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DOE OSTI2024

Extracting the femtometer structure of strange baryons using the vacuum polarization effect

Abstract One of the fundamental goals of particle physics is to gain a microscopic understanding of the strong interaction. Electromagnetic form factors quantify the structure of hadrons in terms of charge and magnetization distributions. While the nucleon structure has been investigated extensively, data on hyperons are still scarce. It has recently been demonstrated that electron-positron annihilations into hyperon-antihyperon pairs provide a powerful tool to investigate their inner structure. We present a method useful for hyperon-antihyperon pairs of different types which exploits the cross section enhancement due to the effect of vacuum polarization at theJ/ψresonance. Using the 10 billionJ/ψevents collected with the BESIII detector, this allows a precise determination of the hyperon structure function. The result is essentially a precise snapshot of the$$\bar{\Lambda }{\Sigma }^{0}\,(\Lambda {\bar{\Sigma }}^{0})$$ Λ ¯ Σ 0 ( Λ Σ ¯ 0 ) transition process, encoded in the transition form factor ratio and phase. Their values are measured to beR = 0.860 ± 0.029(stat.) ± 0.015(syst.),$$\Delta {\Phi }_{\bar{\Lambda }{\Sigma }^{0}}=(1.011\pm 0.094({{\rm{stat.}}})\pm 0.010({{\rm{syst.}}}))\,{{\rm{r}}}ad$$ Δ Φ Λ ¯ Σ 0 = ( 1.011 ± 0.094 ( stat. ) ± 0.010 ( syst. ) ) r a d and$$\Delta {\Phi }_{\Lambda {\bar{\Sigma }}^{0}}=(2.128\pm 0.094({{\rm{stat.}}})\pm 0.010({{\rm{syst.}}}))\,{{\rm{r}}}ad$$ Δ Φ Λ Σ ¯ 0 = ( 2.128 ± 0.094 ( stat. ) ± 0.010 ( syst. ) ) r a d . Furthermore, charge-parity (CP) breaking is investigated in this reaction and found to be consistent with CP symmetry.

Science & Technology - Other Topics↗

DOE OSTI2025

Observation of a mixed close-packed structure in superionic water

The study of superionic (SI) water has been a highly active research area since its theoretical prediction. Despite significant experimental and computational efforts, its melting curve and the stability of different oxygen lattices remain debated, impacting our understanding of SI ice’s peculiar transport properties. Experimental results at lower pressures show disagreement, whereas data at higher pressures are scarce due to the extreme challenges of such experiments. In this work, we present ultrafast X-ray diffraction results of water compressed by multiple shocks to pressures up to ~ 180 GPa. At pressures exceeding 150 GPa and temperatures around 2500 K, our diffraction patterns challenge the pure FCC-SI phase model, providing experimental evidence of the mixed close-packed superionic phase predicted by advanced ab initio calculations. At lower pressures, we observe simultaneous signatures of BCC and FCC structures within a pressure-temperature range consistent with some static-compression experiments, helping to resolve contradictory results in literature. These insights offer new constraints on the stability domains of SI phases and reveal detailed structural features, such as stacking faults. Our results advance the structural understanding of high-pressure SI ice to a level approaching that of ice I polymorphs, with potential implications for water-rich interiors of giant planets.

Andriambariarijaona, Leon [Centre National de la R↗

DOE OSTI2020

Melting of charge order in the low-temperature state of an electronic ferroelectric-like system

Strong electronic interactions can drive a system into a state with a symmetry breaking. Lattice frustration or competing interactions tend to prevent symmetry breaking, leading to quantum disordered phases. In spin systems frustration can produce a spin liquid state. Frustration of a charge degree of freedom also can result in various exotic states, however, experimental data on these effects is scarce. In this work we demonstrate how in a Mott insulator on a weakly anisotropic triangular lattice a charge ordered state melts on cooling down to low temperatures. Raman scattering spectroscopy finds that κ -(BEDT-TTF) 2 Hg(SCN) 2 Cl enters an insulating “dipole solid” state at T =30K, but below T =15K the order melts, while preserving the insulating energy gap. Based on these observations, we suggest a phase diagram relevant to other quantum paraelectric materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

