JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
TY - CONF AU - Sharankova, R.V. AU - Mwaniki, M.W. AU - Seiya, K. AU - Wesley, M.E. ED - Biedron, Sandra ED - Simakov, Evgenya ED - Milton, Stephen ED - Anisimov, Petr M. ED - Schaa, Volker R.W. TI - Diagnostics for LINAC Optimization with Machine Learning J2 - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022 CY - Albuquerque, NM, USA T2 - International Particle Accelerator Conference T3 - 5 LA - english AB - The Fermilab Linac delivers 400 MeV H⁻ beam to the rest of the accelerator chain. Providing stable intensity, energy, and emittance is key since it directly affects downstream machines. To operate high current beam, accelerators must minimize uncontrolled particle loss; this is generally accomplished by minimizing beam emittance. Ambient temperature and humidity variations are known to affect resonance frequency of the accelerating cavities which induces emittance growth. In addition, the energy and phase space distribution of particles emerging from the ion source are subject to fluctuations. To counter these effects we are working on implementing dynamic longitudinal parameter optimization based on Machine Learning (ML). As an input for the ML model, signals from beam diagnostic have to be well understand and reliable. We have been revisiting diagnostics in the linac. In this presentation we discuss the status of the diagnostics and beam studies as well as the status and plans for ML-based optimization. PB - JACoW Publishing CP - Geneva, Switzerland SP - 139 EP - 142 KW - linac KW - DTL KW - network KW - controls KW - diagnostics DA - 2022/10 PY - 2022 SN - 2673-7000 SN - 978-3-95450-232-5 DO - doi:10.18429/JACoW-NAPAC2022-MOPA41 UR - https://jacow.org/napac2022/papers/mopa41.pdf ER -