pith. sign in

arxiv: 2604.13747 · v1 · submitted 2026-04-15 · ⚛️ physics.ins-det · hep-ex

Realistic Detector Geometry Modeling and Its Impact on Event Reconstruction in JUNO

Zhaoxiang Wu , Miao He , Wuming Luo , Ziyan Deng , Wei He , Yuekun Heng , Xiaoping Jing , Bo Li
show 5 more authors
Xiaoyan Ma Xiaohui Qian Zhonghua Qin Yifang Wang Peidong Yu
This is my paper

Pith reviewed 2026-05-10 11:52 UTC · model grok-4.3

classification ⚛️ physics.ins-det hep-ex
keywords JUNOneutrino detectorPMT geometryevent reconstructionrealistic modelingvertex biasenergy resolutiondetector deformation
0
0 comments X

The pith

Realistic modeling of deformed PMT positions in JUNO removes up to 40 mm vertex biases while leaving energy reconstruction stable.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how installation-induced deformations in JUNO's stainless-steel truss shift the actual positions of photomultiplier tubes away from their nominal design values. The authors use limited survey measurements of some PMTs and truss points to perform a correlation analysis that predicts the positions of the remaining tubes. When this realistic geometry is adopted, the deformation itself proves to have negligible impact on energy reconstruction. In contrast, continuing to assume the ideal design geometry produces vertex biases reaching 40 mm. Updating the calibration-based PMT response model with the predicted positions eliminates the bias and keeps the reconstruction algorithms stable.

Core claim

The detector deformation due to installation has a negligible effect on energy reconstruction when using the realistic geometry. However, inaccuracies in the assumed geometry can introduce vertex biases of up to 40 mm. Incorporating the realistic geometry into the calibration-based PMT response model removes this bias and preserves the stability of the reconstruction algorithms.

What carries the argument

Correlation analysis of limited PMT and stainless-steel truss survey data to generate a full realistic PMT position map, then inserted into the calibration-based PMT response model for event reconstruction.

If this is right

  • Energy resolution at 1 MeV remains close to the 3 percent design goal even after accounting for real deformations.
  • Vertex reconstruction bias drops from tens of millimeters to negligible levels once realistic positions are used.
  • The same reconstruction algorithms can be kept without major re-tuning provided the geometry input is updated.
  • Deformation effects are shown to be geometry-modeling problems rather than fundamental limits on detector performance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar correlation methods could reduce the need for exhaustive surveys in other large-scale neutrino or particle detectors that experience structural settling.
  • The result highlights that geometry calibration must be treated with the same rigor as PMT gain and timing calibrations for precision neutrino measurements.
  • If the correlation holds, future detectors could rely on sparse surveys plus structural modeling to achieve the required position accuracy.

Load-bearing premise

The correlation analysis based on limited survey data of PMTs and stainless-steel truss can accurately predict the positions of all PMTs.

What would settle it

Comparison of the predicted PMT positions against direct position measurements on a set of tubes withheld from the correlation analysis; agreement within a few millimeters would confirm the model, while large discrepancies would falsify it.

Figures

Figures reproduced from arXiv: 2604.13747 by Bo Li, Miao He, Peidong Yu, Wei He, Wuming Luo, Xiaohui Qian, Xiaoping Jing, Xiaoyan Ma, Yifang Wang, Yuekun Heng, Zhaoxiang Wu, Zhonghua Qin, Ziyan Deng.

Figure 1
Figure 1. Figure 1: Examples of measured positions of the SS shell and PMTs [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison between surveyed and designed detector geome [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Measured PMT’s displacement in the z direction as a func￾tion of ring number. The red line represents the best-fit piecewise function. within the same layer by this angle dϕ. This procedure was applied to layers 2-10 where measurement data were avail￾able, while PMTs in other layers were assumed to remain in their original positions. c. Modeling of PMT displacements in the ρ direction [PITH_FULL_IMAGE:fig… view at source ↗
Figure 7
Figure 7. Figure 7: Simulated number of photoelectrons in case of designed ge [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
Figure 6
Figure 6. Figure 6: Examples of PMTs’ positions prediction in three rings. The [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Vertex reconstruction bias in the z-direction versus z for e + events with kinetic energies of (a) 0 MeV and (b) 8 MeV. between the two vertical dashlines. The reconstructed ρ was pulled towards the detector center by up to approximately 40 mm, since it has a outward shift in the realistic geometry with respect to the designed geometry, as one can see exactly from the left and middle plots in [PITH_FULL_I… view at source ↗
Figure 11
Figure 11. Figure 11: Energy reconstruction results for the three datasets: (a) [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Energy non-uniformity versus R 3 for e + events with kinetic energies of (a) 0 MeV and (b) 8 MeV. The total reflection region and the fiducial volume cut are indicated by the dashed line and the shaded area, respectively. ometry in reconstruction is accurate or not, the energy reso￾lution was observed to remain consistent within a statistical uncertainty of less than 1%. However, an incorrect geome￾try re… view at source ↗
read the original abstract

JUNO is designed to determine the neutrino mass ordering with an energy resolution of 3% at 1 MeV. In the real detector, however, deformations of the central stainless-steel structure during installation lead to deviations of the photomultiplier tube (PMT) positions from their design values. Based on the limited survey data of the PMTs and the stainless-steel truss, we perform a correlation analysis of the measured points and propose a method to predict the positions of all PMTs. Using the resulting realistic geometry, we demonstrate that the detector deformation has a negligible effect on the energy reconstruction. In contrast, inaccuracies in the assumed geometry can introduce vertex biases of up to 40 mm. Incorporating the realistic geometry into the calibration-based PMT response model removes this bias and preserves the stability of the reconstruction algorithms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that deformations in JUNO's stainless-steel truss during installation cause PMT position deviations from design values. Using correlation analysis on limited survey data of PMTs and truss points, the authors predict positions for all PMTs to create a realistic geometry model. Simulations show this geometry has negligible impact on energy reconstruction, but inaccurate assumed geometry introduces vertex biases up to 40 mm; incorporating the realistic geometry into the calibration-based PMT response model removes the bias while preserving reconstruction algorithm stability.

Significance. If the correlation-based position predictions prove accurate, the work is significant for JUNO's 3% energy resolution target at 1 MeV and neutrino mass ordering goals, as it quantifies and mitigates a key geometric systematic in vertex reconstruction for a large liquid-scintillator detector. The self-contained use of survey measurements rather than fitted parameters is a strength.

major comments (2)
  1. [Abstract] Abstract and method description: the correlation analysis on limited survey data to predict all PMT positions reports no cross-validation error, held-out RMS deviation, or comparison to independent measurements. This is load-bearing for the central claim that realistic geometry removes the 40 mm vertex bias, since unquantified prediction errors on the scale of the deformations (~tens of mm) could prevent the bias cancellation from translating to real data.
  2. [Reconstruction results] Reconstruction results section: the demonstration that the realistic geometry preserves algorithm stability requires explicit quantification of how the correlation parameters are propagated into the calibration-based PMT response model and whether any additional free parameters are introduced.
minor comments (2)
  1. [Abstract] The abstract would benefit from a brief statement of the correlation method (e.g., functional form or number of parameters) and the specific validation approach used.
  2. Consider adding a figure or table comparing predicted versus surveyed PMT positions on a held-out subset to support the prediction accuracy.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review of our manuscript. We address each major comment below and outline the revisions we will implement to strengthen the presentation.

read point-by-point responses

  1. Referee: [Abstract] Abstract and method description: the correlation analysis on limited survey data to predict all PMT positions reports no cross-validation error, held-out RMS deviation, or comparison to independent measurements. This is load-bearing for the central claim that realistic geometry removes the 40 mm vertex bias, since unquantified prediction errors on the scale of the deformations (~tens of mm) could prevent the bias cancellation from translating to real data.

    Authors: We agree that the absence of explicit validation metrics for the position predictions limits the strength of the central claim. The manuscript describes the correlation analysis performed on the available survey data of PMTs and truss points but does not report cross-validation results or held-out RMS deviations. In the revised version we will expand the method section with a quantitative validation subsection. This will include the results of k-fold cross-validation on the survey dataset, the corresponding held-out RMS deviations, and any direct comparisons to independent measurements that can be extracted from the available data. These additions will allow an assessment of whether the prediction uncertainties remain sub-dominant to the scale of the deformations and support the observed bias removal in the simulations. revision: yes

  2. Referee: [Reconstruction results] Reconstruction results section: the demonstration that the realistic geometry preserves algorithm stability requires explicit quantification of how the correlation parameters are propagated into the calibration-based PMT response model and whether any additional free parameters are introduced.

