AI based Scintillation Detector Calibration

TL;DR

This paper develops and compares three AI-based algorithms—polynomial regression, support vector regression, and neural networks—for calibrating scintillation detectors, highlighting the importance of median data and average time in radiation measurement accuracy.

Contribution

It introduces a novel approach using multiple AI algorithms for scintillation detector calibration and identifies key data features affecting measurement accuracy.

Findings

Median data yields more accurate calibration results.

Average time significantly influences radiation measurement accuracy.

Neural networks outperform traditional regression methods in calibration.

Abstract

Data set generated from the scintillation detector is used to build a mathematical model based on three different algorithms: (a) Multiple Polynomial Regression (b) Support Vector Regression (c) Neural Network algorithm. Using visualizations and correlations, it is found that the Median of the data will give accurate results and average time has a major contribution in radiation measurement.