标题： 基于数据驱动的杂波模型对引力波信号和杂波进行联合推理 显示英文标题

标题： Joint inference for gravitational wave signals and glitches using a data-informed glitch model

摘要： 引力波数据常常受到非高斯噪声瞬变的污染，这些瞬变称为“伪迹”，可能会使天体物理信号参数的推断产生偏差。传统方法要么在预处理步骤中减去伪迹，要么从一种无偏的小波基（例如 BayesWave）中引入伪迹模型。在本工作中，我们介绍了一种基于机器学习的方法来构建伪迹的参数化模型。我们在已知的来自Gravity Spy目录中的伪迹上训练了一个归一化流，从而在伪迹模型上构建了一个信息性先验。通过将此模型与Bilby结合进行贝叶斯推理分析，我们能够同时估计伪迹和信号参数。我们通过减少偏差、伪迹识别和真实伪迹上的贝叶斯模型选择来展示我们方法的表现。我们的结果显示这种方法有效地从数据中消除了伪迹，显著提高了源参数估计，并减少了偏差。 显示英文摘要

摘要： Gravitational wave data are often contaminated by non-Gaussian noise transients, glitches, which can bias the inference of astrophysical signal parameters. Traditional approaches either subtract glitches in a pre-processing step, or a glitch model can be included from an agnostic wavelet basis (e.g. BayesWave). In this work, we introduce a machine-learning-based approach to build a parameterised model of glitches. We train a normalising flow on known glitches from the Gravity Spy catalogue, constructing an informative prior on the glitch model. By incorporating this model into the Bayesian inference analysis with Bilby, we estimate glitch and signal parameters simultaneously. We demonstrate the performance of our method through bias reduction, glitch identification and Bayesian model selection on real glitches. Our results show that this approach effectively removes glitches from the data, significantly improving source parameter estimation and reducing bias.