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DOE OSTI2023

First test of the consistency relation for the large-scale structure using the anisotropic three-point correlation function of BOSS DR12 galaxies

ABSTRACT We present, for the first time, an observational test of the consistency relation for the large-scale structure (LSS) of the Universe through a joint analysis of the anisotropic two- and three-point correlation functions (2PCF and 3PCF) of galaxies. We parameterize the breakdown of the LSS consistency relation in the squeezed limit by Es, which represents the ratio of the coefficients of the shift terms in the second-order density and velocity fluctuations. Es ≠ 1 is a sufficient condition under which the LSS consistency relation is violated. A novel aspect of this work is that we constrain Es by obtaining information about the non-linear velocity field from the quadrupole component of the 3PCF without taking the squeezed limit. Using the galaxy catalogues in the Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12, we obtain $E_{\rm s} = -0.92_{-3.26}^{+3.13}$, indicating that there is no violation of the LSS consistency relation in our analysis within the statistical errors. Our parameterization is general enough that our constraint can be applied to a wide range of theories, such as multicomponent fluids, modified gravity theories, and their associated galaxy bias effects. Our analysis opens a new observational window to test the fundamental physics using the anisotropic higher-order correlation functions of galaxy clustering.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

The Dependence of Electron-only Magnetic Reconnection on Plasma Beta and Magnetic Shear: Theory and Observations

We investigate the onset conditions for electron-only magnetic reconnection through an analysis of 86 reconnection events observed by the Magnetospheric Multiscale mission in magnetosheath and magnetospheric current sheets. Our study presents the first observational evidence that the occurrence of electron-only reconnection depends on both the magnetic-field shear angle (θ) across the current sheet and the cross-sheet electron beta jump, quantified either by the inflow electron beta difference (Δβ e ) or by a modified electron beta jump (Δβ$^*_e$) defined by the cross-sheet electron pressure difference normalized by the average of the inflow magnetic pressure in the two inflow regions. We extend the diamagnetic-drift suppression mechanism from standard reconnection to electron-only reconnection by deriving relations applicable to electron-scale current sheets. Using scaling estimates, the electron-only reconnection suppression relation is derived as Δβ$^*_e$ ≳ 2(L/d e ) tan (θ/2), while a natural extension of the ion-coupled reconnection suppression criterion is Δβ e ≳ 2(L/d e ) tan (θ/2). At low Δβ e and Δβ$^*_e$ values, reconnection is detected across a large range of magnetic shear (from low to high), whereas high Δβ e and Δβ$^*_e$ conditions restrict reconnection to large shear configurations: shear angles as small as θ ≈ 5° occur when Δβ e (or Δβ$^*_e$) ≲0.1. However, only events exceeding θ ≳ 70° are found when Δβ e ≳ 1.0; this apparent restriction weakens when employing Δβ$^*_e$, as the corresponding data points shift toward smaller values of the modified electron beta jump. These findings quantitatively support theoretical predictions of reconnection suppression in high-β e plasmas at low shears, attributed to X-line drift induced by pressure gradients across electron-scale current sheets.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

High-n Rydberg transition spectroscopy for heavy impurity transport studies in W7-X (invited)

Here, we present a novel spectroscopy approach to investigate impurity transport by analyzing line-radiation following high-n Rydberg transitions. While high-n Rydberg states of impurity ions are unlikely to be populated via impact excitation, they can be accessed by charge exchange (CX) reactions along the neutral beams in high-temperature plasmas. Hence, localized radiation of highly ionized impurities, free of passive contributions, can be observed at multiple wavelengths in the visible range. For the analysis and modeling of the observed Rydberg transitions, a technique for calculating effective emission coefficients is presented that can well reproduce the energy dependence seen in datasets available on the OPEN-ADAS database. By using the rate coefficients and comparing modeling results with the new high-n Rydberg CX measurements, impurity transport coefficients are determined with well-documented 2σ confidence intervals for the first time. This demonstrates that high-n Rydberg spectroscopy provides important constraints on the determination of impurity transport coefficients. By additionally considering Bolometer measurements, which provide constraints on the overall impurity emissivity and, therefore, impurity densities, error bars can be reduced even further.

