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RIS citation export for MOPGW026: Transfer Line Optics Design Using Machine Learning Techniques

AU  - Vilsmeier, D.M.
AU  - Bai, M.
AU  - Sapinski, M.
ED  - Boland, Mark
ED  - Tanaka, Hitoshi
ED  - Button, David
ED  - Dowd, Rohan
ED  - Schaa, Volker RW
ED  - Tan, Eugene
TI  - Transfer Line Optics Design Using Machine Learning Techniques
J2  - Proc. of IPAC2019, Melbourne, Australia, 19-24 May 2019
CY  - Melbourne, Australia
T2  - International Particle Accelerator Conference
T3  - 10
LA  - english
AB  - Optimization of transfer line optics is essential for delivering high quality beams to the experimental areas. This type of optimization is usually done by hand and relies on the experience of operators. The nature of this task is repetitive though highly complex. Besides optimizing the beam quality at the experiments this task is often accompanied by secondary objectives or requirements such as keeping the beam losses below an acceptable threshold. In the past years Deep Learning algorithms have experienced a rapid development and gave rise to various advanced software implementations which allow for straightforward usage of corresponding techniques, such as automatic differentiation and gradient backpropagation. We investigate the applicability and performance of these techniques in the field of transfer line optics optimization, specifically for the HADES beamline at GSI, in form of gradient-based differentiable simulators. We test our setup on results obtained from MADX simulations and compare our findings to different gradient-free optimization methods. Successfully employing such methods relieves operators from the tedious optimization tasks.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 139
EP  - 142
KW  - target
KW  - simulation
KW  - quadrupole
KW  - optics
KW  - operation
DA  - 2019/06
PY  - 2019
SN  - 978-3-95450-208-0
DO  - DOI: 10.18429/JACoW-IPAC2019-MOPGW026
UR  - http://jacow.org/ipac2019/papers/mopgw026.pdf
ER  -