Skip to main content
Log in

Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors

  • Published:
Nuclear Science and Techniques Aims and scope Submit manuscript

Abstract

High-precision vertex and energy reconstruction are crucial for large liquid scintillator detectors such as that at the Jiangmen Underground Neutrino Observatory (JUNO), especially for the determination of neutrino mass ordering by analyzing the energy spectrum of reactor neutrinos. This paper presents a data-driven method to obtain a more realistic and accurate expected PMT response of positron events in JUNO and develops a simultaneous vertex and energy reconstruction method that combines the charge and time information of PMTs. For the JUNO detector, the impact of the vertex inaccuracy on the energy resolution is approximately 0.6%.

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

Access this article

Subscribe and save

Springer+
from €37.37 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price includes VAT (Spain)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Super-Kamiokande Collaboration, Evidence for oscillation of atmospheric neutrinos. Phys. Rev. Lett. 81, 1562–1567 (1998). https://doi.org/10.1103/PhysRevLett.81.1562

    Article  Google Scholar 

  2. SNO Collaboration, Direct evidence for neutrino flavor transformation from neutral current interactions in the Sudbury Neutrino Observatory. Phys. Rev. Lett. 89, 011301 (2002). https://doi.org/10.1103/PhysRevLett.89.011301

    Article  Google Scholar 

  3. KamLAND Collaboration, Reactor on-off antineutrino measurement with KamLAND. Phys. Rev. D 88(3), 033001 (2013). https://doi.org/10.1103/PhysRevD.88.033001

    Article  Google Scholar 

  4. SNO Collaboration, Combined analysis of all three phases of solar neutrino data from the sudbury neutrino observatory. Phys. Rev. C 88, 025501 (2013). https://doi.org/10.1103/PhysRevC.88.025501

    Article  Google Scholar 

  5. Daya Bay Collaboration, Measurement of the electron antineutrino oscillation with 1958 days of operation at Daya Bay. Phys. Rev. Lett. 121, 241805 (2018). https://doi.org/10.1103/PhysRevLett.121.241805

    Article  ADS  Google Scholar 

  6. IceCube Collaboration, Evidence for high-energy extraterrestrial neutrinos at the IceCube detector. Science 342, 1242856 (2013). https://doi.org/10.1126/science.1242856

    Article  Google Scholar 

  7. JUNO Collaboration, Neutrino physics with JUNO. J. Phys. G 43, 030401 (2016). https://doi.org/10.1088/0954-3899/43/3/030401

    Article  Google Scholar 

  8. W. Wu, M. He, X. Zhou et al., A new method of energy reconstruction for large spherical liquid scintillator detectors. J. Instrum. 14, P03009 (2019). https://doi.org/10.1088/1748-0221/14/03/P03009

    Article  Google Scholar 

  9. G. Huang, Y. Wang, W. Luo et al., Improving the energy uniformity for large liquid scintillator detectors. Nucl. Instrum. Meth. A 1001, 165287 (2021). https://doi.org/10.1016/j.nima.2021.165287

    Article  Google Scholar 

  10. Q. Liu, M. He, X. Ding et al., A vertex reconstruction algorithm in the central detector of JUNO. J. Instrum. 13, T09005 (2018). https://doi.org/10.1088/1748-0221/13/09/T09005

    Article  Google Scholar 

  11. Z. Li, Y. Zhang, G. Cao et al., Event vertex and time reconstruction in large-volume liquid scintillator detectors. Nucl. Sci. Tech. 32, 49 (2021). https://doi.org/10.1007/s41365-021-00885-z

    Article  Google Scholar 

  12. Z. Qian, V. Belavin, V. Bokov et al., Vertex and energy reconstruction in JUNO with machine learning methods. Nucl. Instrum. Meth. A 1010, 165527 (2021). https://doi.org/10.1016/j.nima.2021.165527

    Article  Google Scholar 

  13. Z.Y. Li, Z. Qian, J.H. He et al., Improvement of machine learning-based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs. Nucl. Sci. Tech. 33, 93 (2022). https://doi.org/10.1007/s41365-022-01078-y

    Article  Google Scholar 

  14. A. Gavrikov, Y. Malyshkin, F. Ratnikov, Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach. Eur. Phys. J. C 82, 1021 (2021). https://doi.org/10.1140/epjc/s10052-022-11004-6

    Article  ADS  Google Scholar 

  15. JUNO Collaboration, JUNO physics and detector. Prog. Part. Nucl. Phys. 123, 103927 (2022). https://doi.org/10.1016/j.ppnp.2021.103927

    Article  Google Scholar 

  16. JUNO and Daya Bay Collaboration, Optimization of the JUNO liquid scintillator composition using a Daya Bay antineutrino detector. Nucl. Instrum. Meth. A 988, 164823 (2021). https://doi.org/10.1016/j.nima.2020.164823

    Article  Google Scholar 

  17. JUNO Collaboration, Calibration strategy of the JUNO experiment. JHEP 2021, 4 (2021). https://doi.org/10.1007/JHEP03(2021)004

    Article  Google Scholar 

  18. T. Lin, J. Zou, W. Li et al., The application of SNiPER to the JUNO simulation. J. Phys. Conf. Series 898, 042029 (2017). https://doi.org/10.1007/JHEP03(2021)004

    Article  Google Scholar 

  19. Z.M. Wang, JUNO PMT system and prototyping. J. Phys. Conf. Ser. 888, 012052 (2017). https://doi.org/10.1088/1742-6596/888/1/012052

    Article  Google Scholar 

  20. JUNO Collaboration, Mass testing and characterization of 20-inch PMTs for JUNO. arXiv:2205.08629

  21. Y. Zhang, J. Liu, M. Xiao et al., Laser calibration system in JUNO. J. Instrum. 14, P01009 (2019). https://doi.org/10.1088/1748-0221/14/01/P01009

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Gui-Hong Huang, Wu-Ming Luo, and Wei Jiang. The first draft of the manuscript was written by Wu-Ming Luo and Gui-Hong Huang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Gui-Hong Huang.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

This work was supported by the National Key R &D Program of China (No.2018YFA0404100), the Strategic Priority Research Program of the Chinese Academy of Sciences (No. 12175257), the National Natural Science Foundation of China (No. 12175257) and the Science Foundation of High-Level Talents of Wuyi University (No. 2021AL027).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, GH., Jiang, W., Wen, LJ. et al. Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors. NUCL SCI TECH 34, 83 (2023). https://doi.org/10.1007/s41365-023-01240-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1007/s41365-023-01240-0

Keywords

Profiles

  1. Wu-Ming Luo