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DOE OSTI2025

First characterisation of the MAGO cavity, a superconducting RF detector for kHz–MHz gravitational waves

Heterodyne detection using microwave cavities is a promising method for detecting high-frequency gravitational waves (GWs) or ultralight axion dark matter. In this work, we report on studies conducted on a spherical 2-cell cavity developed by the MAGO collaboration for high-frequency GWs detection. Although fabricated around 20 years ago, the cavity had not been used since. Due to deviations from the nominal geometry, we conducted a mechanical survey and performed room-temperature plastic tuning. Measurements and simulations of the mechanical resonances and electromagnetic properties were carried out, as these are critical for estimating the cavity’s GW coupling potential. Based on these results, we plan further studies in a cryogenic environment. The cavity characterisation does not only provide valuable experience for a planned physics run but also informs the future development of improved cavity designs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Neutrino beam bunch structure reconstruction with precision timing in the ICARUS liquid argon time projection chamber

The ICARUS detector has been operating smoothly since 2021 as the far detector in the Short Baseline Neutrino (SBN) program at Fermilab, collecting neutrino interactions from both the Booster Neutrino Beam (BNB) and off-axis from the Neutrinos at the Main Injector (NuMI) beam. Analysis of neutrino interactions in ICARUS requires mitigation of substantial cosmogenic backgrounds. This is achieved by using an external Cosmic Ray Tagger (CRT) and a Photomultiplier Tube (PMT) system embedded in the liquid argon. The intrinsic neutrino beam bunch structure, inherited time structure from the Radio Frequency (RF) system used to accelerate the protons, can be resolved at the ICARUS detector using precise timing information. Located at shallow depth, ICARUS is exposed to a high flux of cosmic rays that can be mistaken for neutrino interactions. To mitigate this background, the CRT and a 3-meter-thick concrete overburden were installed. To better model backgrounds, ICARUS makes use of an overlay technique where simulated neutrino events are superimposed on detector beam-off data. PMTs installed within a Time Projection Chamber detect argon scintillation light emitted by high energy charged particles passing through the chamber and provide the event timing of neutrino interactions. The nanosecond-level beam bunch structure is reconstructed with the PMT system and can be used to further understand backgrounds and enhance neutrino physics capabilities. In this thesis, I will discuss background mitigation techniques using precision timing and present a novel technique to select neutrino events from our unbiased data stream using the beam bunch structure.

Heggestuen, Anna [Colorado State U.] (ORCID:000000↗

DOE OSTI2024

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Data-Driven Analysis of Multipactor Dynamics via Dynamic Mode Decomposition

Multipactor effect is a performance-limiting kinetic plasma effect that can occur in high-power microwave and radio frequency (RF) devices. Multipactor effect is of special concern in vacuum or near-vacuum conditions such as those in particle accelerators and spaceborne devices. In this work, we present a data-driven reduced-order model (ROM) based on dynamic mode decomposition (DMD) for modeling of multipactor effects. We study multipactor effects and the resulting nonlinear harmonic generation by processing high-fidelity data generated from electromagnetic particle-in-cell (EMPIC) simulations using the DMD algorithm. We also investigate time-delay embedding extensions of DMD with improved generalizability and accuracy for modeling the electron plasma current density behavior. Here, the results show that DMD provides valuable insights into multipactor phenomena by extracting relevant modal spatiotemporal patterns and frequencies. In addition, DMD offers the potential to time extrapolate EMPIC simulations at a minimal cost, thereby reducing overall simulation time.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

LAMP DTL Scoping Studies (Technical Report)

The present studies are based on the preliminary design efforts, and on the Scoping Studies of the “Strawman” design of LAMP front-end upgrade, referred in the text below to as “Feb.2024 Iteration”, presented in. The main accomplishment of present studies was substantial increase of the fidelity of the beam dynamics simulations in the proposed drift tube linac (DTL). The main accent was on development of the methodology for calculation of the longitudinal (synchrotron) and transverse (betatron) oscillations frequencies (phase advance per focusing period) values and providing the accelerating structure focusing lattice that has safe parameters of such oscillations to avoid unwanted emittance growth and possible beam halo formation. The resulting values of the oscillations phase advances are presented in Table 1, and in Figure 5 in the main body of the report. Special efforts were made to achieve the RF power consumption within limits of the existing RF power system and make sure that DTL fits in the existing tunnel. The LAMP scope does not suggest any additional building and/or tunnel construction. Table 1 summarizes some of these results, as well as Table 3 in the main body of the report.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Overview of recent experimental results on the EAST Tokamak

Since the last IAEA-FEC in 2021, significant progress on the development of long pulse steady state scenario and its related key physics and technologies have been achieved, including the reproducible 403 s long-pulse steady-state H-mode plasma with pure radio frequency (RF) power heating. A thousand-second time scale (~1056 s) fully non-inductive plasma with high injected energy up to 1.73 GJ has also been achieved. The EAST operational regime of high β P has been significantly extended (H 98y2 > 1.3, β P ~ 4.0, β N ~ 2.4 and n e /n GW ~ 1.0) using RF and neutral beam injection (NBI). The full edge localized mode suppression using the n = 4 resonant magnetic perturbations has been achieved in ITER-like standard type-I ELMy H-mode plasmas with q 95 ≈ 3.1 on EAST, extrapolating favorably to the ITER baseline scenario. The sustained large ELM control and stable partial detachment have been achieved with Ne seeding. The underlying physics of plasma-beta effect for error field penetration, where toroidal effect dominates, is disclosed by comparing the results in cylindrical theory and MARS-Q simulation in EAST. Breakdown and plasma initiation at low toroidal electric fields (<0.3 V m -1 ) with EC pre-ionization is developed. A beneficial role on the lower hybrid wave injection to control the tungsten concentration in the NBI discharge is observed for the first time in EAST suggesting a potential way toward steady-state H-mode NBI operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

On the Life Expectancy Ot High-power CW Magnetrons for Superconducting Accelrators

Modern CW or pulse Superconducting RF (SRF) accelerators require efficient RF sources controllable in phase and power with a reduced cost. Therefore, utilization of the high-power CW magnetrons as RF sources in SRF accelerator projects was proposed in a number of works, e.g., [1, 2]. But typically, the CW magnetrons are designed as RF sources for industrial heating, and the lifetime of the tubes is not the first priority as it is required for high-energy accelerators. The high-power industrial CW magnetrons use the cathodes made of pure tungsten. The emission properties of the tungsten cathodes are not deteriorated much by electron and ion bombardments, but the latter causes sputtering of the cathode in the magnetron crossed fields. The sputtered cathode material covers the magnetron interior. This leads to sparks and discharges that limit the life of the magnetrons. We considered an analysis of magnetron failure modes vs. output power [3]. We developed a model of ionization of the residual gas in the magnetrons interaction space and simulated the spattering of the cathode in 100 kW CW magnetrons to estimate the life expectancy. Basing on results we proposed ways to increase the CW magnetrons longevity for SRF accelerators.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

On the Life Expectance of High-power CW Magnetrons for SRF Accelerators

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Design and Engineering of LUPIN: A Test-Bed Radio-Frequency Ion Source for Enhanced Neutral Beam Injection on DIII-D

The Large, Uniform Plasma for Ionizing Neutrals (LUPIN) is a radio-frequency (RF) inductively coupled plasma (ICP) chamber for demonstrating plasma performance of an RF ICP positive ion source upgrade for the DIII-D neutral beam injection (NBI) system. LUPIN will be used to investigate ion source physics, including neutral gas dynamics, plasma density uniformity, interactions with Faraday shields, and power coupling to novel RF antenna designs. LUPIN has an RF generator capable of delivering 20 kW of power at 2 MHz, which is coupled into a cylindrical quartz vessel measuring 20 cm in length and 10 cm in radius. This configuration matches the power density requirements for a full-scale ion source. Target hydrogen and deuterium plasma densities exceeding 10 18 m -3 would relate to extracted ion current densities of 2100 A/m 2 for 10s. Vacuum conductance and gas flow calculations predict a maximum achievable neutral gas flow rate of 1675 Pa ⋅ L/s at 5 Pa of He, which mimics the gas flow of the DIII-D NBI system. Designs have been developed for an internal Faraday shield to mitigate heat flux and ion sputtering on the dielectric vessel. Thermomechanical finite element simulations demonstrated the Faraday shield design to be capable of withstanding anticipated heat loads from worst-case operation scenarios. Finally, results of upcoming experimental investigations on LUPIN will guide the design of a full-scale prototype for DIII-D integration.

Faraday shield↗

DOE OSTI2024

Design and Engineering of LUPIN: A Test-Bed Radiofrequency Ion Source for Enhanced Neutral Beam Injection on DIII-D

The Large, Uniform Plasma for Ionizing Neutrals (LUPIN) is an RF inductively coupled plasma (ICP) chamber for demonstrating plasma performance of an RF ICP positive ion source upgrade for the DIII-D Neutral Beam Injection (NBI) system. LUPIN will be used to investigate ion source physics, including neutral gas dynamics, plasma density uniformity, interactions with Faraday shields, and power coupling to novel RF antenna designs. LUPIN has an RF generator capable of delivering 20 kW of power at 2 MHz, which is coupled into a cylindrical quartz vessel measuring 20 cm in length and 10 cm in radius. This configuration matches the power density requirements for a full-scale ion source. Target hydrogen and deuterium plasma densities exceeding 10 18 m -3 would relate to extracted ion current densities of 2100 A/m 2 for 10 s. Vacuum conductance and gas flow calculations predict a maximum achievable neutral gas flow rate of 1675 Pa·L/s at 5 Pa of He, which mimics the gas flow of the DIII-D NBI system. Designs have been developed for an internal Faraday shield to mitigate heat flux and ion sputtering on the dielectric vessel. Thermomechanical finite element simulations demonstrated the Faraday shield design to be capable of withstanding anticipated heat loads from worst-case operation scenarios. Results of upcoming experimental investigations on LUPIN will guide the design of a full-scale prototype for DIII-D integration.

