The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
@InProceedings{scheinker:napac2019-wexbb1,
author = {A. Scheinker},
title = {{Adaptive Machine Learning and Automatic Tuning of Intense Electron Bunches in Particle Accelerators}},
booktitle = {Proc. NAPAC'19},
pages = {609--613},
paper = {WEXBB1},
language = {english},
keywords = {FEL, electron, controls, feedback, target},
venue = {Lansing, MI, USA},
series = {North American Particle Accelerator Conference},
number = {4},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2019},
issn = {2673-7000},
isbn = {978-3-95450-223-3},
doi = {10.18429/JACoW-NAPAC2019-WEXBB1},
url = {http://jacow.org/napac2019/papers/wexbb1.pdf},
note = {https://doi.org/10.18429/JACoW-NAPAC2019-WEXBB1},
abstract = {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.},
}