The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
TY - CONF AU - Scheinker, A. ED - Yamazaki, Yoshishige ED - Raubenheimer, Tor ED - McCausey, Amy ED - Schaa, Volker RW TI - Adaptive Machine Learning and Automatic Tuning of Intense Electron Bunches in Particle Accelerators J2 - Proc. of NAPAC2019, Lansing, MI, USA, 01-06 September 2019 CY - Lansing, MI, USA T2 - North American Particle Accelerator Conference T3 - 4 LA - english AB - Machine learning and in particular neural networks, have been around for a very long time. In recent years, thanks to growth in computing power, neural networks have reshaped many fields of research, including self driving cars, computers playing complex video games, image identification, and even particle accelerators. In this tutorial, I will first present an introduction to machine learning for beginners and will also touch on a few aspects of adaptive control theory. I will then introduce some problems in particle accelerators and present how they have been approached utilizing machine learning techniques as well as adaptive machine learning approaches, for automatically tuning extremely short and high intensity electron bunches in free electron lasers. PB - JACoW Publishing CP - Geneva, Switzerland SP - 609 EP - 613 KW - FEL KW - electron KW - controls KW - feedback KW - target DA - 2019/10 PY - 2019 SN - 2673-7000 SN - 978-3-95450-223-3 DO - doi:10.18429/JACoW-NAPAC2019-WEXBB1 UR - http://jacow.org/napac2019/papers/wexbb1.pdf ER -