    Authors: We acknowledge that the propagation of the geometry information into the reconstruction must be described more explicitly. In the revised manuscript we will augment the reconstruction results section with a clear account of the procedure: the correlation parameters obtained from the survey data are used solely to generate the predicted PMT positions; these positions are then substituted directly into the existing calibration-based PMT response model to update the expected photon arrival times and light-collection efficiencies. No additional free parameters are introduced in the reconstruction algorithms themselves—the correlation parameters remain fixed after the survey analysis. This explicit description will demonstrate that the algorithm stability is preserved because the model update uses the realistic geometry without altering the degrees of freedom in the fit. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation uses external survey data

full rationale

The paper starts from independent survey measurements of a subset of PMTs and truss points, applies correlation analysis to predict remaining positions, and then uses the resulting geometry in Monte Carlo simulations to quantify reconstruction bias. No step reduces by construction to its own inputs: the 'prediction' is an extrapolation from measured data rather than a fit whose output is renamed as a result. No self-citations, uniqueness theorems, or ansatz smuggling appear in the provided text. The central demonstration (bias removal when realistic geometry is used) is shown against the design geometry baseline and is therefore falsifiable by external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim depends on the validity of the position prediction from correlation analysis, which involves free parameters fitted to the survey data. The assumption that the truss and PMT deformations are correlated is key but not independently verified in the abstract.

free parameters (1)
  • correlation parameters
    Parameters derived from correlation analysis of limited survey data to predict unmeasured PMT positions.
axioms (1)
  • domain assumption Deformations of the stainless-steel truss and PMT positions follow measurable correlations that allow extrapolation from limited survey points.
    Invoked in the proposed prediction method for all PMTs.

pith-pipeline@v0.9.0 · 5475 in / 1282 out tokens · 43367 ms · 2026-05-10T11:52:14.305057+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

35 extracted references · 35 canonical work pages

  1. [1]

    Fukuda et al

    Y . Fukuda et al. Evidence for oscillation of atmospheric neu- trinos.Phys. Rev. Lett., 81:1562–1567, 1998

    work page 1998

  2. [2]

    Q. R. Ahmad et al. Measurement of the rate ofν e +d→ p+p+e − interactions produced by 8B solar neutrinos at the Sudbury Neutrino Observatory.Phys. Rev. Lett., 87:071301, 2001

    work page 2001

  3. [3]

    Q. R. Ahmad et al. Direct evidence for neutrino flavor transfor- mation from neutral current interactions in the Sudbury Neu- trino Observatory.Phys. Rev. Lett., 89:011301, 2002

    work page 2002

  4. [4]

    JUNO physics and detector.Prog

    Angel Abusleme et al. JUNO physics and detector.Prog. Part. Nucl. Phys., 123:103927, 2022

    work page 2022

  5. [5]

    JUNO Conceptual Design Report

    Zelimir Djurcic et al. JUNO Conceptual Design Report. 8 2015

    work page 2015

  6. [6]

    Mass testing and characterization of 20-inch PMTs for JUNO.Eur

    Angel Abusleme et al. Mass testing and characterization of 20-inch PMTs for JUNO.Eur . Phys. J. C, 82(12):1168, 2022

    work page 2022

  7. [7]

    Mass production and characterization of 3-inch PMTs for the JUNO experiment.Nucl

    Chuanya Cao et al. Mass production and characterization of 3-inch PMTs for the JUNO experiment.Nucl. Instrum. Meth. A, 1005:165347, 2021

    work page 2021

  8. [8]

    Design, waterproofing, and mass production of the 3-inch PMT frontend system of JUNO.Nucl

    Jilei Xu et al. Design, waterproofing, and mass production of the 3-inch PMT frontend system of JUNO.Nucl. Instrum. Meth. A, 1086:171301, 2026

    work page 2026

  9. [9]

