scholarly journals Automated analysis of hot spot X-ray images at the National Ignition Facility

2016 ◽  
Vol 87 (11) ◽  
pp. 11E334 ◽  
Author(s):  
S. F. Khan ◽  
N. Izumi ◽  
S. Glenn ◽  
R. Tommasini ◽  
L. R. Benedetti ◽  
...  
Keyword(s):  
Hot Spot ◽  
Automated Analysis ◽  
National Ignition Facility ◽  
X Ray

scholarly journals Using time-resolved penumbral imaging to measure low hot spot x-ray emission signals from capsule implosions at the National Ignition Facility

2018 ◽  
Vol 89 (10) ◽  
pp. 10G111 ◽  
Author(s):  
D. T. Bishel ◽  
B. Bachmann ◽  
A. Yi ◽  
D. Kraus ◽  
L. Divol ◽  
...  
Keyword(s):  
Hot Spot ◽  
National Ignition Facility ◽  
Time Resolved ◽  
X Ray ◽  
Penumbral Imaging

scholarly journals Observations of anomalous x-ray emission at early stages of hot-spot formation in deuterium-tritium cryogenic implosions

2021 ◽  
Vol 103 (2) ◽  
Author(s):  
R. C. Shah ◽  
S. X. Hu ◽  
I. V. Igumenshchev ◽  
J. Baltazar ◽  
D. Cao ◽  
...  
Keyword(s):  
Hot Spot ◽  
X Ray ◽  
Early Stages ◽  
Hot Spot Formation ◽  
Spot Formation

scholarly journals Investigation of high X-ray conversion efficiency Kr filled gas sources at the National Ignition Facility

2019 ◽  
Vol 26 (6) ◽  
pp. 063105 ◽  
Author(s):  
M. J. May ◽  
G. E. Kemp ◽  
J. D. Colvin ◽  
D. A. Liedahl ◽  
P. L. Poole ◽  
...  
Keyword(s):  
Conversion Efficiency ◽  
National Ignition Facility ◽  
X Ray

scholarly journals Multi-keV x-ray source development experiments on the National Ignition Facility

2010 ◽  
Vol 17 (8) ◽  
pp. 082701 ◽  
Author(s):  
K. B. Fournier ◽  
M. J. May ◽  
J. D. Colvin ◽  
J. O. Kane ◽  
M. Schneider ◽  
...  
Keyword(s):  
National Ignition Facility ◽  
X Ray

scholarly journals Capsule design for the National Ignition Facility

1999 ◽  
Vol 17 (2) ◽  
pp. 217-224 ◽  
Author(s):  
T.R. DITTRICH ◽  
S.W. HAAN ◽  
M.M. MARINAK ◽  
D.E. HINKEL ◽  
S.M. POLLAINE ◽  
...  
Keyword(s):  
Dopant Concentration ◽  
Three Dimensions ◽  
Initial Surface ◽  
National Ignition Facility ◽  
X Ray ◽  
Ongoing Effort ◽  
Surface Finishes ◽  
Material Homogeneity ◽  
Indirect Drive ◽  
Reduced Scale

Several choices exist in the design and production of capsules intended to ignite and propagate fusion burn of the deuterium–tritium (D–T) fuel when imploded by indirect drive at the National Ignition Facility (NIF). These choices include ablator material, ablator dopant concentration and distribution, capsule dimensions, and X-ray drive profile (shock timings and strengths). The choice of ablator material must also include fabrication and material characteristics, such as attainable surface finishes, permeability, strength, transparency to radio frequency and infrared radiation, thermal conductivity, and material homogeneity. Understanding the advantages and/or limitations of these choices is an ongoing effort for LLNL and LANL designers. At this time, simulations in one-, two-, and three-dimensions show that capsules with either a copper-doped beryllium or a polyimide (C22H10N2O4) ablator material have both the least sensitivity to initial surface roughnesses and favorable fabrication qualities. Simulations also indicate the existence of capsule designs based on these ablator materials which ignite and burn when imploded by less than nominal laser performance (900-kJ energy, 250-TW power, producing 250-eV peak radiation temperature). We will describe and compare these reduced-scale capsules, in addition to several designs which use the expected 300-eV peak X-ray drive obtained from operating the NIF laser at 1.3 MJ and 500 TW.

scholarly journals Bremsstrahlung x-ray generation for high optical depth radiography applications on the National Ignition Facility

2018 ◽  
Vol 89 (10) ◽  
pp. 10G121 ◽  
Author(s):  
C. M. Huntington ◽  
J. M. McNaney ◽  
E. Gumbrell ◽  
A. Krygier ◽  
C. Wehrenberg ◽  
...  
Keyword(s):  
Optical Depth ◽  
National Ignition Facility ◽  
X Ray

Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool

2003 ◽  
Vol 9 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Paul G. Kotula ◽  
Michael R. Keenan ◽  
Joseph R. Michael
Keyword(s):  
Image Data ◽  
Large Data ◽  
Automated Analysis ◽  
Phase Identification ◽  
Data Sets ◽  
Multivariate Statistical ◽  
X Ray ◽  
Analysis Technique ◽  
Statistical Analysis Technique ◽  
Multi Phase

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

scholarly journals The Effect of Image Resolution on Automated Classification of Chest X-rays

2021 ◽  
Author(s):  
Md Inzamam Ul Haque ◽  
Abhishek K Dubey ◽  
Jacob D Hinkle
Keyword(s):  
High Resolution ◽  
Automated Analysis ◽  
Image Resolution ◽  
Free Text ◽  
X Rays ◽  
Training Time ◽  
X Ray ◽  
Radiology Reports ◽  
Chest X Ray ◽  
High Resolution Images

Deep learning models have received much attention lately for their ability to achieve expert-level performance on the accurate automated analysis of chest X-rays. Although publicly available chest X-ray datasets include high resolution images, most models are trained on reduced size images due to limitations on GPU memory and training time. As compute capability continues to advance, it will become feasible to train large convolutional neural networks on high-resolution images. This study is based on the publicly available MIMIC-CXR-JPG dataset, comprising 377,110 high resolution chest X-ray images, and provided with 14 labels to the corresponding free-text radiology reports. We find, interestingly, that tasks that require a large receptive field are better suited to downscaled input images, and we verify this qualitatively by inspecting effective receptive fields and class activation maps of trained models. Finally, we show that stacking an ensemble across resolutions outperforms each individual learner at all input resolutions while providing interpretable scale weights, suggesting that multi-scale features are crucially important to information extraction from high-resolution chest X-rays.

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