A High-Throughput Hot Surface Ion Source for Electromagnetic Radioisotope Separation
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This collaborative research project focuses on leveraging advanced machine learning techniques to analyze and optimize data from high-repetition-rate laser experiments. The main goal is to apply modern computing hardware, customized data acquisition firmware/software, and machine learning approaches to improve data analysis and experimental control. The project also explores how methodology can be developed on smaller-scale experimental setups and then translated to larger facilities within DOE's LaserNetUS network. With extensive data collection and modeling, the research aims to predict and optimize experimental parameters to enhance performance and efficiency.
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The PIP-II linac will enable >1.2 MW beam power for DUNE, requiring unprecedented operational reliability across its warm front-end (RFQ, MEBT) and five distinct SRF sections operating at 162.5/325/650 MHz. We present a comprehensive digital twin framework uniquely combining a fully differentiable fast beam transport code with neural network surrogates trained on high-fidelity PIC simulations, capturing space charge and nonlinear dynamics beyond traditional envelope codes while achieving 10⁴ speedup at <1% accuracy. End-to-end differentiability enables gradient-based optimization across 500+ parameters simultaneously previously impossible with conventional tools while the model incorporates static/dynamic errors and serves as a virtual commissioning platform for diverse hardware integration. The framework facilitates reinforcement learning for pulsed/CW mode transitions, predictive maintenance through anomaly detection, and autonomous tuning algorithm development with real-time execution capability. Validation against physics simulations shows excellent agreement for the front-end, with initial results demonstrating potential for 30% commissioning time reduction and proactive fault mitigation, providing a scalable blueprint for operating next-generation high-intensity accelerators.
Four samples were studied as part of the evaluation of the component parts of the material recovered from the Surveyor 3 spacecraft. Before analyzing the samples, a calibration sample was obtained. The distribution of lunar fines on painted and polished surfaces is presented.
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The original 12 cm hexagonal magneto-electrostatic containment (MESC) discharge chamber described by Moore in 1969 has been optimized for argon and xenon operation. Argon mass utilization efficiencies of 65 to 77 percent were achieved at keeper-plus-main discharge energy consumptions of 244 to 422 eV/ion respectively. Xenon performance of 85 to 96 percent mass utilization were realized at 203 to 350 eV/ion. The paper discusses the optimization process and test results.
THe original 12 cm hexagonal magneto-electrostatic containment discharge chamber has been optimized for argon and xenon operation. Argon mass utilization efficiencies of 65 to 77 percent were achieved at keeper-plus-main discharge energy consumptions of 200 to 458 eV/ion, respectively. Xenon performance of 84 to 96 percent mass utilization was realized at 203 to 350 eV/ion. The optimization process and test results are discussed.