High-Fidelity Modeling and Experiments to Inform Safety Analysis Codes for Heat Pipe Microreactors

Heat pipe microreactors are reactor designs that primarily use liquid-metal heat pipes to cool the core. The main interest in heat pipes is the fact that they can remove heat passively. This, along with the use of liquid metal, allows the reactor to operate at higher temperatures. Although the use of heat pipes in nuclear reactors is new, liquid-metal heat pipe technology is mature. Nevertheless, experimental data on heat pipes are scarce, and very little is known about their behavior during abnormal operations and close to their thermal limits. Therefore, new experiments and accurate heat pipe simulations are needed to develop reliable closure models. This work describes a joint experimental and numerical investigation into heat pipes that attempts an initial closure of this gap. Further, the numerical and experimental efforts are currently proceeding in parallel, aimed at different aspects of heat pipes. The numerical part is focused on gaps in local closures, and the experiments capture the overall heat pipe behavior.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

233 U(n,γ) measurements in the Unresolved and Fast Regime at LANSCE [Slides]

Experimental 233 U(n,γ) cross section data in the literature are scarce and were measured decades ago. A new report suggests that a simultaneous measurement with capture would be useful, for 233 U fission is around one order of magnitude more likely than capture. Good discrimination between gammas coming from capture and fission is required. New measurements performed at LANL combine NEUANCE and DANCE.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Suggestions on the Vapor Pressure Determination of Molten Salts

The knowledge of the thermophysical properties of coolant and fuel in molten salts nuclear reactors (MSRs), such as thermal stability and vapor pressure, are of remarkable interest, particularly for simulating safe reactor operations. Molten salt reactorsMSRs use molten salt mixtures as the primary coolant and/or fuel and are expected to operate up to 800 °C. With the revival of interest in deploying MSRs, complete thermophysical and thermochemical characterization of these materials is of interest to industry, regulators, and researchers. Vapor pressure data for molten salts are scarce in the literature, both for pure salts but especially for eutectic mixtures. This report describes the effusion method, which consist of measuring the rate of escape of vapor molecules through a small orifice for the determination of the vapor pressure of compounds and may be useful on compounds such as molten salts. Thermogravimetric analysis records the mass loss as a function of time and temperature. There are two equations that can be used to relate the mass loss rate with the vapor pressure: (1) the Knudsen equation and (2) the Langmuir equation. To apply these equations, the system needs to reach a pseudo (or near) equilibrium condition. Therefore, the experimental conditions must be such that allow the condensed and the vapor/gas phases to be in equilibrium. In order to accomplish equilibrium like conditions, the sample must be in an almost-sealed cell except for a small orifice, from where the vapor escapes. In the Knudsen method vacuum is applied to eliminate the effect that the presence of other gas molecules could have on the evaporation rate of the sample. However, Langmuir alleged that in certain conditions, such as at low temperatures and/or when the vapor pressure is low, the rate of evaporation of a substance is independent of the presence of vapor around it. For that reason, some researchers, when measuring the mass loss of a substance with a predictable low vapor pressure, do not apply vacuum when using the Langmuir equation. However, they use a standard substance to parameterize the experimental setup. The use of one equation or the other will depend on the characteristics of the sample and the availability of an appropriate standard. Some important aspects related to the nature of the samples and vapor pressure measurements are described for future consideration.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Energy and AI: Evaluating Future Grid and Water Stress Due to Data Centers

Projections of the need for new data centers to support Artificial Intelligence (AI) are large but highly uncertain. Recent projections indicate up to a 15% annual growth rate in data center electricity demand within the next 5-10 years. Given that most electric utilities are required to have a reserve margin of roughly the same magnitude as the projected growth in demand, these new data center loads could soon threaten resource adequacy and reliability unless data centers build their own generation, interruptible loads are negotiated, commensurate new capacity and/or transmission is built, or some combination of these options. Similarly, depending on the cooling technology and geographic location of new data centers, they could threaten water adequacy in water scarce regions. This presentation highlights the grid and water implications of new data centers to support AI.

