TY - CONF A1 - Radtke, Martin T1 - ML@BAMLINE N2 - An overview of the machine learning methods used at BAMline. Examples of automatic alignment and optimisation based on Gaussian processes are discussed. Quantification of XRF measurements and reconstruction for coded aperture imaging based on neural networks are also presented. T2 - Machine Learning Workshop BAM CY - Online meeting DA - 18.03.2021 KW - Artificial Intelligence KW - Machine learning KW - Bayesian Optimization KW - Gaussian process KW - Neural Network PY - 2021 AN - OPUS4-54142 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radtke, Martin A1 - Buzanich, Ana A1 - Cakir, C.T. T1 - Enhancing efficiency at bamline: employing data science and machine learning for x-ray research N2 - This talk discusses how data science and machine learning techniques are being applied at the BAM Federal Institute for Materials Research and Testing to enhance efficiency and automation at the BAMLine synchrotron facility. The methods presented include Gaussian processes and Bayesian optimization for beamline adjustment and optimization of X-ray measurements. These statistical techniques allow automated alignment of beamline components and active learning scanning to reduce measurement time. Additional machine learning methods covered are neural networks for quantification of X-ray fluorescence (XRF) data and decoding coded apertures. T2 - 17th International Work-Conference on Artificial Neural Networks (IWANN2023) CY - Ponta Delgada, Portugal DA - 19.06.2023 KW - Bayesian Optimization KW - Gaussian Process KW - BAMline PY - 2023 AN - OPUS4-58605 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -