduction of specific estimators, and a machine-learning based signal-noise classification algorithm. Here, we propose an autoencoder neural network, integrated into cWB, that learns transient noises morphologies from GW time-series. We test its performance on the glitch family known as blip. The resulting sensitivity to generic GWT and binary black hole mergers significantly improves when tested on LIGO detectors data from the last observation period (O3b). At false alarm rate of one event per 50 years the sensitivity volume increases up to 30% for signal morphologies similar to blip glitches. In perspective, this tool can adapt to classify different transient noise classes that may affect future observing runs, enhancing GWT searches.