Tailored Particle Sources Driven by Precision-Shaped Short-Pulse Laser Pulses

Project Overview

The goal of this work is to develop and explore short-pulse laser "pulse shaping" for precision tailoring of laser-driven particle beams, which has, to date, been limited in capability. In this LDRD, we explored several techniques for delivering time-dependent high-intensity (I>1018 W/cm2) laser pulses to solid targets, including multipulse combinations and direct single-beam intensity profile shaping through advanced laser spectral-phase control. We experimentally demonstrated significant enhancements in particle energy and flux and control over x-ray distribution temperature and flux through this pulse shaping, including the new record-high maximum proton energies driven by the NIF ARC laser. Additionally, this work demonstrated the first utilization of advanced machine-learning techniques combined with ensemble particle-in-cell simulations to create continuous maps of the massive parameter space enabled by pulse shaping that could be informed by experimental outputs through a technique known as transfer learning. This work shows that it is possible to readily control the properties of particle and x-ray sources through manipulation of the delivered laser-intensity profile and can be used to inform future laser-facility designs to leverage this capability along with modifications to existing laser facilities, including NIF's ARC.

Mission Impact

This work greatly expands the available parameter space for the optimization of short-pulse driven sources. The utilization of pulse shaping has been shown to be an effective means of manipulating the energy and flux of particle and X-ray sources compared to nominal pulses. Additionally, this work has pioneered the first demonstration of ensemble PIC simulations coupled with machine learning to scope these massive multidimensional parameter spaces rapidly and at significantly reduced computational cost as compared to "brute force" scans in notoriously difficult and expensive short-pulse high-intensity laser plasma interactions. The PIC modeling + ML effort is now supporting multiple LDRD and DOE fusion-energy sciences projects, including the development of short-pulse laser-driven neutron sources for nondestructive evaluation to support LLNL and NNSA missions. This work additionally supported the development of high repetition rate (HRR) (>shot/minute) high-energy-density-physics experimentation through the development and demonstration of HRR diagnostics and large-volume data generation in experiments and simulations for physics validation. The utilization of these techniques is envisioned to be a key component for rapidly responding to future DOE and NNSA model-validation needs.

Publications, Presentations, and Patents

Djordjević, B. Z. et al. 2021. "Modeling Laser-Driven Ion Acceleration with Deep Learning." Physics of Plasmas 28 (2021); doi: 10.1063/5.0045449.

Kim, J. et al. 2022. "Efficient Ion Acceleration by Multistaged Intense Short Laser Pulses." Physical Review Research 4.3 (2022); doi: 10.1103/PhysRevResearch.4.L032003.

Mariscal, D. et al. 2021. "Design of Flexible Proton Beam Imaging Energy Spectrometers (PROBIES)." Plasma Physics and Controlled Fusion 63 (2021); doi: 10.1088/1361-6587/ac234a.

Mariscal, D. et al. 2022. "A Flexible Proton Beam Imaging Energy Spectrometer (PROBIES) for High Repetition Rate or Single-Shot HED experiments." Review of Scientific Instruments (Accepted 2022). LLNL-JRNL-836069.

B Z Djordjević, A J Kemp1, J Kim, J Ludwig1, R A Simpson, S C Wilks, T Ma, and D A Mariscal. 2021."Characterizing the Acceleration Time of Laser-driven Ion Acceleration with Data-informed Neural Networks." Plasma Physics and Controlled Fusion, Volume 63, Number 9.

Simpson, R. A. et al. 2021. "Development of a Deep-Learning Based Automated Data Analysis for Step-Filter X-ray Spectrometers in Support of High-Repetition Rate Short-Pulse Laser-Driven Acceleration Experiments." Review of Scientific Instruments 92 (2021); doi: 10.1063/5.0043835.

Simpson, R. A. et al. 2021. "Demonstration of TNSA proton radiography on the National Ignition Facility Advanced Radiographic Capability (NIF-ARC) Laser." Plasma Physics and Controlled Fusion 63 (2021); doi: 10.1088/1361-6587/ac2349.

Simpson, R. A. et al. 2021. "Scaling of Laser-Driven Electron and Proton Acceleration as a Function of Laser Pulse Duration, Energy and Intensity in the Multi-Picosecond Regime." Physics of Plasmas 28 (2021); doi: 10.1063/5.0023612.

Ma, T. et al. 2021. "Accelerating the Rate of Discovery: Toward High-Repetition-Rate HED Science." Plasma Physics and Controlled Fusion 68 (2021); doi: 10.1088/1361-6587/ac1f67.

