RUI: Spatial and Temporal Dynamics of Matter in Intense Laser Fields

这项研究工作旨在更好地从根本上理解物质或量子真空与激光脉冲或其他强外场的相互作用。这项工作的重点是预测如果激光场强到足以触发电子-正电子对的产生，就会出现的新现象。这项工作将有助于激光控制物质创造过程的新时代的跨学科特征，可能会在原子、等离子体和激光物理之间提供新的联系。由于这些项目所审查的进程的根本性质，进展也可能加速其他领域的进展。例如，更好地了解电子-正电子动力学可能有助于明亮光源的相关发展，为在极短的时间尺度上控制原子、化学和生物过程提供新的方法。该项目的一项重要使命也是让本科生有机会获得个人研究经验，并为他们提供重要的技能，包括团队合作，获得解决严重研究问题的耐力和智力灵活性。对相对论量子动力学的理解将主要借助于计算机模拟，以考虑到由PI开发的空间和时间分辨率，以求解量子场理论狄拉克方程。它还将得到现代机器学习技术的支持，例如基于进化算法和神经网络的符号回归。其目标是开发新的理论方法，能够以完全的空间分辨率第一次洞察激光领域的内部动态。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。

This research work aims at providing a better fundamental understanding of the interaction of matter or the quantum vacuum with laser pulses or other strong external fields. The work focuses on new phenomena that are predicted to occur if laser fields are sufficiently intense to trigger the creation of electron-positron pairs. The interdisciplinary character of the new era of laser-controlled matter creation processes to which the work will contribute could provide new connections between atomic, plasma and laser physics. Due to the fundamental nature of the processes examined in these projects, progress could also accelerate advancements in other areas. For example, a better understanding of the electron-positron dynamics could benefit the related development of bright light sources, providing new ways to control atomic, chemical and biological processes on very short time scales. An important mission for the program is also to give undergraduate students the opportunity to gain personal research experience and provide them with important skills, including working as a team and gaining the endurance and intellectual flexibility to tackle serious research problems. The understanding of the relativistic quantum dynamics will be advanced primarily with the aid of computer simulations to allow for spatial and temporal resolution that has been developed by the PIs to solve the quantum field theoretical Dirac equation. It will also be supported by modern machine learning techniques, such as symbolic regression based on evolutionary algorithms as well as on neural networks. The goal is to develop novel theoretical approaches that can lead to a first insight into the internal dynamics inside the laser field with complete spatial resolution.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.