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A Multi-Parameter Persistence Algorithm for the Automatic Energy Calibration of Scintillating Radiation Sensors.

Guglielmo Ferranti^1,2, Chiara Rita Failla^1,3, Paolo Finocchiaro³

¹Department of Physics and Astronomy, University of Catania, Via S. Sofia 64, 95123 Catania, Italy.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This study introduces volumetric multi-parameter persistence (VM-PP) for robust peak detection in spectral data. The novel method reduces parameter sensitivity, improving reliability in scientific analysis.

Keywords:

automatic energy calibration multi-parameter persistence peak detection persistent homology topological data analysis

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Area of Science:

Data Analysis
Signal Processing
Scientific Instrumentation

Background:

Traditional peak detection methods are sensitive to parameter choices, hindering reliable interpretation.
Energy calibration of gamma ray scanners requires robust peak detection.

Purpose of the Study:

To develop a versatile methodology for robust peak detection in spectral and time-series data.
To reduce the sensitivity of peak detection to individual parameter selection.

Main Methods:

Introduced a multi-parameter persistence approach inspired by persistent homology.
Quantified peak robustness using hyper-volume in parameter space (volumetric multi-parameter persistence - VM-PP).
Explored parameter space systematically to identify consistently detected peaks.

Main Results:

VM-PP measure enables robust peak ranking.
Significantly reduced sensitivity of detection outcomes to parameter selection.
Reliably differentiated genuine peaks from noise across simulated and experimental data.

Conclusions:

The VM-PP method enhances interpretability and reliability in complex feature detection.
Demonstrated effectiveness in spectroscopic calibration and adaptable to diverse signal processing applications.