Machine learning models for single-particle classification with Timepix 3 detector

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Semiconductor hybrid pixel detectors with Timepix 3 chips developed by Medipix collaboration at CERN can simultaneously measure deposited energy and time of arrival of individual particle hits in all 256 × 256 pixels with 55 μm pitch size. Leveraging the single-particle detection sensitivity of these chips, there is a potential to develop algorithms for classifying detected single particles into distinct categories corresponding to different particle types. In this study, various machine learning models are introduced, such as recurrent and feedforward neural networks or gradient boosted decision trees, designed to facilitate the classification of single particle events into distinct classes associated to electrons & photons, alpha particles, heavy nuclei (except alpha particles), low energy protons (E ≲ 100 MeV) and high energy protons (E ≳ 100 MeV). All models achieve outcomes with the true positive rate nearing 100% across all classes. The Gaussian Mixture unsupervised machine learning technique is used to differentiate between electron and photon radiation components. The model effectively distinguished between high-energy electrons and low-energy photons, achieving performance comparable to conventionally used heuristic decision trees. All models are trained and tested on an extensive database of experimental data obtained from controlled radiation source experiments.