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{jaworski:napac2022-mozd4,
author = {M.A. Jaworski and D.C. Moir and S. Szustkowski},
title = {{Uncertainty Quantification of Beam Parameters in a Linear Induction Accelerator Inferred from Bayesian Analysis of Solenoid Scans}},
& booktitle = {Proc. NAPAC'22},
booktitle = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
pages = {34--36},
eid = {MOZD4},
language = {english},
keywords = {solenoid, experiment, electron, induction, space-charge},
venue = {Albuquerque, NM, USA},
series = {International Particle Accelerator Conference},
number = {5},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2022},
issn = {2673-7000},
isbn = {978-3-95450-232-5},
doi = {10.18429/JACoW-NAPAC2022-MOZD4},
url = {https://jacow.org/napac2022/papers/mozd4.pdf},
abstract = {{Linear induction accelerators (LIAs) such as the DARHT at Los Alamos National Laboratory make use of the beam envelope equation to simulate the beam and design experiments. Accepted practice is to infer beam parameters using the solenoid scan technique with optical transition radiation (OTR) beam profiles. These scans are then analyzed with an envelope equation solver to find a solution consistent with the data and machine parameters (beam energy, current, magnetic field, and geometry). The most common code for this purpose with flash-radiography LIAs is xtr [1]. The code assumes the machine parameters are perfectly known and that beam profiles will follow a normal distribution about the best fit and solves by minimizing a chi-square-like metric. We construct a Bayesian model of the beam parameters allowing maching parameters, such as solenoid position, to vary within reasonable uncertainty bounds. Posterior distribution functions are constructed using Markov-Chain Monte Carlo (MCMC) methods to evaluate the accuracy of the xtr solution uncertainties and the impact of finite precision in measurements.}},
}