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The CCube reconstruction algorithm for the SoLid experiment

The SoLid collaboration

TL;DR

The paper presents the CCube reconstruction algorithm for the SoLid detector, framing the problem as a linear inverse system $AE=p$ to map fibre readouts to cube energy deposits. It systematically compares regularisation (FISTA/Tikhonov), Bayesian ML-EM, and a physics-informed sOMP-initiated ML-EM pipeline, finding that the combined sOMP+ML-EM method offers the best reconstruction efficiency and lowest ghost rate while meeting the energy-estimator requirements. Validation is performed with MC simulations and open data, showing energy-resolution compatibility with detector design and agreement between background and signal models and the observed open data excess, consistent with IBD candidates. The SoLid approach enables robust topological discrimination of annihilation gamma edges and positron ionisation, supporting precise antineutrino energy reconstruction and a sensitive short-baseline sterile neutrino search.

Abstract

The SoLid experiment is a very-short-baseline experiment aimed at searching for nuclear reactor-produced active to sterile antineutrino oscillations. The detection principle is based on the pairing of two types of solid scintillators: polyvinyl toluene and $^6$LiF:ZnS(Ag), which is a new technology used in this field of Physics. In addition to good neutron-gamma discrimination, this setup allows the detector to be highly segmented (the basic detection unit is a 5 cm side cube). High segmentation provides numerous advantages, including the precise location of Inverse Beta Decay (IBD) products, the derivation of the considerate antineutrino energy estimator, and a powerful background reduction tool based on the topological signature of the signal. Finally, the system is read out by a network of wavelength-shifting fibres coupled to a photodetector (MPPC). This paper describes the design of the reconstruction algorithm that allows maximum use of the granularity of the detector. The goal of the algorithm is to convert the output of the optical-fibre readout to the list of the detection units from which it originated. This paper provides a performance comparison for three methods and concludes with a choice of the baseline approach for the experiment.

The CCube reconstruction algorithm for the SoLid experiment

TL;DR

The paper presents the CCube reconstruction algorithm for the SoLid detector, framing the problem as a linear inverse system to map fibre readouts to cube energy deposits. It systematically compares regularisation (FISTA/Tikhonov), Bayesian ML-EM, and a physics-informed sOMP-initiated ML-EM pipeline, finding that the combined sOMP+ML-EM method offers the best reconstruction efficiency and lowest ghost rate while meeting the energy-estimator requirements. Validation is performed with MC simulations and open data, showing energy-resolution compatibility with detector design and agreement between background and signal models and the observed open data excess, consistent with IBD candidates. The SoLid approach enables robust topological discrimination of annihilation gamma edges and positron ionisation, supporting precise antineutrino energy reconstruction and a sensitive short-baseline sterile neutrino search.

Abstract

The SoLid experiment is a very-short-baseline experiment aimed at searching for nuclear reactor-produced active to sterile antineutrino oscillations. The detection principle is based on the pairing of two types of solid scintillators: polyvinyl toluene and LiF:ZnS(Ag), which is a new technology used in this field of Physics. In addition to good neutron-gamma discrimination, this setup allows the detector to be highly segmented (the basic detection unit is a 5 cm side cube). High segmentation provides numerous advantages, including the precise location of Inverse Beta Decay (IBD) products, the derivation of the considerate antineutrino energy estimator, and a powerful background reduction tool based on the topological signature of the signal. Finally, the system is read out by a network of wavelength-shifting fibres coupled to a photodetector (MPPC). This paper describes the design of the reconstruction algorithm that allows maximum use of the granularity of the detector. The goal of the algorithm is to convert the output of the optical-fibre readout to the list of the detection units from which it originated. This paper provides a performance comparison for three methods and concludes with a choice of the baseline approach for the experiment.
Paper Structure (11 sections, 13 equations, 9 figures, 4 tables)

This paper contains 11 sections, 13 equations, 9 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Signal definition
  3. The CCube algorithm
  4. Reconstruction procedure estimators
  5. The regularisation approach
  6. The Bayesian approach
  7. The SoLid approach
  8. Energy resolution
  9. Open dataset performance
  10. Conclusion
  11. Acknowledgements

Figures (9)

  • Figure 1: The composition of the SoLid detection plane.
  • Figure 2: A sketch of the energy deposit (pink cube) in the SoLid detector plane with fired horizontal (red) and vertical (blue) fibres and impacted (pink and light pink) MPPC.
  • Figure 3: An illustration that the reconstruction problem is ill-posed. The initial digitised readout interpreted in multiple ways in the sense of the attached energy contributions.
  • Figure 4: Data fidelity value for the FISTA algorithm versus the number of executed iterations. The distribution is obtained from 3$\times$10$^5$ simulated IBD events for L2 and KL estimators. The results for both of them are in agreement.
  • Figure 5: Data fidelity value for the FISTA + ML-EM algorithm versus the number of executed iterations. It is obtained from 3$\times$10$^5$ simulated IBD events for L2 and KL estimators.
  • ...and 4 more figures