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Bayesian network 3D event reconstruction in the Cygno optical TPC for dark matter direct detection

Fernando Domingues Amaro, Rita Antonietti, Elisabetta Baracchini, Luigi Benussi, Stefano Bianco, Francesco Borra, Cesidio Capoccia, Michele Caponero, Gianluca Cavoto, Igor Abritta Costa, Antonio Croce, Emiliano Dané, Melba D'Astolfo, Giorgio Dho, Flaminia Di Giambattista, Emanuele Di Marco, Giulia D'Imperio, Matteo Folcarelli, Joaquim Marques Ferreira dos Santos, Davide Fiorina, Francesco Iacoangeli, Zahoor Ul Islam, Herman Pessoa Lima Júnior, Ernesto Kemp, Giovanni Maccarrone, Rui Daniel Passos Mano, David José Gaspar Marques, Luan Gomes Mattosinhos de Carvalhoand Giovanni Mazzitelli, Alasdair Gregor McLean, Pietro Meloni, Andrea Messina, Cristina Maria Bernardes Monteiro, Rafael Antunes Nobrega, Igor Fonseca Pains, Emiliano Paoletti, Luciano Passamonti, Fabrizio Petrucci, Stefano Piacentini, Davide Piccolo, Daniele Pierluigi, Davide Pinci, Atul Prajapati, Francesco Renga, Rita Joana Cruz Roque, Filippo Rosatelli, Alessandro Russo, Giovanna Saviano, Pedro Alberto Oliveira Costa Silva, Neil John Curwen Spooner, Roberto Tesauro, Sandro Tomassini, Samuele Torelli, Donatella Tozzi

Abstract

The CYGNO experiment is developing a high-resolution gaseous Time Projection Chamber with optical readout for directional dark matter searches. The detector uses a helium-tetrafluoromethane (He:CF$_4$ 60:40) gas mixture at atmospheric pressure and a triple Gas Electron Multiplier amplification stage, coupled with a scientific camera for high-resolution 2D imaging and fast photomultipliers for time-resolved scintillation light detection. This setup enables 3D event reconstruction: photomultipliers signals provide depth information, while the camera delivers high-precision transverse resolution. In this work, we present a Bayesian Network-based algorithm designed to reconstruct the events using only the photomultipliers signals, yielding a full 3D description of the particle trajectories. The algorithm models the light collection process probabilistically and estimates spatial and intensity parameters on the Gas Electron Multiplier plane, where light emission occurs. It is implemented within the Bayesian Analysis Toolkit and uses Markov Chain Monte Carlo sampling for posterior inference. Validation using data from the CYGNO LIME prototype shows accurate reconstruction of localized and extended tracks. Results demonstrate that the Bayesian approach enables robust 3D description and, when combined with camera data, further improves the precision of track reconstruction. This methodology represents a significant step forward in directional dark matter detection, enhancing the identification of nuclear recoil tracks with high spatial resolution.

Bayesian network 3D event reconstruction in the Cygno optical TPC for dark matter direct detection

Abstract

The CYGNO experiment is developing a high-resolution gaseous Time Projection Chamber with optical readout for directional dark matter searches. The detector uses a helium-tetrafluoromethane (He:CF 60:40) gas mixture at atmospheric pressure and a triple Gas Electron Multiplier amplification stage, coupled with a scientific camera for high-resolution 2D imaging and fast photomultipliers for time-resolved scintillation light detection. This setup enables 3D event reconstruction: photomultipliers signals provide depth information, while the camera delivers high-precision transverse resolution. In this work, we present a Bayesian Network-based algorithm designed to reconstruct the events using only the photomultipliers signals, yielding a full 3D description of the particle trajectories. The algorithm models the light collection process probabilistically and estimates spatial and intensity parameters on the Gas Electron Multiplier plane, where light emission occurs. It is implemented within the Bayesian Analysis Toolkit and uses Markov Chain Monte Carlo sampling for posterior inference. Validation using data from the CYGNO LIME prototype shows accurate reconstruction of localized and extended tracks. Results demonstrate that the Bayesian approach enables robust 3D description and, when combined with camera data, further improves the precision of track reconstruction. This methodology represents a significant step forward in directional dark matter detection, enhancing the identification of nuclear recoil tracks with high spatial resolution.

Paper Structure

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

Table of Contents

  1. Introduction
  2. Detector description
  3. The LIME prototype
  4. PMTs signals
  5. Application of Bayesian Networks for PMT-Based 3D Reconstruction
  6. Code implementation
  7. Fitting strategy and dataset
  8. In-situ calibration of the PMT response
  9. Reconstruction of localized tracks
  10. Extended tracks
  11. Conclusions

Figures (13)

  • Figure 1: Schematic view of the LIME detector. The He:CF$_4$ (60:40) gas mixture is contained in a PMMA vessel housing a copper field cage. Ionization electrons drift from the cathode (right) toward the amplification region (left), where a triple-GEM structure produces charge multiplication and scintillation light. This light is collected by a centrally aligned APS-sCMOS camera and four PMTs located above the GEM plane, on the optical readout side.
  • Figure 2: Relative disposition of the sensors with respect to the GEM plane, where light is emitted. Top: side view showing the field cage and the vertical distances between the PMTs and the GEMs. Bottom: front view, showing the camera position (centered) and the four PMTs (at the corners).
  • Figure 3: Example of an event recorded with the LIME's optical readout, illustrating (\ref{['fig:LIME_evt_cmos']}) the image acquired by the APS-sCMOS camera during a 300 ms exposure with four distinct tracks: two localized clusters; one extended straight ionization trail; and a curly scattered track (electron recoil). Figure (\ref{['fig:LIME_evt_pmts']}) shows the PMT signals (inverted for clarity) recorded within the same acquisition window, each associated to one of the ionization in the picture.
  • Figure 4: Schematic representation of the illumination of the $i$-th PMT by the radiating source with coordinates $(X_j,Y_j)$ on the GEMs. The distance between the centers of the two surfaces is denoted by $R_{ij}$, and the angle with respect to the $z$-axis is $\theta_{ij}$.
  • Figure 5: Relative standard deviation, for each PMT, of the measured charge as a function of the expected value $\mu_{ij}$ from Eq. \ref{['eq:charge-light']}. The expected value spans over the dynamic range because of a $^{55}$Fe mono-energetic source occurring at different positions on the GEM plane. The colored points are the measured data, the black curve is the fit of the model described in Eq. \ref{['eq:dispersion']} for all the PMTs and the blue band represents the $3\sigma$ uncertainty range of the inference process. The insets display the relative charge dispersion for the three points indicated by the arrows with the relative Gaussian model superimposed.
  • ...and 8 more figures