Instruments & Instrumentation↗

DOE OSTI2025

Application of Flex-QA Arrays in HTS Magnet Testing

Flexible PCB quench antennas have been very useful in providing high-quality high-resolution data in low temperature superconducting magnet tests. Similar multi-sensor arrays have been employed recently to cover a high temperature superconductor magnet tested at FNAL. In the present work, data taking conditions and magnet features to support the analysis framework are discussed. Then observations made during complete magnet powering cycles are described and analysis of quench antenna data are presented. Based on results, improvements to instrumentation and data taking are debated. Views on the future of flexible quench antenna sensors for HTS magnet diagnostics and operational support are shared.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Application of Flex-QA Arrays in HTS Magnet Testing

Stoynev, S.↗

DOE OSTI2022

Constrained model calibration of grain structure dependent spall dynamics in shock-loaded tantalum

Here, we perform a gas gun experiment by shock loading tantalum samples of varying grain structures to assess the suitability of a numerical model for simulating spall behavior. The observed differences in spall strength, as well spallation and re-compression history, are not captured in uncalibrated hydrodynamic simulations. An optimization is performed on the Johnson spall model to determine the best parameters that fit the observed trends. Linear stability analysis is employed to motivate bounds on those parameters. Herein, optimized simulations agree well with the experimental results, reproducing pullback depth and recompression timescales across the different samples tested. Further, the observed pullback time of the single crystal sample was found to imply, via the stability analysis, a percolation threshold in good agreement with the theoretical value for a body centered cubic lattice. Therefore, the combined linear stability and percolation analysis shows promise and may be applied to other materials with diverse microstructures. Collectively, the findings demonstrate that the model is suitable for reproducing spall-induced free surface behavior across various microstructures, but also points to caution in using model coefficients for uncalibrated microstructures.

58 GEOSCIENCES↗

DOE OSTI2021

The history of metal enrichment traced by X-ray observations of high-redshift galaxy clusters

ABSTRACT We present the analysis of deep X-ray observations of 10 massive galaxy clusters at redshifts 1.05 < z < 1.71, with the primary goal of measuring the metallicity of the intracluster medium (ICM) at intermediate radii, to better constrain models of the metal enrichment of the intergalactic medium. The targets were selected from X-ray and Sunyaev–Zel’dovich effect surveys, and observed with both the XMM–Newton and Chandra satellites. For each cluster, a precise gas mass profile was extracted, from which the value of r500 could be estimated. This allows us to define consistent radial ranges over which the metallicity measurements can be compared. In general, the data are of sufficient quality to extract meaningful metallicity measurements in two radial bins, r < 0.3r500 and 0.3 < r/r500 < 1.0. For the outer bin, the combined measurement for all 10 clusters, Z/Z⊙ = 0.21 ± 0.09, represents a substantial improvement in precision over previous results. This measurement is consistent with, but slightly lower than, the average metallicity of 0.315 solar measured at intermediate-to-large radii in low-redshift clusters. Combining our new high-redshift data with the previous low-redshift results allows us to place the tightest constraints to date on models of the evolution of cluster metallicity at intermediate radii. Adopting a power-law model of the form Z ∝ (1 + z)γ, we measure a slope $\gamma = -0.5^{+0.4}_{-0.3}$, consistent with the majority of the enrichment of the ICM having occurred at very early times and before massive clusters formed, but leaving open the possibility that some additional enrichment in these regions may have occurred since a redshift of 2.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Informing nuclear physics via machine learning methods with differential and integral experiments

Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Effect of CO 2 -brine-rock reactions on pore architecture and permeability in dolostone: Implications for CO 2 storage and EOR

Geologic carbon sequestration (GCS) is considered a feasible technology for storing substantive volumes of greenhouse gases in subsurface geological formations. In the reservoir, far from carbon dioxide (CO 2 ) injection wells or in post-injection scenarios, diffusion dominates over advection. This condition conjoins with spatially distributed geochemical reactions to induce heterogeneous changes in pore architecture, i.e. pore body and throat sizes or surface roughness. These changes can affect CO 2 transport properties and storage capacity. In this work, we investigated mineral dissolution and precipitation in dolomite samples saturated with a CO 2 -saturated brine at 93 °C and 34.5 MPa, aged without flow. Two rock types samples, i.e. intergranular- and vuggy-dominant, were selected to investigate changes in pore size, porosity and permeability under reactive conditions. Mineral dissolution and precipitation were characterized using scanning electron microscopy. Changes in pore size were quantified via time-domain nuclear magnetic resonance (TD-NMR) transverse relaxation time (T 2 ) and diffusion coefficient (D) distributions. We show that mineral dissolution likely occurs in highly permeable pathways. These observations are confirmed through analysis of (T 2 ) and diffusion coefficient (D) distributions. In contrast to results during CO 2 -enriched brine continuous injection, mineral precipitation was observed in micropores. The leftward shift of the T 2 peaks, corresponding to micropores, also evidenced mineral precipitation in lowpermeability zones. However, microscale alterations resulted only in a subtle increase in porosity and permeability. Results in this study shed light on effects of geochemical reactions on alteration of rock properties in diffusion-dominated regions during CO 2 storage.

