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@inproceedings{piekarski:ibic2022-tup38, author = {M. Piekarski}, title = {{Deep Neural Network for Beam Profile Classification in Synchrotron}}, & booktitle = {Proc. IBIC'22}, booktitle = {Proc. 11th Int. Beam Instrum. Conf. (IBIC'22)}, pages = {323--326}, eid = {TUP38}, language = {english}, keywords = {network, diagnostics, operation, emittance, synchrotron}, venue = {Kraków, Poland}, series = {International Beam Instrumentation Conference}, number = {11}, publisher = {JACoW Publishing, Geneva, Switzerland}, month = {12}, year = {2022}, issn = {2673-5350}, isbn = {978-3-95450-241-7}, doi = {10.18429/JACoW-IBIC2022-TUP38}, url = {https://jacow.org/ibic2022/papers/tup38.pdf}, abstract = {{The main goal of NSRC SOLARIS is to provide scientific community with high quality synchrotron light. To achieve this, it is necessary to constantly monitor many subsystems responsible for beam stability and to analyze data about the beam itself from various diagnostic beamlines. In this work a deep neural network for transverse beam profile classification is proposed. Main task of the system is to automatically assess and classify transverse beam profiles based solely on the evaluation of the beam image from the Pinhole diagnostic beamline at SOLARIS. At the present stage, a binary assignment of each profile is performed: stable beam operation or unstable beam operation / no beam. Base model architecture consists of a pre-trained convolutional neural network followed by a densely-connected classifier and the system reaches accuracy at the level of 90%. The model and the results obtained so far are discussed, along with plans for future development.}}, }