Scott, G. 2021. "Demonstration of Plasma Mirror Capability for the OMEGA Extended Performance Laser System." Review of Scientific Instruments 93 (2021); doi: 10.1063/5.0067467.

Mariscal, D. et al. 2022. "Enhanced Analysis of Experimental X-Ray Spectra through Deep Learning." Physics of Plasmas 29 (2022); doi: 10.1063/5.0097777.

Mariscal, D. 2022. "Novel Temporal Pulse Shaping of Ultra-Intense Short Pulses for Precision Tailoring of Secondary Sources." Presentation, HILAS 2022: High-Intensity Lasers and High-Field Phenomena, Budapest, Hungary. March 2022. LLNL-PRES-833279.

Mariscal, D. 2021. "Demonstrations of ARC Proton Radiography and Paths Forward." Presentation, PCTS: Charged particle Radiography in High-Energy-Density Laboratory Plasmas. Virtual. January 2021. LLNL-PRES-818750.

Mariscal, D. 2022. "Neural Networks for Rapid Analysis of High Repetition Rate Diagnostics." Presentation, LPA Online Workshop on Control Systems and Machine Learning, Virtual. January 2022. LLNL-PRES-832210.

Mariscal, D. 2022. "A Flexible Proton Beam Imaging Energy Spectrometer (PROBIES) for High Repetition Rate or Single-Shot HED experiments." Presentation, High Temperature Plasma Diagnostics, Rochester, NY. May 2022. LLNL-PRES-835408.

Mariscal, D. "A New Phase Space of Short-pulse Laser-Particle Acceleration: Multi-ps and Pulse shaping." Presentation. Annual High Energy Density Science Association Symposium, Spokane, WA. Oct 2022. LLNL-PRES-794522.

Djordjević, B. "Exploration of the Parameter Space of Short-Pulse, Laser-Driven Ion Acceleration via Neural Networks." Presentation, High Power Lasers for Fusion Research VI, 11666. April 2021. LLNL-PRES-819881.

Djordjević, B. 2021. "Exploring Higher-Order Effects in Laser-Driven Ion Acceleration via Deep Learning." Presentation. 63rd Meeting of the American Physical Society, Department of Plasma Physics. Pittsburgh, PA. November 2021. LLNL-PRES-828490.

Djordjević, B. 20201. "Mapping the Parameter Space of Laser-Driven Ion Acceleration via Neural Networks." Presentation, 62nd Meeting of the American Physical Society, Department of Plasma Physics, Virtual. November 2020. LLNL-PRES-816299.

Mariscal, D. 2020. "Effect of Shaped High-Intensity Short-Pulses on Particle Acceleration." Presentation, 62nd Meeting of the American Physical Society, Department of Plasma Physics, Virtual November 2020. LLNL-PRES-816066.

Djordjevic, B. 2021. "Characterizing Particle-in-Cell Simulations of Short-Pulse, Laser-Driven Ion Acceleration with Multi-Layer Neural Networks." Presentation, SPIE Photonics West, San Francisco, CA. February 2021. LLNL-PRES-819881.

Mariscal, D. 2021. "Neural Network Surrogates for Atomic Physics Simulations and X-Ray Spectral Evaluation." Presentation, 63rd Meeting of the American Physical Society, Department of Plasma Physics, Pittsburgh, PA. November 2021. LLNL-PRES-832703.

Mariscal, D. 2021. "Neural Networks for Rapid Analysis of High Repetition Rate Diagnostics." Presentation, 63rd Meeting of the American Physical Society, Department of Plasma Physics, Pittsburgh, PA. November 2021. LLNL-PRES-832721

Kim, J. 2021. "Efficient Ion Acceleration by Continuous Fields in Target Transparency Regime." Presentation, 63rd Meeting of the American Physical Society, Department of Plasma Physics, Pittsburgh, PA. November 2021. LLNL-PRES-832723.

Mariscal, D. 2020. "Enhancing Laser-driven MeV Electron and Proton Spectra with Pseudo-Shaped Short Pulses." Poster, 62nd Meeting of the American Physical Society, Department of Plasma Physics, Virtual November 2020. LLNL-POST-816929.

Djordjevic, B. 2022. "Transfer Learning for Multi-Fidelity Modeling of Laser-Driven Particle Acceleration." Poster, 50th Anamolous Absorption Conference, Rochester, NY. June 2022. LLNL-POST-835984.

Djordjevic, B. 2022. "Bridging Model Fidelities of Laser-Driven Particle Acceleration via Transfer Learning." Poster, LaserNetUS Users Meeting 2022, Fort Collins, CO. August 2022. LLNL-POST-838675.