58 GEOSCIENCES↗

DOE OSTI2023

Mass Spectrometry–Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Methods for the Enhancement of Security Probabilistic Risk Assessments

The US Department of Energy is exploring the methods associated with probabilistic risk assessment (PRA) and how they could be used to enhance physical security evaluations for nuclear facilities. A brief review of both international and US Nuclear Regulatory Commission physical security assessment methods is presented. From this review, and with an understanding of the technical elements associated with a traditional nuclear safety PRA, several observations are made pertaining to where physical security analysis methods could be enhanced. These observations principally support and enhance the answers within a physical security context to the risk triplet: (1) What can go wrong? (2) What is the likelihood? and (3) What are the consequences? Finally, benefits and technical challenges toward the development of a full security PRA are presented.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

Observational ozone datasets over the global oceans and polar regions (version 2024)

Studying tropospheric ozone over the remote areas of the planet, such as the open oceans and the polar regions, is crucial to understand the role of ozone as a global climate forcer and regulator of atmospheric oxidative capacity. A focus on the pristine oceanic and polar regions complements the available land-based datasets and provides insights into key photochemical and depositional loss processes that control the concentrations and spatiotemporal variability in ozone as well as the physicochemical mechanisms driving these patterns. However, an assessment of the role of ozone over the oceanic and polar regions has been hampered by a lack of comprehensive observational datasets. Here, we present the first comprehensive collection of ozone data over the oceans and the polar regions. The overall dataset consists of 77 ship cruises/buoy-based observations and 48 aircraft-based campaigns. The dataset, consisting of more than 630 000 independent ozone measurement data points covering the period from 1977 to 2022 and an altitude range from the surface to 5000 m (with a focus on the lowest 2000 m), allows systematic analyses of the spatiotemporal distribution and long-term trends over the 11 defined ocean/polar regions. The datasets from ships, buoys, and aircraft are complemented by ozonesonde data from 29 launch sites or field campaigns and by 21 non-polar and 17 polar ground-based station datasets. The datasets contain information on how long the observed air masses were isolated from land, as estimated by backward trajectories from the individual observation points. To extract observations representative of oceanic conditions, we recommend using a subset of the data with an isolation time of 72 h or longer, from the analysis with coincident radon observations. These filtered oceanic and polar data showed typically flat diurnal cycles at high latitudes, whereas daytime decreases in ozone (11 %–16 %) were observed at lower latitudes. The ship/buoy- and aircraft-based datasets presented here will supplement the land-based ones in the TOAR-II (Tropospheric Ozone Assessment Report Phase II) database to provide a fully global assessment of tropospheric ozone. The described dataset is available at https://doi.org/10.17596/0004044 (Kanaya et al., 2025).

Kanaya, Yugo [Japan Agency for Marine-Earth Scienc↗

DOE OSTI2024

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Combined Computer Vision and Deep Learning Approach for Rapid Drone-Based Optical Characterization of Parabolic Troughs

Optical accuracy is a primary driver of parabolic trough concentrating solar power (CSP) plant performance, but can be damaged by wind loads, gravity, error during installation, and regular plant operation. Collecting and analyzing optical measurements over an entire operating parabolic trough plant is difficult, given the large scale of typical installations. Distant Observer, a software tool developed at the National Renewable Energy Laboratory, uses images of the absorber tube reflected in the collector mirror to measure both surface slope in the parabolic mirror and offset of the absorber tube from the ideal focal point. This technology has been adapted for fast data collection using low-cost commercial drones, but until recently still required substantial human labor to process large amounts of data. A new method leveraging advanced deep learning and computer vision tools can drastically reduce the time required to process images. This new method addresses the primary analysis bottleneck, identifying featureless, reflective mirror corner points to a high degree of accuracy. Recent work has shown promising results using computer vision methods. The combined deep learning and computer vision approach presented here proved highly effective and has the potential to further automate data collection and analysis, making the tool more robust. The method presented in this paper automatically identified 74.3% of mirror corners within 2 pixels of their manually marked counterparts and 91.9% within 3 pixels. This level of accuracy is sufficient for practical Distant Observer analysis within a target uncertainty. A commercial drone collected video of over 100 parabolic trough modules at an operating CSP plant to demonstrate the deep learning and computer vision method's usefulness in processing large amounts of data. These troughs were successfully analyzed using Distant Observer, paired with the new deep learning and computer vision algorithm, and can provide plant operators and trough designers with valuable insight about plant performance, operating strategies, and plant-wide optical error trends.

