论文标题
贝叶斯推断以在非共鸣背景下研究具有两个或多个衰减颗粒的信号
Bayesian inference to study a signal with two or more decaying particles in a non-resonant background
论文作者
论文摘要
我们研究了贝叶斯方法的应用，以从数据中提取相关信息，该信息由两个或多个腐烂粒子及其背景组成。该方法利用在事件级别衰减产物的分布中存在的依赖性，并处理整个样品的信息，以推断混合物分数以及信号和背景分布的相关参数。该算法通常需要对后验进行数值计算，我们在简化的$ pp \ to hh \ to hh \ to b \ babbγγ$ search的基准测试场景中明确锻炼。我们对结果进行后验预测检查，并显示了如何从样本中正确提取信号分数，以及信号和背景分布中的许多参数。提出的框架可用于其他搜索，例如$ pp \ to zz，\ ww，\ zw $和一对leptokarks等。
We study the application of a Bayesian method to extract relevant information from data for the case of a signal consisting of two or more decaying particles and its background. The method takes advantage of the dependence that exists in the distributions of the decaying products at the event-by-event level and processes the information for the whole sample to infer the mixture fraction and the relevant parameters for signal and background distributions. The algorithm usually needs a numerical computation of the posterior, which we work out explicitly in a benchmark scenario of a simplified $pp\to hh \to b\bar b γγ$ search. We perform a posterior predictive check on the results and we show how the signal fraction is correctly extracted from the sample, as well as many parameters in the signal and background distributions. The presented framework could be used for other searches such as $pp\to ZZ,\ WW,\ ZW$ and pair of Leptoquarks, among many others.
