Collaborative Research: Disciplinary Improvements: FAIROS-HEP, a Research Coordination Network for Particle Physics

FAIROS-HEP研究协调网络将促进采用做法和网络基础设施，以便能够重复使用和重新解释高能物理(HEP)数据集。该项目试图培育的一个关键机制是“活出版物”，即分布式对象，其中科学结果的描述、它们所基于的数据以及用于生成它们的计算程序都可以用于检查、复制和重复使用。区域协调委员会将促进的这一机制和其他机制，将通过一个支持出版物、数据、软件、分析和工作流程保存的综合基础设施，促进HEP研究产品的分布式、非同步再利用。FAIROS-HEP建立在扩展HEP社区的利益相关者之间的长期合作基础上。在粒子物理方面，“活出版物”将提供：(1)已公布的可用于组合和参数重新解释的似然函数和统计模型(其中可能包括指定EFT算符后的EFT(有效场论)分析)，(2)与REANA(可重复研究数据分析)兼容的分析工作流程，(3)允许运动学重新解释的分析工作流程的重铸界面(这更通用，涵盖所有EFT分析以及直接预测新粒子的理论测试)。(2)中保留的分析也将简化实验希望派生分析和实施更改(即DIFS)的过程，而不是从头开始。这项由高级网络基础设施办公室颁发的奖项由数学和物理科学理事会联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The FAIROS-HEP research coordination network will foster the adoption of practices and cyberinfrastructure to enable reuse and reinterpretation of high energy physics (HEP) datasets. A key mechanism that the project seeks to cultivate is that of “living publications”, distributed objects where the description of scientific results, the data on which they are based, and the computational procedures used to generate them are all available for examination, reproduction, and reuse. This and other mechanisms which the RCN would foster would facilitate distributed, asynchronous reuse of HEP research products, by means of a combined infrastructure supporting publications, data, software, analysis and workflow preservation. FAIROS-HEP builds on longstanding collaboration among the stakeholders of the extended HEP community. In the context of particle physics, a “living publication” will provide: (1) published likelihood functions and statistical models that can be used for combinations and parametric reinterpretation (which could include EFT (effective field theory) analysis once the EFT operators have been specified), (2) analysis workflows compatible with REANA (Reproducible Research Data Analysis), (3) a recast interface to the analysis workflow that would allow for kinematic reinterpretation (which is more general and encompasses all EFT analyses as well as tests of theories that predict new particles directly produced). The preserved analyses in (2) would also streamline the process by which an experiment would like to fork an analysis and implement changes (i.e. diffs) instead of starting from scratch.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for Mathematical and Physical Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.