TY - JOUR A1 - Radtke, Martin A1 - Rakotondrajoa, A. T1 - Machine learning based quantification of synchrotron radiation-induced X-ray fluorescence measurements - a case study JF - Machine Learning: Science and Technology N2 - In this work, we describe the use of artificial neural networks for the quantification of X-ray fluorescence measurements. The training data were generated using Monte Carlo simulation, which avoided the use of adapted reference materials. The extension of the available data set by means of an ANN to generate additional data was demonstrated. Particular emphasis was put on the comparability of simulated and experimental data and how the influence of deviations can be reduced. The search for the optimal hyperparameter, manual and automatic, is also described. For the presented case, we were able to train a network with a mean absolute error of 0.1 weight percent for the synthetic data and 0.7 weight percent for a set of experimental data obtained with certified reference materials. KW - Machine learning KW - BAMline KW - XRF KW - Synchrotron KW - Neural network PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-519008 DO - https://doi.org/10.1088/2632-2153/abc9fb SP - 1 EP - 16 PB - IOP Publishing AN - OPUS4-51900 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -