{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/147365"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/147365","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Investigation of laser-driven particle acceleration for the development of tunable ion sources for applications in high energy density science","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_html":"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_has_math":false,"creators":["Simpson, Raspberry"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Investigation of laser-driven particle acceleration for the development of tunable ion sources for applications in high energy density science"]}]}],"canonical_facts":{"dc:contributor.advisor":["Winslow, Lindley","Ma, Tammy"],"dc:contributor.department":["Massachusetts Institute of Technology. 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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."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/147365"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Investigation of laser-driven particle acceleration for the development of tunable ion sources for applications in high energy density science"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:22:31Z"}