| Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
|---|---|---|
| Article Number | 07011 | |
| Number of page(s) | 6 | |
| Section | 7 - Facilities, Clouds and Containers | |
| DOI | https://doi.org/10.1051/epjconf/202024507011 | |
| Published online | 16 November 2020 | |
https://doi.org/10.1051/epjconf/202024507011
Jupyter-based service for JUNO analysis
Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
* e-mail: lintao@ihep.ac.cn
Published online: 16 November 2020
The JUNO (Jiangmen Underground Neutrino Observatory) is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters. The estimated data volume of raw data is about 2 PB/year. The event rate of reactor anti-neutrinos is about 60/day, while the event rate of background is about O(10) Hz. The challenge is the event correlation during the analysis, where the background events could not be discarded. In order to use big data techniques to search for rare events, a Jupyter-based interactive service is developed for JUNO analysis.
In this paper, an overview of this service is presented. The infrastructure is based on Jupyter and Kubernetes, which provides the user interface and resource management. In order to integrate the data processing framework and big data techniques, an index file is used as an intermediate file, which points to the interested events. Data processing framework SNiPER is used to select the candidate of neutrino signals and produce the index file. Apache Spark is then used to process such index file repeatedly with data cached in memory. With the index file produced from Spark and the complete event data files, SNiPER is used to process them and produce the final physics result. At the end of paper, the test-bed is presented and the testing result is shown.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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