Faraday shield↗

DOE OSTI2025

PID-Regulated Heating System for PIP-II Reference Line

The Proton Improvement Project-2 centers on building a new superconducting linear particle accelerator (Linac) at Fermilab. At the heart of the accelerator is the reference line, a critical system that defines the ideal path for the particle beam as it passes through magnets, RF cavities, and other beamline elements. Temperature stability is crucial for the reliable operation of RF components, such as mixers and filters. Fluctuations affect key performance parameters like conversion loss, isolation, and linearity. To mitigate any drift caused by ambient temperature changes, a heating plate assembly is utilized to maintain key components at a controlled temperature of 40°C. The system utilizes an aluminum 36”x36”x0.5” heat plate powered by a MOSFET-based control circuit, delivering approximately 460 W of thermal energy through a resistor array. Real-time temperature feedback is provided by a PT100 Resistance Temperature Detector (RTD), which interfaces with a Proportional–Integral–Derivative (PID) control algorithm to maintain closed-loop temperature regulation. The control signal actively modulates the gate voltage of an N channel MOSFET, dynamically adjusting power delivery in response to deviations from the temperature setpoint. Simulations and LTspice models validate the functionality and responsiveness of the circuit under varying conditions. The prototype has successfully demonstrated stable thermal control, paving the way for integration into the PIP-II infrastructure. The final design will feature an expanded resistor array, as well as communication with a PLC for continuous data acquisition and diagnostics. This work directly supports Fermilab’s broader mission by contributing to the stability and reliability of core accelerator systems, enhancing the precision of particle beam delivery for future physics experiments.

Mosher, Alexander [Fermilab]↗

DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Cyclotron resonance accelerators for industrial applications

Here, this paper describes novel configurations for cyclotron resonance acceleration of electrons and ions that have several attractive features including: a compact robust room-temperature single-cell RF cavity as the accelerator structure; and continuous high current accelerated un-bunched beam output with self-scanning, obviating need for a separate beam scanner. An electron accelerator version, the electron Cyclotron Resonance Accelerator (eCRA), is under development to be an efficient source for high power electron and x-ray beams for medical, research, sterilization, and National Security applications, so as to replace radioactive materials. An ion accelerator version, the ion Cyclotron Auto-Resonance Accelerator (iCARA) is described here, suggesting its potential to produce, as an example, a high-current multi-MeV beam of deuterons which could be highly competitive with that produced either with linacs or cyclotrons. Such a deuteron beam could produce a high flux of fast neutrons via deuteron stripping, for applications including the transmutation of used nuclear fuel, material studies relevant for a fusion reactor inner wall, tritium breeding and medical isotope production. For the high-current, high efficiency simulated performance for eCRA and iCARA as described in this paper, the particle beams produced may not exhibit the low emittance values that are important for most discovery research. Rather, the beams could be useful for industrial applications where higher emittance and some energy spread can be tolerated, in favor of high beam power.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Determining the extent of potential fugitive fluid migration from geologic carbon storage in hydrocarbon-bearing reservoirs: Insights from one-dimensional numerical modeling

Numerical modeling of Geologic Carbon Sequestration in permeable reservoirs initially containing hydrocarbons is conducted using the multi-phase, multi-component thermohydrologic simulator TOGA (TOUGH Oil, Gas, Aqueous; TOUGH stands for Transport Of Unsaturated Groundwater and Heat), to determine how phase and composition of the original fluids influence the extent of the zone where upward fugitive fluid migration could potentially occur, denoted R f . The area within R f comprises regions of substantially elevated pressure and free-phase CO 2 saturation, where a breach in reservoir sealing capacity would lead to upward fugitive fluid migration. The model examines the conditions within the storage reservoir that could lead to fugitive flow, but does not model the fugitive flow itself. A one-dimensional radial model of the storage reservoir is used, and three initial phase conditions are considered: single-phase aqueous, two-phase gas-aqueous, and three-phase oil-gas-aqueous. Components that may be present are H 2 O, CO 2 , CH 4 , C 4 H 10 , and C 10 H 22 . The most important factors controlling Rf are (1) the initial gas-phase saturation within the reservoir, and (2) the lateral extent of multi-phase initial conditions, particularly CO 2 . The composition of liquid and gas phases has a secondary effect. The impact of reservoir depth, thickness, injection rate, and hydrologic properties are also briefly examined, with thickness (or equivalently injection rate) having the biggest effect. These results can help to understand important trends in potential response of CO 2 -EOR fields being considered for dedicated CO 2 storage.

CO₂ plume migration↗

DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