Bayesian network 3D event reconstruction in the Cygno optical TPC for dark matter direct detection
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| Format: | Preprint |
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2025
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| author | Amaro, Fernando Domingues Antonietti, Rita Baracchini, Elisabetta Benussi, Luigi Bianco, Stefano Borra, Francesco Capoccia, Cesidio Caponero, Michele Cavoto, Gianluca Costa, Igor Abritta Croce, Antonio Dané, Emiliano D'Astolfo, Melba Dho, Giorgio Di Giambattista, Flaminia Di Marco, Emanuele D'Imperio, Giulia Folcarelli, Matteo Santos, Joaquim Marques Ferreira dos Fiorina, Davide Iacoangeli, Francesco Islam, Zahoor Ul Júnior, Herman Pessoa Lima Kemp, Ernesto Maccarrone, Giovanni Mano, Rui Daniel Passos Marques, David José Gaspar Mazzitelli, Luan Gomes Mattosinhos de Carvalhoand Giovanni McLean, Alasdair Gregor Meloni, Pietro Messina, Andrea Monteiro, Cristina Maria Bernardes Nobrega, Rafael Antunes Pains, Igor Fonseca Paoletti, Emiliano Passamonti, Luciano Petrucci, Fabrizio Piacentini, Stefano Piccolo, Davide Pierluigi, Daniele Pinci, Davide Prajapati, Atul Renga, Francesco Roque, Rita Joana Cruz Rosatelli, Filippo Russo, Alessandro Saviano, Giovanna Silva, Pedro Alberto Oliveira Costa Spooner, Neil John Curwen Tesauro, Roberto Tomassini, Sandro Torelli, Samuele Tozzi, Donatella |
| author_facet | Amaro, Fernando Domingues Antonietti, Rita Baracchini, Elisabetta Benussi, Luigi Bianco, Stefano Borra, Francesco Capoccia, Cesidio Caponero, Michele Cavoto, Gianluca Costa, Igor Abritta Croce, Antonio Dané, Emiliano D'Astolfo, Melba Dho, Giorgio Di Giambattista, Flaminia Di Marco, Emanuele D'Imperio, Giulia Folcarelli, Matteo Santos, Joaquim Marques Ferreira dos Fiorina, Davide Iacoangeli, Francesco Islam, Zahoor Ul Júnior, Herman Pessoa Lima Kemp, Ernesto Maccarrone, Giovanni Mano, Rui Daniel Passos Marques, David José Gaspar Mazzitelli, Luan Gomes Mattosinhos de Carvalhoand Giovanni McLean, Alasdair Gregor Meloni, Pietro Messina, Andrea Monteiro, Cristina Maria Bernardes Nobrega, Rafael Antunes Pains, Igor Fonseca Paoletti, Emiliano Passamonti, Luciano Petrucci, Fabrizio Piacentini, Stefano Piccolo, Davide Pierluigi, Daniele Pinci, Davide Prajapati, Atul Renga, Francesco Roque, Rita Joana Cruz Rosatelli, Filippo Russo, Alessandro Saviano, Giovanna Silva, Pedro Alberto Oliveira Costa Spooner, Neil John Curwen Tesauro, Roberto Tomassini, Sandro Torelli, Samuele Tozzi, Donatella |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04973 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian network 3D event reconstruction in the Cygno optical TPC for dark matter direct detection Amaro, Fernando Domingues Antonietti, Rita Baracchini, Elisabetta Benussi, Luigi Bianco, Stefano Borra, Francesco Capoccia, Cesidio Caponero, Michele Cavoto, Gianluca Costa, Igor Abritta Croce, Antonio Dané, Emiliano D'Astolfo, Melba Dho, Giorgio Di Giambattista, Flaminia Di Marco, Emanuele D'Imperio, Giulia Folcarelli, Matteo Santos, Joaquim Marques Ferreira dos Fiorina, Davide Iacoangeli, Francesco Islam, Zahoor Ul Júnior, Herman Pessoa Lima Kemp, Ernesto Maccarrone, Giovanni Mano, Rui Daniel Passos Marques, David José Gaspar Mazzitelli, Luan Gomes Mattosinhos de Carvalhoand Giovanni McLean, Alasdair Gregor Meloni, Pietro Messina, Andrea Monteiro, Cristina Maria Bernardes Nobrega, Rafael Antunes Pains, Igor Fonseca Paoletti, Emiliano Passamonti, Luciano Petrucci, Fabrizio Piacentini, Stefano Piccolo, Davide Pierluigi, Daniele Pinci, Davide Prajapati, Atul Renga, Francesco Roque, Rita Joana Cruz Rosatelli, Filippo Russo, Alessandro Saviano, Giovanna Silva, Pedro Alberto Oliveira Costa Spooner, Neil John Curwen Tesauro, Roberto Tomassini, Sandro Torelli, Samuele Tozzi, Donatella Instrumentation and Detectors Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Data Analysis, Statistics and Probability 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. |
| title | Bayesian network 3D event reconstruction in the Cygno optical TPC for dark matter direct detection |
| topic | Instrumentation and Detectors Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2506.04973 |