MPD Data Lab: Towards the Modern Data Analysis Framework for the MPD Experiment

IF 0.6 4区 物理与天体物理 Q4 PHYSICS, PARTICLES & FIELDS Physics of Particles and Nuclei Pub Date : 2024-08-18 DOI:10.1134/s1063779624700680
J. Buša, A. Bychkov, S. Hnatič, A. Krylov, V. Krylov, O. Rogachevsky
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Abstract

MPDRoot is an off-line software framework for simulation, reconstruction, and physical analysis of the simulated or experimental data for MPD experiment at NICA collider. The experiment is projected to run for a few decades and to obtain ~108 events of heavy ion collisions, collecting the data for physics analysis at the 100 PB scale. For overall experiment success it is imperative to have state of the art data analysis software, which integrates best of available latest technologies, while adhering to time-proven, most effective development methodologies. In this paper, we introduce the MPD Data Lab—the technological integration of Acceptance Test Driven Development and Rapid Development concepts into the MPDRoot framework. At the beginning, we standardized the existing codebase by designing and writing API. This was a necessary step to be able to plug-in the external diagnostic software entities and to make the in-depth comparison of different realizations of the reconstruction modules possible. The logic of the diagnostics is encapsulated into the separate controller—the QA Engine, while its visualization is provided by JupyterLab framework. We show how full integration of MPDRoot’s libraries into JupyterLab enables to use the power of rapid development provided by JupyterLab technology to enhance productivity by fast prototyping of MPDRoot’s algorithms. The combination of these technologies together with the existing development environment form a software complex, providing means to accomplish the long term strategic objectives—competent software development with reliable quality control and algorithm innovation.

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MPD 数据实验室:为 MPD 实验建立现代数据分析框架
摘要MPDRoot 是一个离线软件框架,用于模拟、重建和物理分析 NICA 对撞机 MPD 实验的模拟或实验数据。预计该实验将运行几十年,获得约 108 个重离子碰撞事件,收集的数据将用于 100 PB 规模的物理分析。为了使整个实验取得成功,必须拥有最先进的数据分析软件,该软件集成了现有的最佳最新技术,同时遵循经过时间验证的最有效的开发方法。在本文中,我们将介绍 MPD 数据实验室--将验收测试驱动开发和快速开发理念融入 MPDRoot 框架的技术集成。一开始,我们通过设计和编写应用程序接口(API)对现有代码库进行了标准化。这是必要的一步,以便能够插入外部诊断软件实体,并对重建模块的不同实现方式进行深入比较。诊断的逻辑封装在单独的控制器--QA 引擎中,而其可视化则由 JupyterLab 框架提供。我们展示了如何将 MPDRoot 的库完全集成到 JupyterLab 中,从而利用 JupyterLab 技术提供的快速开发能力,通过快速建立 MPDRoot 算法原型来提高生产率。这些技术与现有开发环境的结合形成了一个软件综合体,为实现长期战略目标--具有可靠质量控制和算法创新能力的软件开发--提供了手段。
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来源期刊
Physics of Particles and Nuclei
Physics of Particles and Nuclei 物理-物理:粒子与场物理
CiteScore
1.00
自引率
0.00%
发文量
116
审稿时长
6-12 weeks
期刊介绍: The journal Fizika Elementarnykh Chastits i Atomnogo Yadr of the Joint Institute for Nuclear Research (JINR, Dubna) was founded by Academician N.N. Bogolyubov in August 1969. The Editors-in-chief of the journal were Academician N.N. Bogolyubov (1970–1992) and Academician A.M. Baldin (1992–2001). Its English translation, Physics of Particles and Nuclei, appears simultaneously with the original Russian-language edition. Published by leading physicists from the JINR member states, as well as by scientists from other countries, review articles in this journal examine problems of elementary particle physics, nuclear physics, condensed matter physics, experimental data processing, accelerators and related instrumentation ecology and radiology.
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