Collaborative Research: Enhancing Laser Based Ion Sources with High Data Rate Techniques

合作研究：利用高数据速率技术增强基于激光的离子源

• 批准号: 2109222
• 负责人: Christopher Orban
• 金额: $ 47.77万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2109222&HistoricalAwards=false
• 关键词: Collaborative; Research; Enhancing; Laser; Based

As laser technology continues to improve, it is important to investigate how laser interactions with matter can be better controlled and optimized to develop new applications. This research project will investigate two methods to enhance intense laser interactions in order to accelerate protons and ions. One method involves using a machine learning algorithm, which is a form of artificial intelligence, to control the laser system. The other method involves splitting the laser pulse into two beams and using the constructive interference to as much as double the intensity on target without requiring additional laser energy. The goal of both methods is to maximize the numbers and the energies of the protons and ions ejected from intense laser interactions. The intellectual products of this research may have a profound impact on efforts to use intense laser systems to perform proton radiography for a variety of biomedical, industrial, and defense purposes. The project will support several graduate and undergraduate students, and its members will be actively involved in several efforts to increase cultural, socioeconomic, and gender diversity in STEM.There is great potential for intense laser systems to become a useful source of energetic ions for a variety of scientific and engineering applications, but the properties of laser accelerated protons and ions are typically far from ideal and the peak ion energy scales weakly with laser intensity. This project will address these problems by investigating two complementary techniques to enhance and control laser interactions with solid density targets. Specifically, machine learning methods will be used to control multiple experimental parameters on intense laser systems to examine how much optimization and control over the resulting proton spectrum can be achieved. The other technique involves using the constructive interference of two laser pulses to significantly increase the effective intensity and absorption of laser light. Both techniques leverage high repetition rate laser systems such as the kHz repetition rate Extreme Light intense laser system at Wright Patterson Air Force Base, which will be involved in this project. Particle-in-Cell simulations will be performed to better understand optimal conditions for ion acceleration and to understand the physics of why the double pulse technique is so effective.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着激光技术的不断改进，研究如何更好地控制和优化激光与物质的相互作用，以开发新的应用是很重要的。本研究项目将研究两种增强强激光相互作用以加速质子和离子的方法。一种方法涉及使用机器学习算法来控制激光系统，机器学习算法是人工智能的一种形式。另一种方法包括将激光脉冲分成两束，并使用建设性干涉将目标的强度增加一倍，而不需要额外的激光能量。这两种方法的目标都是最大化从强激光相互作用中射出的质子和离子的数量和能量。这项研究的智力产品可能会对使用强激光系统进行各种生物医学、工业和国防目的的质子照相的努力产生深远的影响。该项目将支持几名研究生和本科生，其成员将积极参与几项努力，以增加STEM的文化、社会经济和性别多样性。强激光系统有巨大的潜力成为各种科学和工程应用中有用的高能离子源，但激光加速质子和离子的性质通常远不理想，峰值离子能量随激光强度的变化而减弱。该项目将通过研究两种增强和控制激光与固体密度靶相互作用的互补技术来解决这些问题。具体地说，将使用机器学习方法来控制强激光系统上的多个实验参数，以检查可以在多大程度上实现对所产生的质子光谱的优化和控制。另一种技术涉及利用两个激光脉冲的建设性干涉来显著增加激光的有效强度和吸收。这两种技术都利用了高重复频率激光系统，例如莱特帕特森空军基地的千赫重复频率极光强激光系统，该系统将参与该项目。将进行细胞内粒子模拟，以更好地了解离子加速的最佳条件，并了解双脉冲技术如此有效的物理原因。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。