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.
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References
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
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
Cao Z, Aharonian FA et al (2021) Peta-electron volt gamma-ray emission from the Crab Nebula. Science 373(6553):425–430
Cao Z, Chen MJ, Chen SZ et al (2019) Introduction to Large High Altitude Air Shower Observatory (LHAASO). Acta Astronom Sinica 60(3):16
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
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
Gu M, Zhu KJ, Zhuang J et al (2013) Data acquisition software of LHAASO prototype system. Nucl Electron Detect Technol 33(5):5
Schwan P (2003) Lustre: Building a file system for 1,000-node clusters. In: Proceedings of the Linux symposium
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
Guthmundsson O, Da Silva J, Guomundsson O (1993) The Amanda network backup manager. In: Proceedings of USENIX systems administration (LISA VII) conference
Baud JP, Barring O, Durand JD (2000) CASTOR project status. In: CHEP 2000, Padova, February
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
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
Ongaro D, Ousterhout J (2014) In search of an understandable consensus algorithm. In: 2014 USENIX annual technical conference (Usenix ATC 14), pp 305–319
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
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
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
Fajardo EM, Dost JM, Holzman B et al (2015) How much higher can HTCondor fly? J Phys: Conf Ser IOP Publ 664(6):062014
Rosado T, Bernardino J (2014) An overview of openstack architecture. In: Proceedings of the 18th international database engineering & applications symposium. pp 366–367
Bernstein D (2014) Containers and cloud: from lxc to docker to kubernetes. IEEE Cloud Comput 1(3):81–84
Kurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5):e0177459
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
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
Burgess JM, Vianello G (2016) The multi-mission maximum likelihood framework (3ML). In: Eighth Huntsville gamma-ray burst symposium, vol 1962. p 4110
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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
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DOI: https://doi.org/10.1007/978-981-99-8270-7_5
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