The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
TY - CONF AU - Fol, E. AU - Coello de Portugal, J.M. AU - Franchetti, G. AU - Tomás, R. ED - Schaa, Volker R.W. ED - Decking, Winfried ED - Sinn, Harald ED - Geloni, Gianluca ED - Schreiber, Siegfried ED - Marx, Michaela TI - Application of Machine Learning to Beam Diagnostics J2 - Proc. of FEL2019, Hamburg, Germany, 26-30 August 2019 CY - Hamburg, Germany T2 - Free Electron Laser Conference T3 - 39 LA - english AB - Machine Learning (ML) techniques are widely used in science and industry to discover relevant information and make predictions from data. The application ranges from face recognition to High Energy Physics experiments. Recently, the application of ML has grown also in accelerator physics and in particular in the domain of diagnostics and control. The target is to provide an overview of ML techniques and to indicate beam diagnostics tasks where ML based solutions can be efficiently applied to complement or potentially surpass existing methods. Besides, a short summary of recent works will be given demonstrating the great interest for use of ML concepts in beam diagnostics and latest results of incorporating these concepts into accelerator problems, with the focus on beam optics related application. PB - JACoW Publishing CP - Geneva, Switzerland SP - 311 EP - 317 KW - optics KW - network KW - diagnostics KW - controls KW - target DA - 2019/11 PY - 2019 SN - "" SN - 978-3-95450-210-3 DO - doi:10.18429/JACoW-FEL2019-WEB03 UR - http://jacow.org/fel2019/papers/web03.pdf ER -