Source localization for neutron imaging systems using convolutional neural networks

Saavedra, Gary Joseph; Geppert-Kleinrath, Verena; Danly, Chris Russell; Durocher, Mora; Wilde, Carl Huerstel; Fatherley, Valerie E.; Mendoza, Emily Faith; Tafoya, Landon Reese; Volegov, Petr; Fittinghoff, David; Rubery, Michael; Freeman, Matthew Stouten

doi:10.1063/5.0205472

Source localization for neutron imaging systems using convolutional neural networks

Journal Article · Tue Jun 18 00:00:00 EDT 2024 · Review of Scientific Instruments

DOI:https://doi.org/10.1063/5.0205472· OSTI ID:2426833

^[1]; ^[1]; ^[1]; ^[1]; ^[1]; ^[1]; Mendoza, Emily Faith ^[1]; ^[1]; ^[2]; ^[2]; ^[2]; ^[1]

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium–tritium shots.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001; AC52-07NA27344

OSTI ID:: 2426833

Alternate ID(s):: OSTI ID: 2475263

Report Number(s):: LA-UR--24-22060; LLNL--JRNL-870911

Journal Information:: Review of Scientific Instruments, Journal Name: Review of Scientific Instruments Journal Issue: 6 Vol. 95; ISSN 0034-6748

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

References (19)

A survey on ensemble learning Dong, Xibin; Yu, Zhiwen; Cao, Wenming Frontiers of Computer Science, Vol. 14, Issue 2 https://doi.org/10.1007/s11704-019-8208-z	journal	August 2019
Effect of data standardization on neural network training Shanker, M.; Hu, M. Y.; Hung, M. S. Omega, Vol. 24, Issue 4 https://doi.org/10.1016/0305-0483(96)00010-2	journal	August 1996
On hyperparameter optimization of machine learning algorithms: Theory and practice Yang, Li; Shami, Abdallah Neurocomputing, Vol. 415 https://doi.org/10.1016/j.neucom.2020.07.061	journal	November 2020
Artificial Neural Networks for Neuroscientists: A Primer Yang, Guangyu Robert; Wang, Xiao-Jing Neuron, Vol. 107, Issue 6 https://doi.org/10.1016/j.neuron.2020.09.005	journal	September 2020
A Convergent Variant of the Nelder–Mead Algorithm Price, C. J.; Coope, I. D.; Byatt, D. Journal of Optimization Theory and Applications, Vol. 113, Issue 1 https://doi.org/10.1023/a:1014849028575	journal	April 2002
Modeling the National Ignition Facility neutron imaging system Wilson, D. C.; Grim, G. P.; Tregillis, I. L. Review of Scientific Instruments, Vol. 81, Issue 10 https://doi.org/10.1063/1.3496993	journal	October 2010
The neutron imaging diagnostic at NIF (invited) Merrill, F. E.; Bower, D.; Buckles, R. Review of Scientific Instruments, Vol. 83, Issue 10 https://doi.org/10.1063/1.4739242	journal	October 2012
Simultaneous usage of pinhole and penumbral apertures for imaging small scale neutron sources from inertial confinement fusion experiments Guler, N.; Volegov, P.; Danly, C. R. Review of Scientific Instruments, Vol. 83, Issue 10 https://doi.org/10.1063/1.4746745	journal	October 2012
Neutron source reconstruction from pinhole imaging at National Ignition Facility Volegov, P.; Danly, C. R.; Fittinghoff, D. N. Review of Scientific Instruments, Vol. 85, Issue 2 https://doi.org/10.1063/1.4865456	journal	February 2014
Self characterization of a coded aperture array for neutron source imaging Volegov, P. L.; Danly, C. R.; Fittinghoff, D. N. Review of Scientific Instruments, Vol. 85, Issue 12 https://doi.org/10.1063/1.4902978	journal	December 2014
Combined neutron and x-ray imaging at the National Ignition Facility (invited) Danly, C. R.; Christensen, K.; Fatherley, V. E. Review of Scientific Instruments, Vol. 87, Issue 11 https://doi.org/10.1063/1.4962194	journal	October 2016
A liquid VI scintillator cell for fast-gated neutron imaging Geppert-Kleinrath, Verena; Freeman, Matthew S.; Hurlbut, Charles R. Review of Scientific Instruments, Vol. 89, Issue 10 https://doi.org/10.1063/1.5039362	journal	October 2018
Neutron time of flight (nToF) detectors for inertial fusion experiments Moore, A. S.; Schlossberg, D. J.; Appelbe, B. D. Review of Scientific Instruments, Vol. 94, Issue 6 https://doi.org/10.1063/5.0133655	journal	June 2023
A Simplex Method for Function Minimization Nelder, J. A.; Mead, R. The Computer Journal, Vol. 7, Issue 4 https://doi.org/10.1093/comjnl/7.4.308	journal	January 1965
Lawson Criterion for Ignition Exceeded in an Inertial Fusion Experiment Abu-Shawareb, H.; Acree, R.; Adams, P. Physical Review Letters, Vol. 129, Issue 7 https://doi.org/10.1103/physrevlett.129.075001	journal	August 2022
A Comprehensive Survey on Transfer Learning Zhuang, Fuzhen; Qi, Zhiyuan; Duan, Keyu Proceedings of the IEEE, Vol. 109, Issue 1 https://doi.org/10.1109/jproc.2020.3004555	journal	January 2021
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects Li, Zewen; Liu, Fan; Yang, Wenjie IEEE Transactions on Neural Networks and Learning Systems, Vol. 33, Issue 12 https://doi.org/10.1109/tnnls.2021.3084827	journal	December 2022
The Essential Physics of Medical Imaging, Third Edition. Mahesh, Mahadevappa Medical Physics, Vol. 40, Issue 7 https://doi.org/10.1118/1.4811156	journal	June 2013
Developments in Image Processing Using Deep Learning and Reinforcement Learning Valente, Jorge; António, João; Mora, Carlos Journal of Imaging, Vol. 9, Issue 10 https://doi.org/10.3390/jimaging9100207	journal	September 2023