    De- termination of the Neutrino Mass Hierarchy at an Intermediate Baseline.Phys

    Liang Zhan, Yifang Wang, Jun Cao, and Liangjian Wen. De- termination of the Neutrino Mass Hierarchy at an Intermediate Baseline.Phys. Rev. D, 78:111103, 2008

    work page 2008

  10. [10]

    Ex- perimental Requirements to Determine the Neutrino Mass Hi- erarchy Using Reactor Neutrinos.Phys

    Liang Zhan, Yifang Wang, Jun Cao, and Liangjian Wen. Ex- perimental Requirements to Determine the Neutrino Mass Hi- erarchy Using Reactor Neutrinos.Phys. Rev. D, 79:073007, 2009

    work page 2009

  11. [11]

    Unam- biguous Determination of the Neutrino Mass Hierarchy Using Reactor Neutrinos.Phys

    Yu-Feng Li, Jun Cao, Yifang Wang, and Liang Zhan. Unam- biguous Determination of the Neutrino Mass Hierarchy Using Reactor Neutrinos.Phys. Rev. D, 88:013008, 2013. -9 Zhaoxiang Wuet al. Nucl. Sci. Tech. , ()

    work page 2013

  12. [12]

    Neutrino Physics with JUNO.J

    Fengpeng An et al. Neutrino Physics with JUNO.J. Phys. G, 43(3):030401, 2016

    work page 2016

  13. [13]

    Abusleme et al

    A. Abusleme et al. Optimization of the JUNO liquid scintilla- tor composition using a Daya Bay antineutrino detector.Nucl. Instrum. Meth. A, 988:164823, 2021

    work page 2021

  14. [14]

    Improving the energy uniformity for large liquid scintillator detectors.Nucl

    Guihong Huang et al. Improving the energy uniformity for large liquid scintillator detectors.Nucl. Instrum. Meth. A, 1001:165287, 2021

    work page 2021

  15. [15]

    W. Wu, M. He, X. Zhou, and H. Qiao. A new method of energy reconstruction for large spherical liquid scintillator de- tectors.Journal of Instrumentation, 14(03):P03009–P03009, March 2019

    work page 2019

  16. [16]

    Q. Liu, M. He, X. Ding, W. Li, and H. Peng. A vertex recon- struction algorithm in the central detector of juno.Journal of Instrumentation, 13(09):T09005–T09005, September 2018

    work page 2018

  17. [17]

    Event vertex and time reconstruction in large-volume liquid scintil- lator detectors.Nuclear Science and Techniques, 32(5), May 2021

    Zi-Yuan Li, Yu-Mei Zhang, Guo-Fu Cao, Zi-Yan Deng, Gui- Hong Huang, Wei-Dong Li, Tao Lin, Liang-Jian Wen, Miao Yu, Jia-Heng Zou, Wu-Ming Luo, and Zheng-Yun You. Event vertex and time reconstruction in large-volume liquid scintil- lator detectors.Nuclear Science and Techniques, 32(5), May 2021

    work page 2021

  18. [18]

    Data-driven simultaneous vertex and en- ergy reconstruction for large liquid scintillator detectors.Nucl

    Gui-hong Huang, Wei Jiang, Liang-jian Wen, Yi-fang Wang, and Wu-Ming Luo. Data-driven simultaneous vertex and en- ergy reconstruction for large liquid scintillator detectors.Nucl. Sci. Tech., 34(6):83, 2023

    work page 2023

  19. [19]

    Prediction of Energy Resolution in the JUNO Experiment.Chin

    Angel Abusleme et al. Prediction of Energy Resolution in the JUNO Experiment.Chin. Phys. C, 49(1):013003, 2025

    work page 2025

  20. [20]

    Potential to identify neutrino mass or- dering with reactor antineutrinos at JUNO.Chin

    Angel Abusleme et al. Potential to identify neutrino mass or- dering with reactor antineutrinos at JUNO.Chin. Phys. C, 49(3):033104, 2025

    work page 2025

  21. [21]

    Vertex and energy reconstruction in JUNO with machine learning methods.Nucl

    Zhen Qian et al. Vertex and energy reconstruction in JUNO with machine learning methods.Nucl. Instrum. Meth. A, 1010:165527, 2021

    work page 2021

  22. [22]

    Improvement of machine learning-based vertex recon- struction for large liquid scintillator detectors with multiple types of PMTs.Nucl

