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RIS citation export for THBPP03: Deep Learning Methods on Neutron Scattering Data

AU  - Mutti, P.
AU  - Cecillon, F.
AU  - Le Goc, Y.
AU  - Song, G.
ED  - White, Karen S.
ED  - Brown, Kevin A.
ED  - Dyer, Philip S.
ED  - Schaa, Volker RW
TI  - Deep Learning Methods on Neutron Scattering Data
J2  - Proc. of ICALEPCS2019, New York, NY, USA, 05-11 October 2019
CY  - New York, NY, USA
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 17
LA  - english
AB  - Recently, by using deep learning methods, computers are able to surpass or come close to matching human performance on image analysis and pattern recognition. This advanced method could also help interpreting data from neutron scattering experiments. Those data contain rich scientific information about structure and dynamics of materials under investigation, and deep learning could help researchers better understand the link between experimental data and materials properties. We applied deep learning techniques to scientific neutron scattering data. This is a complex problem due to the multi-parameter space we have to deal with. We have used a convolutional neural network-based model to evaluate the quality of experimental neutron scattering images, which can be influenced by instrument configuration, sample and sample environment parameters. Sample structure can be deduced during data collection that can be therefore optimized. The neural network model can predict the experimental parameters to properly setup the instrument and derive the best measurement strategy. This results in a higher quality of data obtained in a shorter time, facilitating data analysis and interpretation.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 1580
EP  - 1583
KW  - scattering
KW  - experiment
KW  - neutron
KW  - network
KW  - detector
DA  - 2020/08
PY  - 2020
SN  - 2226-0358
SN  - 978-3-95450-209-7
DO  - doi:10.18429/JACoW-ICALEPCS2019-THBPP03
UR  - https://jacow.org/icalepcs2019/papers/thbpp03.pdf
ER  -