Artificial gamma ray spectra simulation using Generative Adversarial Networks (GANs) and Supervised Generative Networks (SGNs)

X-ray and gamma-ray spectral analysis is classically based on models of expected spectra that are compared with acquired data. The usual approach is to define a mathematical model of an instrument in a stable and constant environment. However, when the operating conditions are varied, unstable, when the instrument has multiple configurations or when the environment itself has an impact on the measurement, it becomes extremely difficult to make accurate models to represent those different conditions. In this context, new approaches based on Deep Neural Networks are developed for nuclear, medical and astrophysical applications to perform spectra analysis, with the advantage that they can handle a wide variety of acquisition conditions, provided that they are represented in the training database. Therefore, in order to train these models, it is utterly important to have high quality and representative data, usually based on simulation models.