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{tran:napac2022-mopa90,
author = {A.D. Tran and Y. Hao and J.L. Martinez Marin and B. Mustapha},
title = {{Relating Initial Distribution to Beam Loss on the Front End of a Heavy-Ion Linac Using Machine Learning}},
& booktitle = {Proc. NAPAC'22},
booktitle = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
pages = {263--266},
eid = {MOPA90},
language = {english},
keywords = {network, simulation, LEBT, emittance, controls},
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-MOPA90},
url = {https://jacow.org/napac2022/papers/mopa90.pdf},
abstract = {{This work demonstrates using a Neural Network and a Gaussian Process to model the ATLAS front-end. Various neural network architectures were created and trained on the machine settings and outputs to model the phase space projections. The model was then trained on a dataset, with non-linear distortion, to gauge the transferability of the model from simulation to machine.}},
}