scholarly journals Recent improvements in Thomson scattering data analysis

1980 ◽  
Author(s):  
M.S. Tillack ◽  
E.A. Lazarus
Keyword(s):  
Data Analysis ◽  
Thomson Scattering ◽  
Scattering Data

scholarly journals Collective Thomson scattering data analysis for Wendelstein 7-X

2017 ◽  
Vol 12 (08) ◽  
pp. C08015-C08015 ◽  
Author(s):  
I. Abramovic ◽  
A. Pavone ◽  
J. Svensson ◽  
D. Moseev ◽  
M. Salewski ◽  
...  
Keyword(s):  
Data Analysis ◽  
Thomson Scattering ◽  
Scattering Data ◽  
Collective Thomson Scattering

Revision of TV Thomson scattering data analysis and detection of profile structure

2003 ◽  
Vol 74 (9) ◽  
pp. 3998-4011 ◽  
Author(s):  
B. Ph. van Milligen ◽  
I. G. J. Classen ◽  
C. J. Barth
Keyword(s):  
Data Analysis ◽  
Thomson Scattering ◽  
Scattering Data

scholarly journals Global Small-Angle X-ray Scattering Data Analysis of Triacylglycerols in the Molten State (Part I)

2018 ◽  
Vol 122 (45) ◽  
pp. 10320-10329 ◽  
Author(s):  
Amin Sadeghpour ◽  
Marjorie Ladd Parada ◽  
Josélio Vieira ◽  
Megan Povey ◽  
Michael Rappolt
Keyword(s):  
Data Analysis ◽  
Small Angle ◽  
Scattering Data ◽  
Molten State ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

scholarly journals Performance Improvement in Real-Time Mapping of Thomson Scattering Data to Flux Coordinates in LHD

2012 ◽  
Vol 7 (0) ◽  
pp. 2405058-2405058 ◽  
Author(s):  
Masahiko EMOTO ◽  
Masanobu YOSHIDA ◽  
Chihiro SUZUKI ◽  
Yasuhiro SUZUKI ◽  
Katsumi IDA ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Improvement ◽  
Thomson Scattering ◽  
Scattering Data

scholarly journals Small Angle Scattering Data Analysis Assisted by Machine Learning Methods

MRS Advances ◽  
2020 ◽  
Vol 5 (29-30) ◽  
pp. 1577-1584
Author(s):  
Changwoo Do ◽  
Wei-Ren Chen ◽  
Sangkeun Lee
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Small Angle ◽  
Confusion Matrix ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Search Spaces ◽  
Angle Scattering ◽  
Analysis Workflow ◽  
The Right

ABSTRACTSmall angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.

Small-angle scattering data analysis for dense polydisperse systems: the FLAC program

2000 ◽  
Vol 133 (1) ◽  
pp. 66-75 ◽  
Author(s):  
Flavio Carsughi ◽  
Achille Giacometti ◽  
Domenico Gazzillo
Keyword(s):  
Data Analysis ◽  
Small Angle ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Angle Scattering

scholarly journals The Small-Angle Neutron Scattering Data Analysis of the Phospholipid Transport Nanosystem Structure

2018 ◽  
Vol 1023 ◽  
pp. 012017 ◽  
Author(s):  
E.V. Zemlyanaya ◽  
M.A. Kiselev ◽  
E.I. Zhabitskaya ◽  
V.L. Aksenov ◽  
O.M. Ipatova ◽  
...  
Keyword(s):  
Data Analysis ◽  
Neutron Scattering ◽  
Small Angle ◽  
Small Angle Neutron Scattering ◽  
Scattering Data ◽  
Phospholipid Transport
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