标题： 使用数据引导的异常信号模型进行引力波信号和异常的联合推断 显示英文标题

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

摘要： 引力波数据经常受到非高斯噪声瞬态，即故障（glitches）的污染，这可能导致对天体物理信号参数的推断产生偏差。传统的做法是在预处理步骤中减去故障，或者可以从一种无偏的子波基函数（例如BayesWave）中包含一个故障模型。在本工作中，我们引入了一种基于机器学习的方法来构建故障的参数化模型。我们在Gravity Spy目录中的已知故障上训练了一个归一化流，构建了对故障模型的有信息的先验。通过将此模型纳入使用Bilby的贝叶斯推断分析中，我们同时估计故障和信号参数。我们通过在真实故障上的偏差减少、故障识别和贝叶斯模型选择来展示我们方法的性能。我们的结果表明，这种方法有效地从数据中移除了故障，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.