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 -