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Papers and notes


  • F. Hulphers, "Deep learning for anomaly detection in high-energy beam dump data from the Large Hadron Collider", Master's thesis 2022, Available:

  • C. Obermair, T. Cartier-Michaud, A. Apollonio, W. Millar, L. Felsberger, L. Fischl, H. S. Bovbjerg, D. Wollmann, W. Wuensch, N. Catalan-Lasheras, M. Boronat, F. Pernkopf, G. Burt, “Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities” Phys. Rev. Accel. Beams, 25(10), 104601. 2022, Available:

  • H.S. Bovbjerg, C. Obermair, A. Apollonio, T. Cartier-Michaud, W. Millar, Z.H. Tan , M. Shen, D. Wollmann, “Data Augmentation for Breakdown Prediction in CLIC RF Cavities” in Proc. IPAC'22, Available:

  • J. Barth, F. Bogyai, J.C. Garnier, M. Majewski, T. Ribeiro, A. Mnich, M. Pocwierz, R. Selvek, R. Simpson, A. Stanisz, D. Wollmann, M. Zerlauth, "A Modernized Architecture for the Post Mortem System at CERN", Proc. IPAC’22, (2022). 1557-1560, Available:

  • A. Lechner, P. Bélanger, I. Efthymiopoulos, L. Grob, B. Lindstrom, R. Schmidt, and D. Wollmann, "Dust-induced beam losses in the cryogenic arcs of the CERN Large Hadron Collider", Phys. Rev. Accel. Beams, 2022, Available:

  • L. Fischl, "Data Analysis of the XBox-2 Radiofrequency Cavity at CERN using Machine Learning Techniques", Master's thesis 2022, Available: