Since the innovation of chirped-pulse amplification by Donna Strickland and Gerard Morou in 1985 (which received the 2018 Nobel Prize in Physics), laser technology has evolved such that we can create exceedingly short pulses of light (10-12 seconds) with extremely high powers (1015 watts) in small, focused spots (about a few microns). A prolific area of research that has emerged over the last two decades is the use of these high-intensity lasers to drive particle beams. In contrast to traditional particle accelerators, laser-plasma-based accelerators can create accelerating electric fields to drive particles in a few centimeters rather than many kilometers. Possible applications of these compact and deployable laser-driven particle accelerators include isotope production for medical and national security applications, proton therapy for cancer research, studies in material structure, and fusion. Advancements in high-repetition-rate laser systems - that is, lasers that can fire at rates greater than 1 Hz - and in machine-learning tools provide novel opportunities to accelerate the rate of discovery on these laser systems. This poster details new work at the intersection of computation, machine learning and laser-plasma-based acceleration for fast analysis of resulting particle sources toward the goal of tunable particle sources.
Abstract Type
poster
University
Massachusetts Institute of Technology
Abstract Title
Towards Rapid Learning for Short-Pulse Laser Ion Acceleration Experiments
Abstract Author(s)
R. Simpson, B.Z. Djordjevic, E. Grace, A. Kemp, J. Kim, J.D. Ludwig, D.A. Mariscal, G.G. Scott, K. Swanson, S. Wilks, G.J. Williams, T. Ma
Username
simpson2018
First Name
Raspberry
Last Name
Simpson
Program
lrgf
Area of Study
DOE NNSA LRGF
Poster Group
Group 1
Poster Number
1
Reviewer Approved
No