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Picometer-Precision Atomic Position Tracking through Electron Microscopy

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From Light to Energy: Machine Learning Algorithms for Position and Energy Deposition Estimation in Scintillator-SiPM

Yoav Simhony^1,2, Alex Segal³, Ofer Amrani²

¹Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv-Yafo 6997801, Israel.

Sensors (Basel, Switzerland)

|January 10, 2026

Summary

Machine learning enhances Scintillator-SiPM Particle Detectors (SSPDs) for improved particle tracking and energy estimation. Gradient boosting models, like XGBoost, significantly outperform analytic methods, boosting detector performance in physics and space applications.

Keywords:

SSPD gradient boosting machine learning

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Area of Science:

Particle Physics and Instrumentation
Detector Technology
Computational Science

Background:

Scintillator-SiPM Particle Detectors (SSPDs) are compact, low-power devices utilized in diverse scientific fields.
Existing analytic algorithms offer moderate accuracy in reconstructing particle trajectories and energy deposition.
There is a need for enhanced accuracy in SSPD data analysis.

Purpose of the Study:

To improve the accuracy of spatial location and energy deposition estimation in SSPDs.
To evaluate the effectiveness of machine learning (ML) techniques, specifically gradient boosting, for SSPD data analysis.
To compare ML-based approaches against traditional analytic methods.

Main Methods:

Utilized the GEANT4 simulation toolkit to model cosmic muons and energetic oxygen ions interacting with an SSPD.
Trained XGBoost and LightGBM models to predict particle impinging positions and deposited energy.
Investigated hybrid ML strategies, including hybrid boosting and probing.

Main Results:

Both XGBoost and LightGBM models demonstrated superior performance compared to the analytic baseline in position and energy estimation.
Probing strategies yielded measurable improvements in both position and Linear Energy Transfer (LET) estimation.
Hybrid boosting did not provide significant performance gains.

Conclusions:

Machine learning-driven reconstruction significantly enhances the performance of SSPDs.
Gradient boosting techniques offer a powerful tool for improving particle detection accuracy.
ML methods represent a promising advancement for SSPD applications in physics and instrumentation.