computer vision↗

DOE OSTI2020

The ARM Data-oriented Metrics and Diagnostics Package for Climate Models - A New Tool for Evaluating Climate Models with Field Data

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Inter-comparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Large-scale Structure of the Heliospheric Current Sheets within the Heliosheath Inferred from Voyager 2 Observations

In this paper, we conducted an analysis of the heliospheric current sheets (HCSs) in the heliosheath (HS), utilizing observations by Voyager 2 between 2008 and 2018. Employing rigorous criteria, we identified a total of 34 HCSs that indicate significant changes in magnetic polarity. These occurrences were more prevalent during solar maximum periods when the HCS expanded to higher latitudes, coinciding with the spacecraft positioned at an average latitude of –31° from the solar equator. We determined certain features of the large-scale structures of the identified HCSs. Most importantly, employing two distinct methods indicates that the thickness of the HCSs within the HS ranges from ~0.003 to ~0.4 au with an average thickness of ~0.03 to ~0.1 au, depending on methods of event selection and fitting techniques. This thickness surpasses that known near 1 au or other heliospheric distances. It is also notably thicker than the typical proton inertial length, implying unfavorable conditions for magnetic reconnection. Additionally, our analysis reveals a frequent tilt of HCS planes relative to the solar equatorial plane by a varying angle up to several tens of degrees, likely implying a common occurrence of a warped structure of the HCS within the HS. Longitudinally, the HCS planes closely align with the Parker spiral field direction expected in the HS. Finally, for a large fraction of the identified HCS events, the HCS planes are likely characterized by a rotational discontinuity. These findings are valid within the limits of the 1 hr resolution data used in this study.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Studies on the Encapsulation of Brine Generated from a Process Using Selective Electrodialysis Reversal

This topical report summarizes the laboratory testing, geochemical modeling, and mineralogical testing conducted to examine the efficacy of wastewater encapsulation for the brine generated from the Saltworks Flex electrodialysis reversal technology. Within the laboratory testing, the short- and long-term physical properties of seven separate mix designs were examined along with the leaching of each mix design. Additionally, geochemical thermodynamic modeling was used along with mineralogical analyses to help gain a fundamental understanding of the chemistry involved in the encapsulation process. Encapsulation is a process that results in the immobilization of constituents of interest by creating an engineered material that contains the waste within its matrix, both physically and chemically, in such a manner that the receiving environment is protected from adverse effects. Within the context of this study, encapsulation mix designs involved mixing the Saltworks brine with fly ash and various additives, principally lime (calcium oxide) and cement, to form a paste that eventually sets and hardens into a monolith upon placement in a landfill. Moisture conditioning fly ash with wastewater sources for on-site landfill disposal is a process that is in use at some sites and has been presented as a viable option to dispose of liquid wastes. However, because chemical reactions that can lead to sequestration of constituents of interest are not optimized, leaching can occur. Therefore, in this study, mixes were created to mimic moisture conditioning, as well as engineered pastes of varying mix designs. The study found that engineered encapsulation mixes provide significant resistance to leaching compared to moisture conditioning alone. The best performing encapsulation mix used lime as the additive, which proved superior to the use of cement. After 90 days of curing, this encapsulated mix was 5.5 times stronger than the moisture conditioned mix. Additionally, this mix exhibited an approximate 2.5 order of magnitude drop in hydraulic conductivity compared to the moisture conditioned mix. When comparing leaching, nearly all the chloride was found to evolve from the moisture conditioned mix, whereas only 6.7% of the chlorine contained in the best performing encapsulated mix leached out. Mineralogical analysis via X-ray diffraction (XRD) showed a significant concentration of the chloride sequestering mineral, Friedel’s salt, in the best performing encapsulation mix, whereas none was observed in the moisture conditioned mix, pointing to it being the primary chemical mechanism for chloride retention, along with the physical impermeability of the material. With respect to the modeling study, the model was successful in predicting the presence of most mineral phases observed in the XRD analysis.

01 COAL, LIGNITE, AND PEAT↗