Publishing statistical models: Getting the most out of particle physics experiments
Publishing statistical models: Getting the most out of particle physics experiments
复制标题
DOI：
10.21468/scipostphys.12.1.037
复制
发表时间：
2021-09
期刊：
影响因子：
5.5
通讯作者：
K. Cranmer;S. Kraml;H. Prosper;P. Bechtle;F. Bernlochner;I. Bloch;E. Canonero;M. Chrzaszcz;
中科院分区：
文献类型：
--
作者：
K. Cranmer;S. Kraml;H. Prosper;P. Bechtle;F. Bernlochner;I. Bloch;E. Canonero;M. Chrzaszcz;
用于导出实验分析结果的统计模型具有令人难以置信的科学价值，并且是分析保存和再利用的基本信息。在本文中，我们提出了系统地发布完整统计模型的科学案例，并讨论了使其实用化的技术发展。通过各种物理案例-包括部分子分布函数，希格斯玻色子测量，有效的场论解释，直接寻找新的物理，重口味的物理，直接暗物质探测，世界平均值，超越标准模型的全球拟合-我们说明了统计建模的详细信息如何增强实验结果的短期和长期影响。
The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases - including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits - we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.