MAUS: the MICE analysis user software
MAUS: the MICE analysis user software
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DOI：
10.1088/1748-0221/14/04/t04005
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发表时间：
2018-12
影响因子：
1.3
通讯作者：
R. Asfandiyarov;R. Bayes;V. Blackmore;M. Bogomilov;D. Colling;A. Dobbs;F. Drielsma;M. Drews;M. Ellis;M. Fedorov;P. Franchini;R. Gardener;J. R. Greis;P. Hanlet;C. Heidt;C. Hunt;G. Kafka;Y. Karadzhov;A. Kurup;P. Kyberd;M. Littlefield;Ao Liu;K. Long;K. Long;D. Maletic;J. Martyniak;S. Middleton;T. Mohayai;T. Mohayai;J. Nebrensky;J. Nugent;E. Overton;V. Pěč;C. Pidcott;D. Rajaram;M. Rayner;I. Reid;C. Rogers;E. Santos;M. Savic;I. Taylor;Y. Torun;C. Tunnell;M. Uchida;V. Verguilov;K. Walaron;M. Winter;S. Wilbur
中科院分区：
文献类型：
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作者：
R. Asfandiyarov;R. Bayes;V. Blackmore;M. Bogomilov;D. Colling;A. Dobbs;F. Drielsma;M. Drews;M. Ellis;M. Fedorov;P. Franchini;R. Gardener;J. R. Greis;P. Hanlet;C. Heidt;C. Hunt;G. Kafka;Y. Karadzhov;A. Kurup;P. Kyberd;M. Littlefield;Ao Liu;K. Long;K. Long;D. Maletic;J. Martyniak;S. Middleton;T. Mohayai;T. Mohayai;J. Nebrensky;J. Nugent;E. Overton;V. Pěč;C. Pidcott;D. Rajaram;M. Rayner;I. Reid;C. Rogers;E. Santos;M. Savic;I. Taylor;Y. Torun;C. Tunnell;M. Uchida;V. Verguilov;K. Walaron;M. Winter;S. Wilbur
Muon电离冷却实验(MICES)合作开发了MICES分析用户软件(MAUS)来模拟和分析实验数据。它作为实验的主要代码库，提供离线批量模拟和重建以及在线数据质量检查。该软件既提供传统的粒子物理功能，如轨迹重建和粒子识别，也提供加速器物理功能，如计算转移矩阵和发射率。代码设计是面向对象的，但具有基于Map-Reduce模型的顶层结构。这允许并行化以支持数据获取操作期间的实时数据重建。MAUS允许用户使用Python或C++进行开发，并为两者提供API。还使用业界的各种软件工程实践来确保代码的正确性和可维护性，包括样式、单元和集成测试、持续集成和负载测试、代码审查和分布式版本控制。描述了系统的软件框架以及仿真和重构功能。
The Muon Ionization Cooling Experiment (MICE) collaboration has developed the MICE Analysis User Software (MAUS) to simulate and analyze experimental data. It serves as the primary codebase for the experiment, providing for offline batch simulation and reconstruction as well as online data quality checks. The software provides both traditional particle-physics functionalities such as track reconstruction and particle identification, and accelerator physics functions, such as calculating transfer matrices and emittances. The code design is object orientated, but has a top-level structure based on the Map-Reduce model. This allows for parallelization to support live data reconstruction during data-taking operations. MAUS allows users to develop in either Python or C++ and provides APIs for both. Various software engineering practices from industry are also used to ensure correct and maintainable code, including style, unit and integration tests, continuous integration and load testing, code reviews, and distributed version control. The software framework and the simulation and reconstruction capabilities are described.