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

TL;DR

This paper introduces a Bayesian network-based algorithm for 3D event reconstruction in a gaseous TPC detector, improving dark matter detection by accurately modeling particle tracks using optical signals.

Contribution

It presents a novel Bayesian inference method for 3D event reconstruction using photomultiplier signals, enhancing spatial resolution in dark matter detectors.

Findings

Accurate 3D reconstruction of particle tracks demonstrated.

Bayesian approach improves robustness and precision of event localization.

Method validated with prototype data showing effective 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$_4$ 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.