Skip to main content

The Construction and Application of Data System of LHAASO Project

  • Chapter
  • First Online:
China’s e-Science Blue Book 2023
  • 46 Accesses

Abstract

The Large High Altitude Air Shower Observatory (LHAASO) is a major national science and technology infrastructure project. The project collects trillions of cosmic ray events every year, generating about 10 PB of data annually, providing valuable scientific data resources for physicists all over the world to explore the origin of high-energy cosmic rays, the related evolution of high-energy celestial bodies, and search for dark matter. In this paper, we firstly give a brief introduction to the LHAASO experiment including its detectors, and then explain the LHAASO data system in detail, including data acquisition system, data processing platform, data processing software, and scientific applications. Finally, we summarize the design and construction of LHAASO data system. It is expected that the experience could be useful to similar projects in future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cao Z, Chen MJ, Chen SZ et al (2019) Introduction to large high altitude air shower observatory (LHAASO). Chin Astron Astrophy 43(4):457–478

    Article  Google Scholar 

  2. Cao Z, Aharonian FA, An Q, et al (2021) Ultrahigh-energy photons up to 1.4 petaelectronvolts from 12 γ-ray Galactic sources. Nature 594(7861):33–36

    Google Scholar 

  3. Cao Z, Aharonian FA et al (2021) Peta-electron volt gamma-ray emission from the Crab Nebula. Science 373(6553):425–430

    Article  Google Scholar 

  4. Cao Z, Chen MJ, Chen SZ et al (2019) Introduction to Large High Altitude Air Shower Observatory (LHAASO). Acta Astronom Sinica 60(3):16

    Google Scholar 

  5. Gong G, Chen S, Du Q, et al (2011) Sub-nanosecond timing system design and development for LHAASO project. In: Proceedings of ICALEPCS2011, Grenoble, France

    Google Scholar 

  6. Moreira P, Serrano J, Wlostowski T, et al (2009) White rabbit: sub-nanosecond timing distribution over ethernet. In: 2009 international symposium on precision clock synchronization for measurement, control and communication. IEEE, pp 1–5

    Google Scholar 

  7. Gu M, Zhu KJ, Zhuang J et al (2013) Data acquisition software of LHAASO prototype system. Nucl Electron Detect Technol 33(5):5

    Google Scholar 

  8. Schwan P (2003) Lustre: Building a file system for 1,000-node clusters. In: Proceedings of the Linux symposium

    Google Scholar 

  9. Peters AJ, Sindrilaru E, Adde G (2015) EOS as the present and future solution for data storage at CERN. J Phys: Conf Ser 664(4):042042

    Google Scholar 

  10. Guthmundsson O, Da Silva J, Guomundsson O (1993) The Amanda network backup manager. In: Proceedings of USENIX systems administration (LISA VII) conference

    Google Scholar 

  11. Baud JP, Barring O, Durand JD (2000) CASTOR project status. In: CHEP 2000, Padova, February

    Google Scholar 

  12. Cano E, Bahyl V, Caffy C, et al. (2020) CERN Tape Archive: production status, migration from CASTOR and new features. In: EPJ web of conferences, vol 245. EDP Sciences

    Google Scholar 

  13. Bitzes G, Sindrilaru EA, Peters AJ (2019) Scaling the EOS namespace–new developments, and performance optimizations. In: EPJ web of conferences. EDP Sciences, vol 214. p 04019

    Google Scholar 

  14. Ongaro D, Ousterhout J (2014) In search of an understandable consensus algorithm. In: 2014 USENIX annual technical conference (Usenix ATC 14), pp 305–319

    Google Scholar 

  15. Gu J, Wu C, Xie X, et al (2019) Optimizing the parity check matrix for efficient decoding of rs-based cloud storage systems. In: 2019 IEEE international parallel and distributed processing symposium (IPDPS). IEEE, pp 533–544

    Google Scholar 

  16. Cheng Z, Wang L, Cheng Y, et al (2020) Heat prediction of high energy physical data based on LSTM recurrent neural network. In: EPJ Web of Conferences. EDP Sciences 245, p 04002

    Google Scholar 

  17. Cheng ZJ, Cheng YD, Chen G et al (2020) High energy physics data placement strategy based on random forest. Comput Eng Appl 56(21):60–64

    Google Scholar 

  18. Fajardo EM, Dost JM, Holzman B et al (2015) How much higher can HTCondor fly? J Phys: Conf Ser IOP Publ 664(6):062014

    Google Scholar 

  19. Rosado T, Bernardino J (2014) An overview of openstack architecture. In: Proceedings of the 18th international database engineering & applications symposium. pp 366–367

    Google Scholar 

  20. Bernstein D (2014) Containers and cloud: from lxc to docker to kubernetes. IEEE Cloud Comput 1(3):81–84

    Article  Google Scholar 

  21. Kurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5):e0177459

    Article  Google Scholar 

  22. Blomer J, Canal P, Naumann A, et al (2020) Evolution of the ROOT Tree I/O. In: EPJ web of conferences. EDP Sciences, vol 245, 02030

    Google Scholar 

  23. Barthelmy SD, Cline TL, Butterworth P, et al (2000) GRB coordinates network (GCN): a status report. In: AIP conference proceedings. American Institute of Physics, vol 526, no 1. pp 731–735

    Google Scholar 

  24. Burgess JM, Vianello G (2016) The multi-mission maximum likelihood framework (3ML). In: Eighth Huntsville gamma-ray burst symposium, vol 1962. p 4110

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yaodong Cheng .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 Publishing House of Electronics Industry

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cao, Z., Yao, Z., Gu, M., Cheng, Y. (2024). The Construction and Application of Data System of LHAASO Project. In: China’s e-Science Blue Book 2023. Springer, Singapore. https://doi.org/10.1007/978-981-99-8270-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8270-7_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8269-1

  • Online ISBN: 978-981-99-8270-7

  • eBook Packages: Social SciencesSocial Sciences (R0)

Publish with us

Policies and ethics