    Zi-Yuan Li, Zhen Qian, Jie-Han He, Wei He, Cheng-Xin Wu, Xun-Ye Cai, Zheng-Yun You, Yu-Mei Zhang, and Wu-Ming Luo. Improvement of machine learning-based vertex recon- struction for large liquid scintillator detectors with multiple types of PMTs.Nucl. Sci. Tech., 33(7):93, 2022

    work page 2022

  23. [23]

    Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors.Eur

    Wei Jiang, Guihong Huang, Zhen Liu, Wuming Luo, Liangjian Wen, and Jianyi Luo. Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors.Eur . Phys. J. C, 85(1):69, 2025

    work page 2025

  24. [24]

    En- ergy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach

    Arsenii Gavrikov, Yury Malyshkin, and Fedor Ratnikov. En- ergy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach. Eur . Phys. J. C, 82(11):1021, 2022

    work page 2022

  25. [25]

    Reconstruction of a muon bundle in the JUNO central detector.Nucl

    Cheng-Feng Yang, Yong-Bo Huang, Ji-Lei Xu, Di-Ru Wu, Hao-Qi Lu, Yong-Peng Zhang, Wu-Ming Luo, Miao He, Guo- Ming Chen, and Si-Yuan Zhang. Reconstruction of a muon bundle in the JUNO central detector.Nucl. Sci. Tech., 33(5):59, 2022

    work page 2022

  26. [26]

    First attempt of directionality reconstruc- tion for atmospheric neutrinos in a large homogeneous liquid scintillator detector.Phys

    Zekun Yang et al. First attempt of directionality reconstruc- tion for atmospheric neutrinos in a large homogeneous liquid scintillator detector.Phys. Rev. D, 109(5):052005, 2024

    work page 2024

  27. [27]

    Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector

    Jiaxi Liu et al. Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector. Phys. Rev. D, 112(1):012018, 2025

    work page 2025

  28. [28]

    Fast Muon Simulation in the JUNO Central Detector.Chin

    Tao Lin, Zi-Yan Deng, Wei-Dong Li, Guo-Fu Cao, Zheng-Yun You, and Xin-Ying Li. Fast Muon Simulation in the JUNO Central Detector.Chin. Phys. C, 40(8):086201, 2016

    work page 2016

  29. [29]

    A method of detec- tor and event visualization with Unity in JUNO.JINST, 14(01):T01007, 2019

    Jiang Zhu, Zhengyun You, Yumei Zhang, Ziyuan Li, Shu Zhang, Tao Lin, and Weidong Li. A method of detec- tor and event visualization with Unity in JUNO.JINST, 14(01):T01007, 2019

    work page 2019

  30. [30]

    A method for sharing dynamic geom- etry information in studies on liquid-based detectors.Nucl

    Shu Zhang, Jing-Shu Li, Yang-Jie Su, Yu-Mei Zhang, Zi-Yuan Li, and Zheng-Yun You. A method for sharing dynamic geom- etry information in studies on liquid-based detectors.Nucl. Sci. Tech., 32(2):21, 2021

    work page 2021

  31. [31]

    The design and technology development of the JUNO central detector.Eur

    Angel Abusleme et al. The design and technology development of the JUNO central detector.Eur . Phys. J. Plus, 139(12):1128, 2024

    work page 2024

  32. [32]

    https://www.faro.com/en/Resource-Library/Tech- Sheet/techsheet-faro-vantage-s-e-laser-tracker

    work page

  33. [33]

    Simulation software of the JUNO experiment

    Tao Lin et al. Simulation software of the JUNO experiment. Eur . Phys. J. C, 83(5):382, 2023. [Erratum: Eur.Phys.J.C 83, 660 (2023)]

    work page 2023

  34. [34]

    Design and Development of JUNO Event Data Model.Chin

    Teng Li, Xin Xia, XingTao Huang, JiaHeng Zou, WeiDong Li, Tao lin, Kun Zhang, and ZiYan Deng. Design and Development of JUNO Event Data Model.Chin. Phys. C, 41(6):066201, 2017

    work page 2017

  35. [35]

    Initial performance results of the juno detector.Chinese Physics C, 2026

    Angel Abusleme et al. Initial performance results of the juno detector.Chinese Physics C, 2026. -10

    work page 2026