Next we investigate the development of ultra-intense laser-based sources of high energy ions, which is an important goal, with a variety of potential applications. One of the barriers to achieving this goal is the need to maximize the conversion efficiency from laser energy to ion energy. We apply a new approach to this problem, in which we use an evolutionary algorithm to optimize conversion efficiency by exploring variations of the target density profile with thousands of one-dimensional PIC simulations. We then compare this “optimal” target identified by the one-dimensional PIC simulations to more conventional choices, such as with an exponential scale length pre-plasma, with fully three-dimensional PIC simulations. The optimal target outperforms the conventional targets in terms of maximum ion energy by 20% and shows a significant enhancement of conversion efficiency to high energy ions. This target geometry enhances laser coupling to the electrons, while still allowing the laser to strongly reflect from an effectively thin target. These results underscore the potential of this statistics-driven approach for optimizing laser-plasma simulations and experiments. Finally, we present computational fluid dynamic simulations that model the formation of thin liquid targets. These simulations allow us to explore new types of targets that may be beneficial for high repetition rate laser plasma interactions.

High-energy (100s MeV), high spectral quality ion beams are important for many applications like radiography of plasmas, isochoric heating of materials, and tumor therapy. Advances in the development of intense short pulse lasers, recognized with the 2018 Nobel Prize in physics, have been seen as a very promising route to drive compact ion beam sources. However, despite signficant progress over the past two decades, the control of the ion beam properties remains an outstanding challenge. In this Thesis, we discuss two approaches for controlling and optimizing these laser-driven ion beams, using particle-in-cell simulations and theoretical analysis. First, we show that in laser radiation pressure acceleration, the spectral quality of the ion beam is determined by electron heating, which is dictated by the growth of a surface instability. We show that its growth rate imposes an upper limit on the laser pulse duration, and can limit the maximum peak ion beam energy. Next, we explore the development of a hybrid accelerator that combines the advantages of laser-driven beams (compact, high-charge, 10s MeV) with high-gradient RF acceleration in a meter-scale linac, eliminating the large and expensive radio frequency quadrupoles for bunching. Our one-to-one simulations show that the space-charge field plays a critical role in the acceleration effectiveness, and that by tuning the distance at which the laser-driven beam enters the RF, the space-charge field can be controlled such that it actually increases the beam capture. These are important in guiding future experimental developments, for example for the ultrashort laser pulses at state-of-the-art laser facilities and high-gradient linacs, for which we showcase the possibility of a compact (4.5 m) hybrid accelerator that produces a high-quality, high-charge 250 MeV proton beam.

The interaction of ultrashort and intense laser pulses with solid targets and dense plasmas is a rapidly developing area of physics, this being mostly due to the significant advancements in laser technology. There is, thus, a growing interest in diagnosing as accurately as possible the numerous phenomena related to the absorption and reflection of laser radiation. At the same time, envisaged experiments are in high demand of increased accuracy simulation software. As laser-plasma interaction modelings are experiencing a transition from computationally-intensive to data-intensive problems, traditional codes employed so far are starting to show their limitations. It is in this context that predictive modelings of laser-plasma interaction experiments are bound to reshape the definition of simulation software. This chapter focuses an entire class of predictive systems incorporating big data, advanced machine learning algorithms and deep learning, with improved accuracy and speed. Making use of terabytes of already available information (literature as well as simulation and experimental data) these systems enable the discovery and understanding of various physical phenomena occurring during interaction, hence allowing researchers to set up controlled experiments at optimal parameters. A comparative discussion in terms of challenges, advantages, bottlenecks, performances and suitability of laser-plasma interaction predictive systems is ultimately provided.

Introduction and handbook to high-power laser-matter interaction, laser generated plasma, nonlinear waves, particle acceleration, nonlinear optics, nonlinear dynamics, radiation transport, it provides a systematic review of the major results and developments of the past 25 years.

This book presents a unified view of the response of materials as a result of femtosecond laser excitation, introducing a general theory that captures both ultrashort-time non-thermal and long-time thermal phenomena. It includes a novel method for performing ultra-large-scale molecular dynamics simulations extending into experimental and technological spatial dimensions with ab-initio precision. For this, it introduces a new class of interatomic potentials, constructed from ab-initio data with the help of a self-learning algorithm, and verified by direct comparison with experiments in two different materials — the semiconductor silicon and the semimetal antimony. In addition to a detailed description of the new concepts introduced, as well as giving a timely review of ultrafast phenomena, the book provides a rigorous introduction to the field of laser–matter interaction and ab-initio description of solids, delivering a complete and self-contained examination of the topic from the very first principles. It explains, step by step from the basic physical principles, the underlying concepts in quantum mechanics, solid-state physics, thermodynamics, statistical mechanics, and electrodynamics, introducing all necessary mathematical theorems as well as their proofs. A collection of appendices provide the reader with an appropriate review of many fundamental mathematical concepts, as well as important analytical and numerical parameters used in the simulations.

Laser-Plasma Interactions and Applications covers the fundamental and applied aspects of high power laser-plasma physics. With an internationally renowned team of authors, the book broadens the knowledge of young researchers working in high power laser-plasma science by providing them with a thorough pedagogical grounding in the interaction of laser radiation with matter, laser-plasma accelerators, and inertial confinement fusion. The text is organised such that the theoretical foundations of the subject are discussed first, in Part I. In Part II, topics in the area of high energy density physics are covered. Parts III and IV deal with the applications to inertial confinement fusion and as a driver of particle and radiation sources, respectively. Finally, Part V describes the principle diagnostic, targetry, and computational approaches used in the field. This book is designed to give students a thorough foundation in the fundamental physics of laser-plasma interactions. It will also provide readers with knowledge of the latest research trends and elucidate future exciting challenges in laser-plasma science.

Conference Location and Date: Frascati (Rome), Italy, 24-29 May 2009