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Distributed Computing Infrastructure for HERD experiment.

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Název: Distributed Computing Infrastructure for HERD experiment.
Autoři: Zhang, Xuantong, Jiang, Xiaowei, Cheng, Yaodong, Li, Zuhao
Zdroj: EPJ Web of Conferences; 10/7/2025, Vol. 337, p1-8, 8p
Abstrakt: The High Energy cosmic-Radiation Detection (HERD) facility is an under construction space astronomy and particle astrophysics experiment in collaboration between China and Europe, and will run on the China Space Station for more than 10 years since 2027. HERD is designed to search for dark matter with unprecedented sensitivity, investigate the century-old mystery of the origin of cosmic rays, conduct high-sensitivity surveys and monitoring of high-energy gamma rays, and explore new methods of pulsar navigation. Once operational, HERD experiment is expected to generate and distribute more than 90 PB of data over a span of 10 years. To share the data and computing resource for data processing and analysis, a distributed computing system based on grid computing technology is developed with the supports of computing and storage sites in China and Europe. HERD distributed computing infrastructure (DCI) integrates a distributed computing system managing data processing jobs based on DIRAC and dHTC, a distributed data management system distributing raw and produced data based on Rucio, and other grid computing middleware including IAM, FTS3, etc. Furthermore, HERD DCI is designed to be deeply involved with HERD Offline Software and data production workflow, providing grid computing services without normal and production users being aware of it. [ABSTRACT FROM AUTHOR]
Copyright of EPJ Web of Conferences is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Distributed Computing Infrastructure for HERD experiment.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Xuantong%22">Zhang, Xuantong</searchLink><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Xiaowei%22">Jiang, Xiaowei</searchLink><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Yaodong%22">Cheng, Yaodong</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Zuhao%22">Li, Zuhao</searchLink>
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  Data: EPJ Web of Conferences; 10/7/2025, Vol. 337, p1-8, 8p
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  Group: Ab
  Data: The High Energy cosmic-Radiation Detection (HERD) facility is an under construction space astronomy and particle astrophysics experiment in collaboration between China and Europe, and will run on the China Space Station for more than 10 years since 2027. HERD is designed to search for dark matter with unprecedented sensitivity, investigate the century-old mystery of the origin of cosmic rays, conduct high-sensitivity surveys and monitoring of high-energy gamma rays, and explore new methods of pulsar navigation. Once operational, HERD experiment is expected to generate and distribute more than 90 PB of data over a span of 10 years. To share the data and computing resource for data processing and analysis, a distributed computing system based on grid computing technology is developed with the supports of computing and storage sites in China and Europe. HERD distributed computing infrastructure (DCI) integrates a distributed computing system managing data processing jobs based on DIRAC and dHTC, a distributed data management system distributing raw and produced data based on Rucio, and other grid computing middleware including IAM, FTS3, etc. Furthermore, HERD DCI is designed to be deeply involved with HERD Offline Software and data production workflow, providing grid computing services without normal and production users being aware of it. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of EPJ Web of Conferences is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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