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@InProceedings{scheinker:napac2019-thxba3,
author = {A. Scheinker},
title = {{Adaptive Machine Learning and Feedback Control for Automatic Particle Accelerator Tuning}},
booktitle = {Proc. NAPAC'19},
pages = {916--918},
paper = {THXBA3},
language = {english},
keywords = {FEL, electron, controls, target, laser},
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-THXBA3},
url = {http://jacow.org/napac2019/papers/thxba3.pdf},
note = {https://doi.org/10.18429/JACoW-NAPAC2019-THXBA3},
abstract = {Free electron lasers (FEL) and plasma wakefield accelerators (PWA) are creating more and more complicated electron bunch configurations, including multi-color modes for FELs such as LCLS and LCLS-II and custom tailored bunch current profiles for PWAs such as FACET-II. These accelerators are also producing shorter and higher intensity bunches than before and require an ability to quickly switch between many different users with various specific phase space requirements. For some very exotic setups it can take hours of tuning to provide the beams that users require. In this work, we present results adaptive machine learning and model independent feedback techniques and their application in both the LCLS and European XFEL to 1) control electron bunch phase space to create desired current profiles and energy spreads by tuning FEL components automatically, 2) maximize the average pulse output energy of FELs by automatically tuning over 100 components simultaneously, 3) preliminary results on utilizing these techniques for non-invasive electron bunch longitudinal phase space diagnostics at PWAs.},
}