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DOE OSTI2023

Charges in the UV completion of neutral electrodynamics

A theory with a non-compact form-symmetry is described by two closed form fields of degrees k and d – k. Effective theory examples are non-linear electrodynamics, a photon field coupled to a neutron field, and a low energy Goldstone boson. We show these models cannot be completed in the UV without breaking the non-compact form-symmetry down to a compact one. This amounts to the existence of electric or magnetic charges. A theory with an unbroken non-compact k-form symmetry is massless and free.

't Hooft and Polyakov loops↗

DOE OSTI2023

Alignment of the CLAS12 central hybrid tracker with a Kalman Filter

Several factors can contribute to the difficulty of aligning the sensors of tracking detectors, including a large number of modules, multiple types of detector technologies, and non-linear strip patterns on the sensors. All three of these factors apply to the CLAS12 CVT, which is a hybrid detector consisting of planar silicon sensors with non-parallel strips, and cylindrical micromegas sensors with longitudinal and arc-shaped strips located within a 5 T superconducting solenoid. To align this detector, we used the Kalman Alignment Algorithm, which accounts for correlations between the alignment parameters without requiring the time-consuming inversion of large matrices. This is the first time that this algorithm has been adapted for use with hybrid technologies, non-parallel strips, and curved sensors. We present the results for the first alignment of the CLAS12 CVT using straight tracks from cosmic rays and from a target with the magnetic field turned off. After running this procedure, we achieved alignment at the level of 10 µm, and the widths of the residual spectra were greatly reduced. These results attest to the flexibility of this algorithm and its applicability to future use in the CLAS12 CVT and other hybrid or curved trackers, such as those proposed for the future Electron-Ion Collider.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Horn Location Sensors for LBNF

The Horn Location Sensor (HLS) system—named for the magnetic focusing horns it monitors, along with other critical beamline elements – is a high-precision alignment system developed for the Long-Baseline Neutrino Facility (LBNF) to support the Deep Underground Neutrino Experiment (DUNE). With minimal maintenance, the HLS can operate reliably in environments with high radiation, and it ensures that key components such as the protective baffle, focusing horns, and beam position monitors are aligned correctly – each essential for maintaining one of the world’s most intense muon-neutrino beams. A high-precision hydrostatic level sensor, a linear variable differential transformer, and INVAR rods are all used in the system to monitor vertical motion and tilt with sub-millimeter accuracy. A novel Sweep Tracker interferometer enhances calibration fidelity by correcting for non-linearities in laser wavelength and scan rate in real time. A critical part of DUNE’s precision alignment and flux prediction requirements, the HLS system initially supports beam power of up to 1.2 MW and can be upgraded to 2.4 MW.

Frequency scanning interferometry↗

DOE OSTI2022

Parametric dependencies of resonant layer responses across linear, two-fluid, drift-MHD regimes

Non-axisymmetric magnetic fields arising in a tokamak either by external or internal perturbations can induce complex non-ideal MHD responses in their resonant surfaces while remaining ideally evolved elsewhere. This layer response can be characterized in a linear regime by a single parameter called the inner-layer Δ, which enables outer-layer matching and the prediction of torque balance to non-linear island regimes. In this work, we follow strictly one of the most comprehensive analytic treatments including two-fluid and drift MHD effects and keep the fidelity of the formulation by incorporating the numerical method based on the Riccati transformation when quantifying the inner-layer Δ. The proposed scheme reproduces not only the predicted responses in essentially all asymptotic regimes but also with continuous transitions as well as improved accuracies. In particular, the Δ variations across the inertial regimes with viscous or semi-collisional effects have been further resolved, in comparison with additional analytic solutions. The results imply greater shielding of the electromagnetic torque at the layer than what would be expected by earlier work when the viscous or semi-collisional effects can compete against the inertial effects, and also due to the intermediate regulation by kinetic Alfvén wave resonances as rotation slows down. These are important features that can alter the non-axisymmetric plasma responses including the field penetration by external fields or island seeding process in rotating tokamak plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Review: Turbulence dynamics during the pedestal evolution between edge localized modes in magnetic fusion devices

Fusion performance has been shown to be related to the H-mode pedestal structure. The pedestal is associated with steep gradients that are the source of free energy for microinstabilities. A variety of instabilities have been shown to co-exist in the pedestal. This paper reviews the experimentally observed signatures of instabilities during the pedestal parameters' evolutions, with a focus on turbulence observations, made in between edge localized modes (ELMs), in multiple tokamaks. ELMs are cyclic events associated with bursty relaxations of the pedestal. The multiple machine results point to very similar pedestal localized modes suggesting the existence of a unifying mechanism governing the modes' onset and dynamics. Modeling efforts to identify the generation mechanism of these instabilities are introduced. Several remaining challenges include the study of the instability saturation mechanisms and impact on the pedestal structure formation. Finally, potential future research avenues will require multiscale-non-linear-gyrokinetic analyses to study the non-linear interaction between these instabilities, and which pedestal conditions facilitate such non-linear interactions, as well as the relationship with the formation of the pedestal through transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

