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Simulation and Optimisation of the JLab/AES DC Photo-injector

Simulation and Optimisation of the JLab/AES DC Photo-injector. Fay Hannon. Overview. Background specification Simulation 135pC scenario By hand Multivariate optimisation Simulation 1nC Conclusions. Background. Designed by Advanced Energy Systems

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Simulation and Optimisation of the JLab/AES DC Photo-injector

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  1. Simulation and Optimisation of the JLab/AES DC Photo-injector Fay Hannon

  2. Overview • Background • specification • Simulation 135pC scenario • By hand • Multivariate optimisation • Simulation 1nC • Conclusions

  3. Background • Designed by Advanced Energy Systems • Many iterations (7 cell +/- 3rd harmonic) • Now a demo of 1nC/135pC • Not completely modelled at AES in present form • Possible upgrade route

  4. Injector Layout Minimise the drift between gun and accelerating cavities. Single cell cavities. 750MHz

  5. Specification For 7 cell design

  6. Simulation: ASTRA model • A Space-charge TRacking Algorithm • For rotationally symmetric components on-axis field maps give good approximation. Radial E and B fields generated from derivative. • Can be fully 3D • Field maps created using SUPERFISH/ POISSON

  7. Layout & Field Maps

  8. Simulation technique • By hand: aim for spec. • Piecewise methodology, starting with gun and solenoid • Parameter scans • Start with 135pC (easier) case & FEL laser properties for initial distribution • Thermal emittance excluded – rough modelling • Difficult to meet the target energy

  9. 135pC Simulation

  10. Results 135pC JLab laser parameters assumed for initial distribution

  11. Multivariate Optimisation Ivan Bazarov @ Cornell 100mA injector with DC gun – showed improved design. Evolutionary algorithm Can optimise a number of beam parameters simultaneously • Start with 2 objectives: minimise longitudinal and transverse emittance • After a number of generations solutions converge on an optimisation front • Pick solution that is best suited to the application

  12. PISA Platform and programming language independent Interface for Search Algorithms Institute of Technology, Zurich Evaluate candidate solutions -> select promising candidates -> generate new candidates by variation - Text based interface for search algorithms - Consists of 2 parts: variator and selector

  13. Selector: algorithm specific operations. Assigning fitness functions. Creating new mating pools from the better solutions. Various selector modules can be chosen eg. Simulated annealing, genetic algorithms. Variator: Problem specific. Objectives, decision variables and constraints. Crossing and mutation operators that act on the candidates chosen by selector. Variator includes a parallel implementation of ASTRA. Variator and Selector talk to each other via a state machine and common text files

  14. Initial Population Decision variables Field strengths – phases etc Evaluate: ASTRA & parallel processing J-Lab HPC 5*128 node cluster (24hr limit) Constraints (spec) Objectives ‘fitness’ evaluated Solutions Mating pool created from best solutions. Crossing & mutation New Population ++ Generation

  15. 100mA Optimisation Problem Decision variables (10) Laser spot size Solenoid B 4 cavities: max gradient and phase Constraints (7) Bunch length < 3mm KE >7 MeV Longitudinal emittance < 55 keV mm Transverse size > 1.0 mm Energy spread < 70keV <x.x’>,<z dE> < 0 Objectives (2) Minimise transverse emittance Minimise longitudinal emittance Selector Module: Strength pareto evolutionary algorithm 2 (SPEA2)

  16. Problems noted… Tail overtakes the head.

  17. Decision Variables

  18. 128 population 200 Generations 1k Macro-particles Tracked to 5m Evolution

  19. Results 135pC – 100 generations Population size 128, 200 generations, 1k macro-particles for ASTRA.

  20. Results 135pC – 200 generations Doesn’t meet longitudinal emittance spec of 15keV mm Other constraints met

  21. Results 135pC – XYrms 1 – 2.5mm Improvement – spec not met. Thermal emittance/halo increases.

  22. Try to re-arrange Conventionally 3rd harmonic cavities are placed last in an injector purely to make the longitudinal phase space linear

  23. Best Solution Comparison

  24. Best Solution Comparison Absence of a dedicated buncher cavity makes longitudinal spec. difficult to meet. First cell and 3rd harmonic doing the bunching.

  25. Energy trade off 7MeV was goal of 7 cell design. Energy can be reduced for demo. Lower energy – can get close to longitudinal spec

  26. Energy Trade Off

  27. What if bunch length a variable? 128 population 200 Generations 1k Macro-particles Tracked to 5m Trms < 10ps, Meet specification at 7MeV

  28. 1nC Simulation

  29. 1nC (7MeV constraint) Unsuccessful in achieving both goals (<45keV mm, <10um)

  30. 1nC Case All possible solutions (meeting spec) are below 5.5MeV

  31. Solution (4.6MeV)

  32. Shorter laser pulse Not typical optimal front because not enough generations

  33. Conclusions & Future plans • Specification can be met in both cases when the target energy is lowered to ~5MeV • Spec and 7MeV can be met if laser pulse is 10ps duration • Multivariate optimisation is an excellent tool for injector design (operating points and dependencies) • More cells – greater flexibility • Currently under construction at JLab • Gun & laser ready. Diagnostic beamline in design • More detailed simulation (thermal emittance, realistic distributions, macro particles)

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