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RIS citation export for WEPTS106: Accelerator Optimization using Big Data Science Techniques

AU  - Welsch, C.P.
ED  - Boland, Mark
ED  - Tanaka, Hitoshi
ED  - Button, David
ED  - Dowd, Rohan
ED  - Schaa, Volker RW
ED  - Tan, Eugene
TI  - Accelerator Optimization using Big Data Science Techniques
J2  - Proc. of IPAC2019, Melbourne, Australia, 19-24 May 2019
CY  - Melbourne, Australia
T2  - International Particle Accelerator Conference
T3  - 10
LA  - english
AB  - Managing, analyzing and interpreting large, complex datasets and high rates of data flow is a growing challenge for many areas of science and industry. At particle accelerators and light sources, this data flow occurs both, in the experiments as well as the machine itself. The Liverpool Big Data Science Center for Doctoral Training (LIV. DAT) was established in 2017 to tackle the challenges in Monte Carlo modelling, high performance computing, machine learning and data analysis across particle, nuclear and astrophysics, as well as accelerator science. LIV. DAT is currently training 24 PHD students, making it one of the largest initiatives of this type in the world. This contribution presents research results obtained to date in projects that focus on the application of big data techniques within accelerator R&D.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 3370
EP  - 3373
KW  - plasma
KW  - experiment
KW  - radiation
KW  - electron
KW  - proton
DA  - 2019/06
PY  - 2019
SN  - 978-3-95450-208-0
DO  - DOI: 10.18429/JACoW-IPAC2019-WEPTS106
UR  - http://jacow.org/ipac2019/papers/wepts106.pdf
ER  -