https://doi.org/10.1140/epjc/s10052-025-14965-6
Regular Article - Experimental Physics
Bayesian network 3D event reconstruction in the Cygno optical TPC for dark matter direct detection
1
LIBPhys, Department of Physics, University of Coimbra, 3004-516, Coimbra, Portugal
2
Istituto Nazionale di Fisica Nucleare, Sezione di Roma TRE, 00146, Rome, Italy
3
Dipartimento di Matematica e Fisica, Università Roma TRE, 00146, Rome, Italy
4
Gran Sasso Science Institute, 67100, L’Aquila, Italy
5
Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali del Gran Sasso, 67100, Assergi, Italy
6
Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Frascati, 00044, Frascati, Italy
7
Istituto Nazionale di Fisica Nucleare, Sezione di Roma, 00185, Rome, Italy
8
Dipartimento di Fisica, Sapienza Università di Roma, 00185, Rome, Italy
9
ENEA Centro Ricerche Frascati, 00044, Frascati, Italy
10
Universidade Estadual de Campinas-UNICAMP, 13083-859, Campinas, SP, Brazil
11
Department of Physics and Astronomy, University of Sheffield, S3 7RH, Sheffield, UK
12
Universidade Federal de Juiz de Fora, Faculdade de Engenharia, 36036-900, Juiz de Fora, MG, Brazil
13
Dipartimento di Ingegneria Chimica, Materiali e Ambiente, Sapienza Università di Roma, 00185, Rome, Italy
14
University of L’Aquila, Edificio Renato Ricamo, via Vetoio, Coppito, 67100, L’Aquila, Italy
15
Donostia International Physics Center, BERC Basque Excellence Research Centre, Manuel Lardizabal 4, 20018, San Sebastián/Donostia, Spain
a
francesco.borra@uniroma3.it
b
matteo.folcarelli@uniroma1.it
c
david.marques@gssi.it
Received:
9
June
2025
Accepted:
20
October
2025
Published online:
6
November
2025
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: 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 Author(s) 2025
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Funded by SCOAP3.
