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

Gui-Hong Huang

doi:10.1007/s41365-023-01240-0

Your Location：

Home >

Browse articles >

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

NUCLEAR PHYSICS AND INTERDISCIPLINARY RESEARCH | Updated：2023-08-14

- Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors
- Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors
- 核技术（英文版） 2023年34卷第6期文章编号：83
- Affiliations：
  
  1.School of Applied Physics and Materials, Wuyi University, Jiangmen 529020, China
  2.Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
  3.School of Physical Sciences, University of Chinese Academy of Science, Beijing 100049, China
- Author bio：
  
  †huanggh@wyu.edu.cn
  ‡luowm@ihep.ac.cn
- Funds：
  
  National Key R&D Program of China(2018YFA0404100);Strategic Priority Research Program of the Chinese Academy of Sciences(12175257);Science Foundation of High-Level Talents of Wuyi University(2021AL027)
- DOI：10.1007/s41365-023-01240-0
  中图分类号：
Scan for full text
引用本文
Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors[J]. 核技术（英文版）, 2023, 34(6):83

Gui-Hong Huang, Wei Jiang, Liang-Jian Wen, et al. Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors[J]. Nuclear Science and Techniques, 2023, 34(6):83
Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors[J]. 核技术（英文版）, 2023, 34(6):83 DOI： 10.1007/s41365-023-01240-0.

Gui-Hong Huang, Wei Jiang, Liang-Jian Wen, et al. Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors[J]. Nuclear Science and Techniques, 2023, 34(6):83 DOI： 10.1007/s41365-023-01240-0.

摘要

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%.

关键词

Keywords

JUNOLiquid scintillator detectorNeutrino experimentVertex reconstructionEnergy reconstruction

references

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

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

KamLAND Collaboration, Reactor on-off antineutrino measurement with KamLAND. Phys. Rev. D 88, no.3, 033001 (2013). doi: 10.1103/PhysRevD.88.033001http://doi.org/10.1103/PhysRevD.88.033001

SNO Collaboration, Combined analysis of all three phases of solar neutrino data from the Sudbury Neutrino Observatory. Phys. Rev. C 88, 025501 (2013). doi: 10.1103/PhysRevC.88.025501http://doi.org/10.1103/PhysRevC.88.025501

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

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

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

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). doi: 10.1088/1748-0221/14/03/P03009http://doi.org/10.1088/1748-0221/14/03/P03009

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

Q. Liu, M. He, X. Ding et al., A vertex reconstruction algorithm in the central detector of JUNO. J. Instrum. 13, T09005 (2018). doi: 10.1088/1748-0221/13/09/T09005http://doi.org/10.1088/1748-0221/13/09/T09005

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). doi: 10.1007/s41365-021-00885-zhttp://doi.org/10.1007/s41365-021-00885-z

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). doi: 10.1016/j.nima.2021.165527http://doi.org/10.1016/j.nima.2021.165527

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). doi: 10.1007/s41365-022-01078-yhttp://doi.org/10.1007/s41365-022-01078-y

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). doi: 10.1140/epjc/s10052-022-11004-6http://doi.org/10.1140/epjc/s10052-022-11004-6

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

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). doi: 10.1016/j.nima.2020.164823http://doi.org/10.1016/j.nima.2020.164823

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

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

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

JUNO Collaboration, Mass Testing and Characterization of 20-inch PMTs for JUNO. arXiv:2205.08629.

Y. Zhang, J. Liu, M. Xiao et al., Laser calibration system in JUNO. J. Instrum. 14, P01009 (2019). doi: 10.1088/1748-0221/14/01/P01009http://doi.org/10.1088/1748-0221/14/01/P01009

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Improvement of machine learning-based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs

Event vertex and time reconstruction in large volume liquid scintillator detectors

Reconstruction of a muon bundle in the JUNO central detector

Afterpulse measurement of JUNO 20-inch PMTs

Low-radioactivity ultrasonic hydrophone used in positioning system for Jiangmen Underground Neutrino Observatory