Results for “ion beam figuring” — Page 2

DOE OSTI2023

Heavy-ion physics at the LHC

Heavy-ion collisions have been part of the programme of the LHC since its conception. The main nucleus–nucleus system is 208 82 + Pb + 208 82 + Pb . During the Runs 1 and 2, the LHC provided Pb+Pb collisions with centre-of-mass energy per nucleon pair √ s NN = 2.76 and 5.02 TeV, Xe+Xe with √ s NN = 5.44 TeV, and p+Pb with √ s NN = 5.02 and 8.16 TeV. The LHC performance with heavy-ion beams is described in Ref. 1. The ALICE detector was conceived to be dedicated to the study of heavy-ion collisions. The ATLAS and CMS experiments participated in the programme since the very beginning, and the LHCb experiment participated since the end of Run 1. Figure 1 shows event displays of Pb+Pb collisions from the four experiments.

Audurier, Benjamin↗

DOE OSTI2026

An absolute recoil-carbon polarimeter for polarized light-ions at the BNL Booster

We describe a recoil-carbon polarimeter for the BNL Booster synchrotron capable of measuring the transverse polarization of both the polarized proton beam and the polarized 3 He ion beam throughout the acceleration cycle. The instrument addresses a critical gap in the BNL polarized beam program: no independent polarization measurement currently exists in the injector chain between the 200 MeV Linac polarimeter and the AGS. The proposed new polarimeter will provide continuous, absolute polarization measurements at the Booster stage, supplying additional anchor points for the polarization transmission through the accelerator chain. The polarimeter 9 is based on elastic pC and 3 He C scattering from a thin internal carbon target, with detection of the recoil 12 C nucleus in a fixed six-station silicon detector ring. A single detector geometry provides continuous kinematic coverage from injection to extraction for both beam species without mechanical adjustment. For pC scattering, the available data and the Bonin parametrization provide a well-established basis for estimating the analyzing power and figure of merit over the Booster energy range; for 3 He C the sole experimental anchor is a measurement at 443 MeV, and the Booster polarimeter itself is identified as the instrument to map the analyzing power across the remainder of the acceleration ramp by a ramp-and-return calibration that anchors the absolute 3 He polarization scale to a few percent. Provision for future deuteron polarimetry is incorporated in the chamber design without modification to the existing geometry. With appropriate detector upgrades, the same recoil-carbon technique can be extended to deuteron beams, for which analyzing power data are already available, and, with future analyzing-power measurements, also to 6 Li and 7 Li beams, making the polarimeter a versatile instrument for the full range of light polarized ion species anticipated at BNL.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Polarized internal target experiments based on EIC beams

The Electron-Ion Collider is under construction at BNL. It will have high-energy high-intensity polarized beams of electrons and hadrons. These beams will allow a high accuracy investigation of nucleon structure in the low- to very-low-x DIS regime. At the same time, similar to the realization at HERA, these beams could be used with an internal target for a very productive investigation of medium- to high-x nucleon structure. Due to a novel regime of electron beam operation and its high polarization and intensity, the Figure-of-Merit of an internal target experiment at EIC will be 500+ times higher than was obtained by HERMES.

Wojtsekhowski, Bogdan↗

DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