TY - CONF A1 - Radtke, Martin T1 - Machine learning for direct quantification of XRF measurements N2 - In this talk I’ll describe the use of artificial neural networks (ANN) for quantifying X-ray fluorescence (XRF) measurements. The main idea of this talk is to give an overview of the process needed to generate a model that can then be applied to a specific problem. In XRF, a sample is excited with X-rays and the resulting characteristic radiation is detected to determine elements quantitatively and qualitatively. This is traditionally done in several time-consuming steps. I’ll show the possibilities and problems of using a neural network to realise a "one-click" quantification. This includes generating training data using Monte Carlo simulation and augmenting the existing data set with an ANN to generate more data. The search for the optimal hyperparameters, manually and automatically, is also described. For the case presented, we were able to train a network with a mean absolute error of 0.1% by weight for the synthetic data and 0.7% by weight for a set of experimental data obtained with certified reference materials. T2 - Seminar series: Artificial Intelligence applied to X-ray / synchrotron techniques CY - Online meeting DA - 24.06.2021 KW - Artificial intelligence KW - Machine learning KW - Synchrotron KW - XRF PY - 2021 AN - OPUS4-54140 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -