JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{einstein-curtis:icalepcs2023-tu1bco04,
author = {J.A. Einstein-Curtis and S.K. Barber and C.E. Berger and S.J. Coleman and N.M. Cook and J.P. Edelen and J. van Tilborg},
% author = {J.A. Einstein-Curtis and S.K. Barber and C.E. Berger and S.J. Coleman and N.M. Cook and J.P. Edelen and others},
% author = {J.A. Einstein-Curtis and others},
title = {{Laser Focal Position Correction Using FPGA-Based ML Models}},
% booktitle = {Proc. ICALEPCS'23},
booktitle = {Proc. 19th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'23)},
eventdate = {2023-10-09/2023-10-13},
pages = {262--266},
paper = {TU1BCO04},
language = {english},
keywords = {controls, laser, network, FPGA, simulation},
venue = {Cape Town, South Africa},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {19},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {02},
year = {2024},
issn = {2226-0358},
isbn = {978-3-95450-238-7},
doi = {10.18429/JACoW-ICALEPCS2023-TU1BCO04},
url = {https://jacow.org/icalepcs2023/papers/tu1bco04.pdf},
abstract = {{High repetition-rate, ultrafast laser systems play a critical role in a host of modern scientific and industrial applications. We present a diagnostic and correction scheme for controlling and determining laser focal position by utilizing fast wavefront sensor measurements from multiple positions to train a focal position predictor. This predictor and additional control algorithms have been integrated into a unified control interface and FPGA-based controller on beamlines at the Bella facility at LBNL. An optics section is adjusted online to provide the desired correction to the focal position on millisecond timescales by determining corrections for an actuator in a telescope section along the beamline. Our initial proof-of-principle demonstrations leveraged pre-compiled data and pre-trained networks operating ex-situ from the laser system. A framework for generating a low-level hardware description of ML-based correction algorithms on FPGA hardware was coupled directly to the beamline using the AMD Xilinx Vitis AI toolchain in conjunction with deployment scripts. Lastly, we consider the use of remote computing resources, such as the Sirepo scientific framework, to actively update these correction schemes and deploy models to a production environment. }},
}