基于GPU的多维振幅分析寻找四夸克候选
A GPU based multidimensional amplitude analysis to search for tetraquark candidates
摘要：
实验高能物理对计算资源的需求正在稳步增加，因为当前的对撞机实验继续积累大量的数据，物理学家沉迷于更复杂和雄心勃勃的分析策略。这在强子光谱学和味物理学领域尤其如此，在这些领域中，分析通常依赖于复杂的多维未分组最大似然拟合，具有几十个自由参数，目的是研究强子的内部结构。图形处理单元（GPU）代表了最复杂和通用的并行计算架构之一，其正成为高能物理学家满足其计算需求的流行工具包。GooFit是一款即将推出的开源工具，将ROOT/RooFit与NVIDIA GPU上的CUDA平台连接起来，充当了ARMIT最小化算法和并行处理器之间的桥梁，允许同时在多个内核上估计概率密度函数。在这篇文章中，一个成熟的振幅分析框架开发使用GooFit的速度和可靠性进行了测试。四维fitter框架是GooFit上首次建立的框架之一，旨在寻找$B^0 \rightarrow J/ψK π$衰变中的奇异四夸克态，也可以无缝地适用于其他类似的分析。在GPU上运行的GooFit fitter与在多核CPU集群上运行的相同分析的ROOT/RooFit实现相比，在计算速度上有了显着的提高。此外，它显示出对整体拟合贡献较小的组件的敏感性。它有可能成为敏感和计算密集型物理分析的强大工具。
英文摘要：
The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with an aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the $B^0 \rightarrow J/ψK π$ decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
