基于近邻拟合的级联粒子衰变多变量振幅分析
Multivariate amplitude analysis of the cascade particle decays based on the Nearest Neighbors fitting
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中文总结 AI 辅助
本研究将似然的近邻估计方法应用于级联粒子衰变的多变量振幅分析，无需解析概率密度函数，可准确考虑重建效应，仅需适度CPU资源，还展示了B介子衰变振幅分析的玩具示例。
中文摘要 AI 辅助
将似然的近邻估计方法应用于级联粒子衰变的多变量振幅分析。该方法中，用于描述数据的蒙特卡罗模拟事件被赋予权重，权重取决于理论拟合模型的参数；每个此类加权事件在参数空间的邻域内被视为对N维概率密度函数有贡献。此方法无需解析概率密度函数，也无需在每次拟合步骤中耗费CPU资源重新生成N维概率密度函数，可准确考虑重建效应，仅需适度的CPU资源，且能有效实现多线程。本文展示了拟合的通用设置以及B介子衰变振幅分析的玩具示例。
英文摘要
The Nearest Neighbors estimation of likelihood is implemented for the multivariate amplitude analysis of the cascades of particle decays. In this approach Monte Carlo simulated events that are used to describe data are assigned weights dependent on the parameters of the theoretical fitting model. Each of such weighted events is considered as contributing to N-dimensional probability density function in it's neighborhood in the parameter space. No analytic p.d.f. or cpu-intensive re-generation of N-dimensional p.d.f. at each fit step are required. The method allows accurate accounting for reconstruction effects, demands modest cpu resources and can be effectively multi-threaded. General setup of the fit as well as toy example of the amplitude analysis of B-meson decays are demonstrated.
