Beam line optimization using derivative-free algorithms
- The present study focuses on the beam line optimization from the heavy-ion synchrotron SIS18 to the HADES experiment. BOBYQA (Bound Optimization BY Quadratic Approximation) solves bound constrained optimization problems without using derivatives of the objective function. The Bayesian optimization is another strategy for global optimization of costly, noisy functions without using derivatives. A python programming interface to MADX allow the use of the python implementation of BOBYQA and Bayesian method. This gave the possibility to use tracking simulation with MADX to determine the loss budget for each lattice setting during the optimization and compare both optimization methods.
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| Author: | Sabrina Appel, Stephan ReimannGND |
|---|---|
| URN: | urn:nbn:de:hebis:30:3-589647 |
| DOI: | https://doi.org/10.18429/JACoW-IPAC2019-WEPMP005 |
| ISBN: | 978-3-95450-208-0 |
| Parent Title (English): | IPAC2019 = Proceedings of the 10th International Particle Accelerator Conference, [Melbourne, Australia, 2019] |
| Publisher: | JACoW Publishing |
| Place of publication: | Geneva |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Completion: | 2019 |
| Year of first Publication: | 2019 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Release Date: | 2022/01/27 |
| Page Number: | 4 |
| First Page: | 2307 |
| Last Page: | 2310 |
| HeBIS-PPN: | 49053354X |
| Institutes: | Physik / Physik |
| Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Creative Commons - Namensnennung 3.0 |