MARS-Q modeling of kink-peeling instabilities in DIII-D QH-mode plasma

In quiescent H-mode (QH-mode) regime, edge harmonic oscillations (EHOs) are believed to provide necessary radial transport to prevent occurrence of large edge localized modes. A systematic modeling study is performed here on the low- n EHOs in a DIII-D QH-mode plasma (Chen et al 2016 Nucl. Fusion 56 076011), by utilizing the MARS-Q code (Liu et al 2013 Phys. Plasmas 20 042503). Both the n = 1 and n = 2 instabilities are found to be strongly localized near the plasma edge, exhibiting the edge-peeling characteristics. The DIII-D resistive wall is found to have minor effects on these instabilities. The plasma resistivity is found to strongly modify the mode growth rate. Assuming the Spitzer model for the plasma resistivity, the computed mode growth rate scales as S sup>-1/3 with S being the Lundquist number. Toroidal flow of the plasma slightly stabilizes these edge localized kink-peeling modes. Drift kinetic effects all have a destabilization effect on these modes. Non-perturbative magneto-hydrodynamic-kinetic hybrid computations find that the drift kinetic effects associated with thermal particle species push the peak location of the eigenmode radially inward but still in the pedestal region. The modeled plasma temperature and density fluctuations in the plasma edge region, as well as the poloidal magnetic field perturbations along both the low and high field sides of the plasma surface, are in good agreement with experimental measurements. Finally, the quasi-linear initial value simulations find a strong non-linear interplay between the kink-peeling instability and the toroidal flow near the plasma edge. The combined effect of the damping of the flow amplitude and change of the edge flow shear is found to be the stabilizing factor for the kink-peeling mode, leading to the mode saturation and thus EHOs.

Physics↗

DOE OSTI2022

Origin of multifractality in solar wind turbulence: the role of current sheets

ABSTRACT In this work, a multifractal framework is proposed to investigate the effects of current sheets in solar wind turbulence. By using multifractal detrended fluctuation analysis coupled with surrogate methods and volatility, two solar wind magnetic field time series are investigated, one with current sheets and one without current sheets. Despite the lack of extreme-events intermittent bursts in the current sheet-free series, both series are shown to be strongly multifractal, although the current sheet-free series displays an almost linear behaviour for the scaling exponent of structure functions. Long-range correlations are shown to be the main source of multifractality for the series without current sheets, while a combination of heavy-tail distribution and non-linear correlations is responsible for multifractality in the series with current sheets. The multifractality in both time series is formally shown to be associated with an energy-cascade process using the p-model.

Gomes, Leonardo F.↗

DOE OSTI2021

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Parametric dependencies of resonant layer responses across linear, two-fluid, drift-MHD regimes

Non-axisymmetric magnetic fields arising in a tokamak either by external or internal perturbations can induce complex non-ideal MHD responses in their resonant surfaces while remaining ideally evolved elsewhere. This layer response can be characterized in a linear regime by a single parameter called the inner-layer Delta, which enables outer-layer matching and the prediction of torque balance to non-linear island regimes. Here, we follow strictly one of the most comprehensive analytic treatments including two-fluid and drift MHD effects and keep the fidelity of the formulation by incorporating the numerical method based on the Riccati transformation when quantifying the inner-layer Delta. The proposed scheme reproduces not only the predicted responses in essentially all asymptotic regimes but also with continuous transitions as well as improved accuracies. In particular, the Delta variations across the inertial regimes with viscous or semi-collisional effects have been further resolved, in comparison with additional analytic solutions. The results imply greater shielding of the electromagnetic torque at the layer than what would be expected by earlier work when the viscous or semi-collisional effects can compete against the inertial effects, and also due to the intermediate regulation by kinetic Alfven wave resonances as rotation slows down. These are important features that can alter the nonaxisymmetric plasma responses including the field penetration by external fields or island seeding process in rotating tokamak plasmas.

MHD instability↗

DOE OSTI2026

Analysis of the elliptic integrable non-linear system in IOTA using tracking of a single electron

Integrable nonlinear lattices that can be realized in practical accelerators are of great interest, as they offer the potential to support high-intensity beams via Landau damping of collective instabilities. One such system, based on an elliptic potential, has been extensively studied at the IOTA storage ring at Fermilab. The analysis of strongly nonlinear dynamics with multi-particle bunches is challenging due to the rapid decoherence of kicked beams. IOTA has the capability to track single electrons using linear multi-anode photomultiplier tubes for simultaneously measuring transverse coordinates and arrival times of synchrotron-radiation pulses. This technology enables the full reconstruction of turn-by-turn positions and momenta in all three planes for a single particle. Using this apparatus, we measured the dependence of small-amplitude tunes on the strength of the nonlinear magnet, as well as tunes dependence on oscillations amplitudes.

Romanov, A. [Fermilab]↗

DOE OSTI2025

Beam Non-Uniformity Characterization at the SpinQuest and DarkQuest Experiments

The SpinQuest experiment, including upgrades to SpinQuest designed to increase sensitivity to dark sector searches (commonly known as DarkQuest), utilizes the high-intensity 120 GeV proton beam delivered by the Fermilab Accelerator Complex to probe the inner structure of nucleons and search for new physics beyond the Standard Model. The SpinQuest beam is extracted from the Main Injector synchrotron at Fermilab in what is known as a slow spill . The slow spill involves a complex non-linear half-integer extraction method, which results in non-uniform beam behavior. SpinQuest observes spikes of very high intensity beam which can saturate detectors and reduce trigger efficiency, significantly impacting the experiment's sensitivity. In this project we address this challenge by developing an analysis framework to characterize the beam delivered to SpinQuest. By discovering trends within each spill and by comparing thousands of spills, we can better inform the Accelerator Division and improve the slow spill extraction. We have also begun a collaboration with the Accelerator Division in order to simulate the slow spill and improve the magnet ramp process controls which will improve the uniformity of the beam. These improvements will directly enhance the physics reach of SpinQuest/DarkQuest, increasing their sensitivity to key measurements such as the Sivers function and searches for new physics.

Dolen, James William [Purdue U., Calumet] (ORCID:0↗

DOE OSTI2025

Analysis of the Elliptic Integrable Non-Linear System in IOTA Using Tracking of a Single Electron

Romanov, Aleksandr Leonidovich [Fermilab] (ORCID:0↗

DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