Multivariate optimization of a high brightness dc gun photoinjector

Abstract

We have conducted a multiobjective computational optimization of a high brightness, high average current photoinjector under development at Cornell University. This injector employs a dc photoemission electron gun. Using evolutionary algorithms combined with parallel computing resources, the multivariate parameter space of the photoinjector was explored for optimal values. This powerful computational tool allows an extensive study of complex and nonlinear systems such as the space-charge dominated regions of an accelerator, and has broad areas of potential application to accelerator physics and engineering problems. In the present case, the optimized injector is simulated to deliver beam of very high quality (e.g., a rms normalized emittance of 0.1 mm mrad for 0.1 nC, and 0.7 mm mrad for 1 nC bunches). The field strengths of the active elements of the injector are moderate and technically practical. The relevance of these results to various novel linac-based accelerator proposals is pointed out.

Tailored electron bunches with smooth current profiles for enhanced transformer ratios in beam-driven acceleration

“…The program GENETICOPTIMIZER implements an evolutionary Pareto algorithm similar to the one described in Ref. [37]. The optimized accelerator settings are summarized in Table II.…”

Experimental investigation of an optimum configuration for a high-voltage photoemission gun for operation at≥500 kV

“…Generation of high brightness beam with 90% normalized emittances of 0.29 μm in the vertical plane and 0.51 μm in the horizontal plane with bunch charge of 77 pC was also demonstrated at the Cornell photoinjector with a 350 kV photoemission gun equipped with a GaAs photocathode [4]. Further increase of the gun voltage is desirable to reduce space charge induced emittance growth especially for high bunch charge operation required for a MHz-class x-ray FEL [5,6]. However, the gun's operational voltage has been limited to 350 kV or lower, mainly because of the field emission problem, since the first 500-kV dc photoemission gun was proposed in 1991 [7].…”

Optimizing the lattice design of a diffraction-limited storage ring with a rational combination of particle swarm and genetic algorithms

“…In contrast, a more efficient way is to use stochastic optimization algorithms, e.g., the multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO). The MOGA methods mimic the process of natural selection and evolution of species, and have been widely applied to many accelerator optimization problems [23][24][25][26][27]. While MOPSO emulates the self-organizing behavior of social animal living in group, and has been recently used to optimize the linac operation and nonlinear dynamics of storage rings [28][29][30].…”