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

Area of Science:

• Experimental particle physics
• Astrophysics and cosmology

Background:

• Directional dark matter searches require precise 3D event reconstruction.
• The CYGNO experiment utilizes a gaseous Time Projection Chamber with optical readout.
• Existing reconstruction methods may benefit from alternative approaches.

Purpose of the Study:

• To develop and validate a Bayesian Network-based algorithm for 3D event reconstruction using only photomultiplier signals.
• To infer particle trajectories and improve directional dark matter detection capabilities.

Main Methods:

• A Bayesian Network algorithm was developed to model the light collection process probabilistically.
• Markov Chain Monte Carlo sampling was used for posterior inference within the Bayesian Analysis Toolkit.
• The algorithm was validated using data from the CYGNO LIME prototype.

Main Results:

• The algorithm accurately reconstructs localized and extended straight tracks using photomultiplier signals alone.
• A robust 3D description of particle trajectories was achieved.
• The Bayesian approach shows potential for future improvements in spatial and energy resolution.

Conclusions:

• The developed Bayesian Network algorithm provides a robust method for 3D event reconstruction in directional dark matter searches.
• This methodology enhances the identification of nuclear recoil tracks with high spatial resolution.
• Combining this technique with camera data promises further advancements in detector performance.