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

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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mrow></mml:mrow> <mml:mn>4</mml:mn> <mml:mrow></mml:mrow></mml:mmultiscripts> </mml:math> 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: photomultiplier 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 photomultiplier signals, inferring a 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 straight tracks. Results demonstrate that the Bayesian approach enables robust 3D description and, when combined with camera data, opens the way to future improvements in spatial and energy resolution. This methodology represents a significant step forward in directional dark matter detection, enhancing the identification of nuclear recoil tracks with high spatial resolution.

Authors

Institutions

Publication Details

Journal
The European Physical Journal C
Published
2025-11-06
DOI
https://doi.org/10.1140/epjc/s10052-025-14965-6
Primary Topic
Dark Matter and Cosmic Phenomena
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

F. Rosatelli, N.J.C. Spooner, Rafael Antunes Nóbrega, Z. Islam et al.
The European Physical Journal C
Dark Matter and Cosmic Phenomena
article

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

F. Rosatelli, N.J.C. Spooner, Rafael Antunes Nóbrega, Z. Islam, S. Torelli, David José Gaspar Marques, C. Capoccia, P. A. O. C. Silva, Giorgio Dho, Stefano Piacentini, F. Petrucci, Flaminia Di Giambattista, D. Fiorina, Ashok Prajapati, L. Passamonti, Alessandro Croce, D Tozzi, G. Mazzitelli, Igor Abritta Costa, E. Paoletti, F. Iacoangeli, Alasdair Gregor McLean, E. Kemp, A. Russo, S. Tomassini, D. Pinci, M. Caponero, R. D. P. Mano, Rita Antonietti, Igor Fonseca Pains, H.P. Lima, J.M.F. dos Santos, C. M. B. Monteiro, F. D. Amaro, G. Maccarrone, A. Messina, Matteo Folcarelli, M. D'Astolfo, Luan Gomes Mattosinhos de Carvalho, S. Bianco, G. Cavoto, Emiliano Dané, Davide Piccolo, Emanuele Di Marco, R. Tesauro, D. Pierluigi, G. D’imperio, Giovanna Saviano, Pietro Meloni, L. Benussi, F. Renga, E. Baracchini, Rita Joana Cruz Roque, Francesco Borra
article en

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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mrow></mml:mrow> <mml:mn>4</mml:mn> <mml:mrow></mml:mrow></mml:mmultiscripts> </mml:math> 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: photomultiplier 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 photomultiplier signals, inferring a 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 straight tracks. Results demonstrate that the Bayesian approach enables robust 3D description and, when combined with camera data, opens the way to future improvements in spatial and energy resolution. This methodology represents a significant step forward in directional dark matter detection, enhancing the identification of nuclear recoil tracks with high spatial resolution.

The European Physical Journal CVol. 85(11)
Universidade Federal de Juiz de Fora (BR), National Agency for New Technologies, Energy and Sustainable Economic Development (IT), Roma Tre University (IT), Istituto Nazionale di Fisica Nucleare (IT), Universidade Estadual de Campinas (UNICAMP) (BR), University of L'Aquila (IT), Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali del Gran Sasso (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Roma Tre (IT), Basque Centre for Climate Change (ES), Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Frascati (IT), Gran Sasso Science Institute (IT), ENEA Frascati Research Centre, University of Coimbra (PT), Sapienza University of Rome (IT), University of Sheffield (GB)
European Commission, Ministero dell’Istruzione, dell’Università e della Ricerca, Laboratori Nazionali del Gran Sasso
Openalex Percentile: Top 85%
Dark Matter and Cosmic Phenomena
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.