Mongird, Kendall (ORCID:0000000328077088)↗

DOE OSTI2023

Accessing the strong interaction between Λ baryons and charged kaons with the femtoscopy technique at the LHC

The interaction between baryons and kaons/antikaons is a crucial ingredient for the strangeness $S$ = 0 and $S$ = -2 sector of the meson–baryon interaction at low energies. In particular, the $Λ\overline{K}$ might help in understanding the origin of states such as the $Ξ$(1620), whose nature and properties are still under debate. Experimental data on $Λ–K$ and $Λ–\overline{K}$ systems are scarce, leading to large uncertainties and tension between the available theoretical predictions constrained by such data. In this Letter we present the measurements of $Λ–K$ + ⊕ $\overline{Λ}–K$ - and $Λ–K$ - ⊕ $\overline{Λ}–K$ + correlations obtained in the high multiplicity triggered data sample in pp collisions at $\sqrt{s}$ = 13 TeV recorded by ALICE at the LHC. The correlation function for both pairs is modeled using the Lednický–Lyuboshits analytical formula and the corresponding scattering parameters are extracted. The $Λ–K$ - ⊕ $\overline{Λ}–K$ + correlations show the presence of several structures at relative momenta k above 200 MeV/c, compatible with the $Ω$ baryon, the $Ξ$(1690), and $Ξ$(1820) resonances decaying into $Λ–K$ - pairs. The low $k^*$ region in the $Λ–K$ - ⊕ $\overline{Λ}–K$ + also exhibits the presence of the $Ξ$(1620) state, expected to strongly couple to the measured pair. The presented data allow to access the $ΛK$ + and $ΛK$ - strong interaction with an unprecedented precision and deliver the first experimental observation of the $Ξ$(1620) decaying into $ΛK$ - .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Machine learning enabled measurements of astrophysical (p,n) reactions with the SECAR recoil separator

he synthesis of heavy elements in supernovae is affected by low-energy (n,p) and (p,n) reactions on unstable nuclei, yet experimental data on such reaction rates are scarce. The SECAR (SEparator for CApture Reactions) recoil separator at FRIB (Facility for Rare Isotope Beams) was originally designed to measure astrophysical reactions that change the mass of a nucleus significantly. We used a novel approach that integrates machine learning with ion-optical simulations to find an ion-optical solution for the separator that enables the measurement of (p,n) reactions, despite the reaction leaving the mass of the nucleus nearly unchanged. A new measurement of the 58 Fe (p,n)⁢ 58 Co reaction in inverse kinematics with a 3.66 ± 0.12 MeV/nucleon 58 Fe beam (corresponding to 3.69 ± 0.12 MeV proton energy in normal kinematics) yielded a cross-section of 20.3 ± 6.3 mb and served as a proof of principle experiment for the new technique demonstrating its effectiveness in achieving the required performance criteria. This novel approach paves the way for studying astrophysically important (p,n) reactions on unstable nuclei produced at FRIB.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Models and Processes to Extract Drug-like Molecules From Natural Language Text

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations

Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biosynthesizable, biocompatible, and biodegradable polymers to replace petroleum-based plastics for addressing the global plastic pollution problem. Although PHAs offer a wide range of chemical diversity, the structure–property relationships in this class of polymers remain poorly established. In particular, the available experimental data on the mechanical properties is scarce. In this contribution, we have used molecular dynamics simulations employing a recently developed forcefield to predict chemical trends in mechanical properties of PHAs. Specifically, we make predictions for Young’s modulus, and yield stress for a wide range of PHAs that exhibit varying lengths of backbone and side chains as well as different side chain functional groups. Deformation simulations were performed at six different strain rates and six different temperatures to elucidate their influence on the mechanical properties. Our results indicate that Young’s modulus and yield stress decrease systematically with increase in the number of carbon atoms in the side chain as well as in the polymer backbone. In addition, we find that the mechanical properties were strongly correlated with the chemical nature of the functional group. The functional groups that enhance the interchain interactions lead to an enhancement in both the Young’s modulus and yield stress. Finally, we applied the developed methodology to study composition-dependence of the mechanical properties for a selected set of binary and ternary copolymers. Overall, our work not only provides insights into rational design rules for tailoring mechanical properties in PHAs, but also opens up avenues for future high throughput atomistic simulation studies geared towards identifying functional PHA polymer candidates for targeted applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

