Benchmarking collisional rates and hot electron transport in high-intensity laser-matter interaction

点击翻译按钮获取中文摘要

"The interaction of high-intensity lasers with matter has been demonstrated as a versatile source of energetic protons with desirable characteristics (e.g. ultra-short durations) that cannot be achieved with conventional sources. These characteristics could facilitate societally-impactful applications such a high-dose rate radiobiology. During the acceleration of the protons, the laser energy is transferred to the proton beam via a multi-step processes in which the target material is rapidly ionised and a population of highly energetic electrons are produced. It is these 'hot' electrons that govern the evolution of the target material and the properties of the resultant proton beam determining its suitability for applications which also include medical isotope generation, materials testing and radiation damage testing, and exploration of fundamental high-energy density (HED) physics of relevance to astrophysics and fusion energy. Measurement of the evolution of the 'hot' electron population and the dense plasma target with femtosecond temporal resolution is crucial to accurately model the transfer of energy from the laser to the proton beam and to optimise these radiation sources to fulfil their potential. A novel diagnostic technique which allows femtosecond resolution measurements of the ultra-fast excitation and relaxation of a material has recently been demonstrated. This relies upon resonantly stimulated x-ray line emission, which is driven using high-brightness, narrow bandwidth x-ray pulses from an XFEL. By utilising facilities which combine an XFEL and a high-intensity laser, this diagnostic technique will allow unprecedented insight into the ultra-fast process of laser energy transfer to proton beams. In addition, by exploiting the new high-repetition rate capability of modern high-intensity lasers, it is possible utilise online feedback between the diagnostics and experimental controls to efficiently map the multi-dimensional parameter space of laser-driven proton acceleration and optimally exploit access to world-class facilities. The huge increase in high-value data that can be obtained via this methodology also enables inter-disciplinary work employing novel machine learning tools to enhance understanding of processes key to fundamental HED physics as well as deepening understanding of laser plasma accelerators thereby facilitating their development as tools for applications. "