Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration

TL;DR

This paper introduces a multi-objective, multi-fidelity Bayesian optimization method for laser-plasma accelerators, enabling efficient exploration of trade-offs and reducing computational costs by adaptively selecting simulation fidelity.

Contribution

It presents the first multi-objective Bayesian optimization approach for laser-plasma accelerators, combining multi-fidelity techniques to significantly cut down simulation time.

Findings

Multi-objective optimization achieves comparable results to single-objective methods.

The approach allows instant evaluation of new objectives.

Optimization time is reduced by an order of magnitude using multi-fidelity methods.

Abstract

Beam parameter optimization in accelerators involves multiple, sometimes competing objectives. Condensing these individual objectives into a single figure of merit unavoidably results in a bias towards particular outcomes, in absence of prior knowledge often in a non-desired way. Finding an optimal objective definition then requires operators to iterate over many possible objective weights and definitions, a process that can take many times longer than the optimization itself. A more versatile approach is multi-objective optimization, which establishes the trade-off curve or Pareto front between objectives. Here we present the first results on multi-objective Bayesian optimization of a simulated laser-plasma accelerator. We find that multi-objective optimization reaches comparable performance to its single-objective counterparts while allowing for instant evaluation of entirely new objectives. This dramatically reduces the time required to find appropriate objective definitions for new problems. Additionally, our multi-objective, multi-fidelity method reduces the time required for an optimization run by an order of magnitude. It does so by dynamically choosing simulation resolution and box size, requiring fewer slow and expensive simulations as it learns about the Pareto-optimal solutions from fast low-resolution runs. The techniques demonstrated in this paper can easily be translated into many different computational and experimental use cases beyond accelerator optimization.