| Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
|---|---|---|
| Article Number | 03038 | |
| Number of page(s) | 6 | |
| Section | 3 - Middleware and Distributed Computing | |
| DOI | https://doi.org/10.1051/epjconf/202024503038 | |
| Published online | 16 November 2020 | |
https://doi.org/10.1051/epjconf/202024503038
China-EU scientific cooperation on JUNO distributed computing
1
Istituto Nazionale di Fisica Nucleare (INFN), Catania division
2
on behalf of JUNO collaboration
* e-mail: giuseppe.andronico@ct.infn.it
Published online: 16 November 2020
The Jiangmen Underground Neutrino Observatory (JUNO) is an underground 20 kton liquid scintillator detector being built in the south of China. Targeting an unprecedented relative energy resolution of 3% at 1 MeV, JUNO will be able to study neutrino oscillation phenomena and determine neutrino mass ordering with a statistical significance of 3-4 sigma within six years running time.
These physics challenges are addressed by a large Collaboration localized in three continents. In this context, key to the success of JUNO will be the realization of a distributed computing infrastructure to fulfill foreseen computing needs.
Computing infrastructure development is performed jointly by the Institute for High Energy Physics (IHEP) (part of Chinese Academy of Sciences (CAS)), and a number of Italian, French and Russian data centers, already part of WLCG (Worldwide LHC Computing Grid).
Upon its establishment, JUNO is expected to deliver not less than 2 PB of data per year, to be stored in the data centers throughout China and Europe. Data analysis activities will be also carried out in cooperation.
This contribution is meant to report on China-EU cooperation to design and build together the JUNO computing infrastructure and to describe its main characteristics and requirements.
© 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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