Building and steering binned template fits with cabinetry
Building and steering binned template fits with cabinetry
复制标题
建筑和转向箱模板适合橱柜
DOI：
10.1051/epjconf/202125103067
复制
发表时间：
2021
影响因子：
--
通讯作者：
Held, Alexander
中科院分区：
文献类型：
--
作者：
Cranmer, Kyle;Held, Alexander
cabinetry库提供了一个基于Python的解决方案，用于构建和控制装箱模板拟合。它与pythonic High Energy Physics生态系统紧密集成，特别是与pyhf进行统计推断。cabinetry使用声明性方法来构建统计模型，并使用JSON模式描述可能的配置选择。此外，还可以通过自定义代码提供模型构建指令，在适用于工作流程的关键步骤时自动执行。该库实现了用于执行最大似然拟合、参数上限确定和发现重要性计算的接口。cabinetry还提供了一系列实用程序来研究和传播拟合结果。这些包括拟合模型和数据的可视化、模板直方图和拟合结果的可视化、干扰参数的影响排名、拟合优度计算和可能性扫描。该库采用模块化方法，允许用户将其部分或全部功能纳入其工作流程。
The cabinetry library provides a Python-based solution for building and steering binned template fits. It tightly integrates with the pythonic High Energy Physics ecosystem, and in particular with pyhf for statistical inference. cabinetry uses a declarative approach for building statistical models, with a JSON schema describing possible configuration choices. Model building instructions can additionally be provided via custom code, which is automatically executed when applicable at key steps of the workflow. The library implements interfaces for performing maximum likelihood fitting, upper parameter limit determination, and discovery significance calculation. cabinetry also provides a range of utilities to study and disseminate fit results. These include visualizations of the fit model and data, visualizations of template histograms and fit results, ranking of nuisance parameters by their impact, a goodness-of-fit calculation, and likelihood scans. The library takes a modular approach, allowing users to include some or all of its functionality in their workflow.