English

MAUS: The MICE Analysis User Software

Computational Physics 2019-07-31 v4

Abstract

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.

Keywords

Cite

@article{arxiv.1812.02674,
  title  = {MAUS: The MICE Analysis User Software},
  author = {R. Asfandiyarov and R. Bayes and V. Blackmore and M. Bogomilov and D. Colling and A. J. Dobbs and F. Drielsma and M. Drews and M. Ellis and M. Fedorov and P. Franchini and R. Gardener and J. R. Greis and P. M. Hanlet and C. Heidt and C. Hunt and G. Kafka and Y. Karadzhov and A. Kurup and P. Kyberd and M. Littlefield and A. Liu and K. Long and D. Maletic and J. Martyniak and S. Middleton and T. Mohayai and J. J. Nebrensky and J. C. Nugent and E. Overton and V. Pec and C. E. Pidcott and D. Rajaram and M. Rayner and I. D. Reid and C. T. Rogers and E. Santos and M. Savic and I. Taylor and Y. Torun and C. D. Tunnell and M. A. Uchida and V. Verguilov and K. Walaron and M. Winter and S. Wilbur},
  journal= {arXiv preprint arXiv:1812.02674},
  year   = {2019}
}
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