# Machine learning-assisted event classification in cadmium zinc telluride positron emission tomography detectors leveraging entanglement-informed angular correlations

## TL;DR

This paper introduces a machine learning method to improve PET imaging by using quantum physics features to better distinguish true events from noise.

## Contribution

The novel use of entanglement-informed angular correlations in machine learning for PET event classification.

## Key findings

- Combining energy and Δφ features achieved the highest ROC–AUC (0.87–0.95) for event discrimination.
- Including spatial coordinates with energy and Δφ ranked third in performance (ROC–AUC 0.81–0.91).
- Entanglement-sensitive features effectively suppress contamination while preserving true lines of response.

## Abstract

Gamma–positron imaging with tracers that emit a prompt \documentclass[12pt]{minimal}
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\begin{document}$$\gamma$$\end{document} (> 511 keV) is vulnerable to Compton down-scatter leaking into the 511-keV window and mimicking true annihilation pairs. Conventional Positron Emission Tomography (PET) systems reconstruct annihilation events without leveraging that the two 511-keV photons are not only orthogonally polarized but also produced in a Bell-entangled state. The polarization correlations of this entanglement imprint themselves in Compton scattering kinematics, particularly the relative azimuthal scattering angle (\documentclass[12pt]{minimal}
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\begin{document}$$\Delta \phi$$\end{document}), offering a physics-informed handle for event discrimination. We present a machine-learning framework that exploits these quantum-encoded features to resolve true lines of response (LORs) and reject random coincidences in a dual-panel cadmium zinc telluride (CZT) system. Detected events were categorized into one-photoelectric (1P) and Compton (1C) interaction patterns, yielding four candidate interaction sequences per event. Each event was represented as a 4 \documentclass[12pt]{minimal}
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\begin{document}$$\times$$\end{document} 21 feature matrix comprising spatial coordinates, energy deposits, and angular descriptors, including \documentclass[12pt]{minimal}
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\begin{document}$$\Delta \phi$$\end{document} and polar scattering angle \documentclass[12pt]{minimal}
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\begin{document}$$\theta$$\end{document}. Feature ablation with five-fold cross-validation revealed that the combination of energy and \documentclass[12pt]{minimal}
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\begin{document}$$\Delta \phi$$\end{document} provided the highest discriminative power (Area Under the Receiver Operating Characteristic Curve (ROC–AUC) 0.87–0.95), followed by energy alone (ROC–AUC 0.85–0.95), while inclusion of spatial coordinates with energy and \documentclass[12pt]{minimal}
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\begin{document}$$\Delta \phi$$\end{document} ranked third, achieving consistent performance across folds (ROC–AUC 0.81–0.91). These results demonstrate that incorporating entanglement-sensitive angular features into learning pipelines can suppress prompt contamination while preserving true LORs in a gamma-positron imaging system.