Bayesian optimization of laser-plasma accelerators assisted by reduced physical models

Abstract

Particle-in-cell simulations are among the most essential tools for the modeling and optimization of laser-plasma accelerators, since they reproduce the physics from first principles. However, the high computational cost associated with them can severely limit the scope of parameter and design optimization studies. Here, we show that a multitask Bayesian optimization algorithm can be used to mitigate the need for such high-fidelity simulations by incorporating information from inexpensive evaluations of reduced physical models. In a proof-of-principle study, where a high-fidelity optimization with FBPIC is assisted by reduced-model simulations with Wake-T, the algorithm demonstrates an order-of-magnitude speedup. This opens a path for the cost-effective optimization of laser-plasma accelerators in large parameter spaces, an important step towards fulfilling the high beam quality requirements of future applications.

References in corpus (13)

• A spectral, quasi-cylindrical and dispersion-free Particle-In-Cell algorithm
• Beam loading in the nonlinear regime of plasma-based acceleration
• Bayesian optimization of a free-electron laser
• Automation and control of laser wakefield accelerators using Bayesian optimisation
• Observation of beam loading in a laser-plasma accelerator
• Physics-informed Gaussian Process for Online Optimization of Particle Accelerators
• libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations
• Laser beam coupling with capillary discharge plasma for laser wakefield acceleration applications
• 3D printing of gas jet nozzles for laser-plasma accelerators
• A novel fast simulation technique for axisymmetric PWFA configurations in the blowout regime
• Intrinsic energy spread and bunch length growth in plasma-based accelerators due to betatron motion
• A final focus system for injection into a laser plasma accelerator at the ARES linac
• Turn-Key Constrained Parameter Space Exploration for Particle Accelerators Using Bayesian Active Learning

Cited by in corpus (10)

• Colliding Pulse Injection of Polarized Electron Bunches in a Laser-Plasma Accelerator
• Bayesian optimization of electron energy from laser wakefield accelerators
• Optimization of the injection beam line at the Cooler Synchrotron COSY using Bayesian Optimization
• Bayesian Optimization Algorithms for Accelerator Physics
• Portable, heterogeneous ensemble workflows at scale using libEnsemble
• Experimental demonstration of a tomographic 5D phase-space reconstruction
• Neural-network-based longitudinal electric field prediction in nonlinear plasma wakefield accelerators
• Combined tools for Particle-In-Cell simulations performed with transversely asymmetric chirped lasers
• High brightness, symmetric electron bunch generation in a plasma wakefield accelerator via a radially-polarized plasma photocathode
• Optimized matching conditions for self-guided laser wakefield accelerators