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

## TL;DR

The CYGNO experiment uses a Bayesian network algorithm to reconstruct 3D particle tracks in a gas detector for directional dark matter detection.

## Contribution

A novel Bayesian Network-based algorithm for 3D event reconstruction using only photomultiplier signals in a gaseous Time Projection Chamber.

## Key findings

- The Bayesian algorithm accurately reconstructs localized and extended straight tracks using photomultiplier signals.
- Combining photomultiplier and camera data improves spatial and energy resolution for particle tracking.
- The methodology advances directional dark matter detection by enhancing nuclear recoil track identification.

## 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\documentclass[12pt]{minimal}
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\begin{document}$$_4$$\end{document}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: 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.