Nanostructures are currently attracting attention as a medium for obtaining ultra-high-density plasmas for beam-driven or laser-driven acceleration. This study investigates Bayesian optimization in Laser Wakefield Acceleration (LWFA) to enhance solid-state plasma parameters towards achieving extremely high gradients on the order of TV/m or beyond, specifically focusing on nanostructured plasmas based on arrays of carbon nanotubes. Through Particle-In-Cell (PIC) simulations via EPOCH and custom Python scripts, we conducted a parameter analysis for various configurations of carbon nanotube arrays. Utilizing the open-source machine learning library BoTorch for optimization, our work resulted in a detailed database of simulation results. This enabled us to pinpoint optimal parameters for generating effective wakefields in these specialized plasmas. Ultimately, the results demonstrate that Bayesian optimization is an excellent tool for significantly refining parameter selection for nanostructures like carbon nanotube arrays, thus enabling the design of promising nanostructures for LWFA.

The interactions of charged particles with carbon nanotubes (CNTs) may excite electromagnetic modes in the electron gas that makes up the nanotube surface. This novel effect has recently been proposed as an alternative method to achieve ultra-high gradients for particle acceleration. In this contribution, the excitations produced by a localized point-like charge propagating in a single wall nanotube are described by means of the linearized hydrodynamic model. In this model, the electron gas is treated as a plasma with specific solid-state properties. The governing set of differential equations consists of the continuity and momentum equations for the electron fluid, in conjunction with Poisson's equation. Through numerical simulations, we investigate the influence of key factors, including the driving velocity, CNT radius, surface density and the friction (between the electron fluid and the ionic lattice) parameter, on the excited wakefields, comparing the results with Particle-in-Cell (PIC) simulations. A comprehensive discussion is presented to analyze similarities, differences and limitations of both methods. This research provides a valuable perspective on the potential use of CNTs to enhance particle acceleration techniques, paving the way for further advancements in high-energy physics and related fields.

Nanostructures are currently attracting attention as a medium for obtaining ultra-high-density plasmas for beam-driven or laser-driven acceleration. This study investigates Bayesian optimization in Laser Wakefield Acceleration (LWFA) to enhance solid-state plasma parameters towards achieving extremely high gradients on the order of TV/m or beyond, specifically focusing on nanostructured plasmas based on arrays of carbon nanotubes. Through Particle-In-Cell (PIC) simulations via EPOCH and custom Python scripts, we conducted a parameter analysis for various configurations of carbon nanotube arrays. Utilizing the open-source machine learning library BoTorch for optimization, our work resulted in a detailed database of simulation results. This enabled us to pinpoint optimal parameters for generating effective wakefields in these specialized plasmas. Ultimately, the results demonstrate that Bayesian optimization is an excellent tool for significantly refining parameter selection for nanostructures like carbon nanotube arrays, thus enabling the design of promising nanostructures for LWFA.