Latent variable modeling of gamma-ray background in repeated measurements

We propose a novel approach for background subtraction in repeated gamma-ray spectrometric measurements. This entirely data-driven method eliminates the need for Monte Carlo detector simulation. To accomplish this, we utilized the framework of Latent Variable Modeling, incorporating various matrix factorization techniques and artificial neural networks. Subsequently, we applied this method to estimate radionuclide activity through spectrum unmixing. Significant improvements in sensitivity, surpassing traditional methods, were observed for the test case scenario of aerosol filter measurements.