Autonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulation.

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.

Convolutional Neural Networks for Challenges in Automated Nuclide Identification.

Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods for the simulation and pre-processing of training data were also developed.