Autonomous Digitizer Calibration of a Monte Carlo Detector Model through Evolutionary Simulation

Abstract

Simulating the response of a radiation detector is a modelling challenge due to the stochastic nature of radiation, often complex geometries, and multi-stage signal-processing. While sophisticated tools for Monte Carlo simulation have been developed for radiation transport, emulating signal-processing and data loss must be accomplished using a simplified model of the electronics called the digitizer. Due to a large number of free parameters, calibrating a digitizer quickly becomes an optimisation problem. To address this, we propose using evolutionary algorithms to perform digitizer calibrations. We demonstrate this by calibrating a digitizer for the Phillips/ACAC Forte, which contains six free parameters. The accuracy of solutions is quantified via a cost function measuring the absolute percent difference between simulated and experimental coincidence count rates across a robust characterisation data set, including three detector configurations and a range of source activities. Ultimately, this calibration produces a count rate response with 5.8\% mean difference to the experiment, improving from 18.3\% difference when manually calibrated. Using evolutionary algorithms for model calibration is a notable advancement because this method is autonomous, fault-tolerant, and achieved through a direct comparison of simulation to reality. The software used in this work has been made freely available through a GitHub repository.