Bayesian optimization of beam quality of plasma accelerated electron beams
Typ
Examensarbete för masterexamen
Program
Publicerad
2021
Författare
Brogren, Frida
Hallborn, Hanna
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Laser-plasma acceleration is a promising novel technique for acceleration of charged
particles. A challenge with this technique is to maintain a high quality of the accelerated
particle bunch. In particular, for accelerated electrons used in Free Electron
Lasers, charge and shape of the energy spectrum are important parameters. The aim
of this project has been to evaluate and examine the use of Bayesian optimization
with respect to these parameters on the LUX laser-plasma accelerator. The focus
was to consider how the Bayesian optimization performed under noisy conditions.
An important part of Bayesian optimization algorithms is the acquisition function
which determines the next point to evaluate in the optimization iteration. In this
thesis, two acquisition functions were compared and evaluated from the performance
point of view.
In order to test and develop the algorithms, Particle-In-Cell (PIC) simulations were
used to emulate the LUX experiment. Further, for cheaper evaluation, a model of the
target surface was built from a vast amount of PIC simulated data using Gaussian
process regression. With this model, different sampling strategies for each parameter
set-point could be investigated. Noise was added to the input parameters as well,
yielding a more realistic imitation of the system. A significant improvement was seen
when the mean value of 20 input parameters and the mean value of corresponding
outputs were fed to the Bayesian optimization algorithm.
Beskrivning
Ämne/nyckelord
Gaussian processes , Bayesian optimization , Laser-plasma acceleration , Wakefield acceleration , Noisy Expected Improvement , Expected Improvement