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 Twin Storage Bed w/Bookcase HDBD 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 a novel technique by which evolutionary algorithms calibrate a digitizer autonomously.
We demonstrate this by calibrating six free parameters in a digitizer model for the ADAC Forte.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 GARLIC RICH experiment, improving from 18.
3% difference when manually calibrated.Using evolutionary algorithms for model calibration is a notable advancement because this method is novel, 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.