NUISANCE: a neutrino cross-section generator tuning and comparison framework

作     者：Stowell, P. Wret, C. Wilkinson, C. Pickering, L. Cartwright, S. Hayato, Y. Mahn, K. McFarland, K. S. Sobczyk, J. Terri, R. Thompson, L. Wascko, M. O. Uchida, Y. 

作者机构：Univ Sheffield Dept Phys & Astron Sheffield S Yorkshire England Imperial Coll London Dept Phys London England Univ Bern Albert Einstein Ctr Fundamental Phys High Energy Phys Lab Bern Switzerland Univ Tokyo Todai Inst Adv Study Kavli Inst Phys & Math Universe WPI Kashiwa Chiba Japan Univ Tokyo Inst Cosm Ray Res Kamioka Observ Kamioka Akita Japan Michigan State Univ Dept Phys & Astron E Lansing MI 48824 USA Univ Rochester Dept Phys & Astron Rochester NY 14627 USA Univ Wroclaw Inst Theoret Phys Wroclaw Poland Queen Mary Univ London Sch Phys & Astron London England 

出 版 物：《JOURNAL OF INSTRUMENTATION》 (仪表制造杂志)

年 卷 期：2017年第12卷第1期

页      面：01016-01016页

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 

基　　金：MEXT, Japan National Science Centre (NCN), Poland MINECO, Spain ERDF funds, Spain SNSF, Switzerland SER, Switzerland STFC, U.K DOE, U.S.A DOE Alfred P. Sloan Foundation Grants-in-Aid for Scientific Research Funding Source: KAKEN STFC [ST/H000992/1, ST/K001604/1, ST/H000992/2, ST/N000277/1, T2K, ST/N000242/1] Funding Source: UKRI 

主　　题：Software architectures (event data models, frameworks and databases) Analysis and statistical methods Simulation methods and programs Data processing methods 

摘      要：NUISANCE is an open source C++ framework which facilitates detailed studies of neutrino interaction cross- section models implemented in Monte Carlo neutrino event generators. It provides a host of automated methods to perform comparisons of multiple generators to published cross- section measurements and each other. External reweighting libraries are used to allow the end- user to evaluate the impact of model parameters variations in the generators with data, or to tune the generator predictions to arbitrary dataset combinations. The design is modular and focusses on ease- of- use to allow new datasets and more generators to be added without requiring detailed understanding of the entire NUISANCE package. We discuss the motivation for the NUISANCE framework and suggested usage cases, alongside a description of its core structure.