2026-09-20T12:12:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1473652023-01-20T03:01:00Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Winslow, Lindley
Ma, Tammy
Simpson, Raspberry
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
2023-01-19T18:48:26Z
2023-01-19T18:48:26Z
2022-09
2022-10-12T19:35:09.877Z
https://hdl.handle.net/1721.1/147365
Since the innovation of chirped pulse amplification by Donna Strickland and Gerard Morou in 1985, laser technology has evolved such that we can create short pulses of light (10⁻¹⁵ − 10⁻¹² seconds) with high peak powers (1015 Watts) in small, focused spots (∼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. Possible applications of these particle sources include isotope production for medical applications, proton cancer therapy, and fusion energy schemes. This thesis focuses on laser-driven proton acceleration and adds to the existing foundation of work in the area by investigating new empirical relationships, conducting new measurements of the accelerating electric field responsible for laser-driven proton acceleration, and developing a new data analysis methodology using machine learning. This work first examines laser-driven proton acceleration in the multi-picosecond regime (>1ps) at laser intensities of 10¹⁷ − 10¹⁹ W/cm². This is motivated by recent results on laser platforms like the National Ignition Facility-Advanced Radiographic Capability laser and the OMEGA-Extended Performance laser, which have demonstrated enhanced accelerated proton energies when compared to established scaling laws. A detailed scaling study was performed on the Titan laser, which provided the basis for a new analytical scaling presented in this thesis. In addition, high-repetition-rate (HRR) lasers that can operate at 1 Hz or faster are now coming online around the world, opening a myriad of opportunities for accelerating the rate of learning on laser-driven particle experiments. To unlock these applications, HRR diagnostics combined with real-time analysis tools must be developed to process experimental measurements and outputs at HRR. Towards this goal, this thesis presents a novel automated data analysis framework based on machine learning and proposes a new methodology based on representation learning to integrate heterogeneous data constrain parameters that are not directly measurable. Taken together, these thrusts enable a new preliminary framework for enhanced analysis of complex HRR experiments and a foundational step towards realizing the goal of tunable laser-driven particle sources.
Ph.D.
Massachusetts Institute of Technology
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Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Investigation of laser-driven particle acceleration for the development of tunable ion sources for applications in high energy density science
Thesis
application/pdf
Doctoral
Doctor of Philosophy
Publication
unknown
unknown
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<Abstract>Since the innovation of chirped pulse amplification by Donna Strickland and Gerard Morou in 1985, laser technology has evolved such that we can create short pulses of light (10⁻¹⁵ − 10⁻¹² seconds) with high peak powers (1015 Watts) in small, focused spots (∼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. Possible applications of these particle sources include isotope production for medical applications, proton cancer therapy, and fusion energy schemes. This thesis focuses on laser-driven proton acceleration and adds to the existing foundation of work in the area by investigating new empirical relationships, conducting new measurements of the accelerating electric field responsible for laser-driven proton acceleration, and developing a new data analysis methodology using machine learning. This work first examines laser-driven proton acceleration in the multi-picosecond regime (>1ps) at laser intensities of 10¹⁷ − 10¹⁹ W/cm². This is motivated by recent results on laser platforms like the National Ignition Facility-Advanced Radiographic Capability laser and the OMEGA-Extended Performance laser, which have demonstrated enhanced accelerated proton energies when compared to established scaling laws. A detailed scaling study was performed on the Titan laser, which provided the basis for a new analytical scaling presented in this thesis. In addition, high-repetition-rate (HRR) lasers that can operate at 1 Hz or faster are now coming online around the world, opening a myriad of opportunities for accelerating the rate of learning on laser-driven particle experiments. To unlock these applications, HRR diagnostics combined with real-time analysis tools must be developed to process experimental measurements and outputs at HRR. Towards this goal, this thesis presents a novel automated data analysis framework based on machine learning and proposes a new methodology based on representation learning to integrate heterogeneous data constrain parameters that are not directly measurable. Taken together, these thrusts enable a new preliminary framework for enhanced analysis of complex HRR experiments and a foundational step towards realizing the goal of tunable laser-driven particle sources.</Abstract>
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