The coordination properties and ionic radius of actinium: A 120-year-old enigma

Actinium is an elusive element with untamed properties and represents a peculiar case in the periodic table, as its isotopes are all radioactive, the longest-lived one having only a 22-year half-life, and the availability of actinium isotopes remains very low (microgram level, at best), hindering research on its compounds. Despite being a natural element discovered more than 120 years ago, and despite an increasing interest in using one of its isotopes ( 225 Ac) for highly efficient cancer therapies, the chemistry of actinium is still largely unknown relative to other elements. Since Ac is the first element of the actinide series, it is accepted that its ion, Ac 3+ , is the most voluminous trivalent cation of the periodic table. However, the structural data available on Ac 3+ compounds are scarce and have mainly been collected in the 1940-1960's, when actinide chemistry was still in its infancy, and have not been put in perspective with the advances in the chemistry of other elements, making it difficult to accurately evaluate its actual size and coordination chemistry. Here, we review progress made on the chemistry of lanthanides and actinides and reevaluate the structural data published on Ac 3+ since the era of the Manhattan Project. The data are combined across different spectroscopic and characterization methods and presented in the context of periodic trends. When considering crystallographic data, solution chemistry results, and the nuclear properties of actinium isotopes, it appears that some structural parameters ascribed to the Ac 3+ ion may have been overestimated. This review can guide researchers interested in actinide sciences and those who are pursuing the development of actinium-based radiotherapies, from isotope production to clinical trials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

MSD CoP Webinar: Energy and AI

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Projections of the need for new data centers to support Artificial Intelligence (AI) are large but highly uncertain. Recent projections indicate up to a 15% annual growth rate in data center electricity demand within the next 5-10 years. Given that most electric utilities are required to have a reserve margin of roughly the same magnitude as the projected growth in demand, these new data center loads could soon threaten resource adequacy and reliability unless data centers build their own generation, interruptible loads are negotiated, commensurate new capacity and/or transmission is built, or some combination of these options. Similarly, depending on the cooling technology and geographic location of new data centers, they could threaten water adequacy in water scarce regions. This webinar will provide an overview of the interactions between energy and AI and highlight two MSD projects exploring the grid and water implications of new data centers to support AI. Presenters : Dr. Casey Burleyson (Pacific Northwest National Laboratory); Dr. Stephanie Morris (Pacific Northwest National Laboratory); Kendall Mongird (Pacific Northwest National Laboratory) Moderator: Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: June 16th, 2025 from 1-2 PM EST.

Artificial Intelligence↗

DOE OSTI2026

Exploring the potential of using L-Band InSAR for the mapping of flooded vegetation in tropical wetlands

Wetlands play a critical role in global water and carbon cycles, yet monitoring their water extent remains difficult, particularly beneath dense vegetation. SAR-based techniques such as backscatter thresholding are limited by complex scattering mechanisms, while fully polarimetric SAR (PolSAR) data capable of detecting doublebounce scattering remain scarce. To address these challenges, this study evaluates the potential of Interferometric SAR (InSAR) for mapping water surfaces beneath vegetation, termed flooded vegetation, using the Atrato floodplain in Colombia as a case study. We develop an automated workflow combining InSAR fringe detection with local phase homogeneity analysis and random sampling of processing parameters to generate probabilistic flooded vegetation maps. Applied to ALOS PALSAR-1 L-band image pairs from 2007–2011, the workflow captures seasonal fluctuations in flooded extent ranging from 500 to 1,500 km2. Compared to other L-band SAR inundation products, the InSAR-based maps identify broader flooded areas, with ~70% agreement in pairwise comparisons. Around 84% of detections align with existing wetland inventories and seasonal changes correspond with regional hydrological indicators, including terrestrial water storage anomalies and water gauge measurements. PolSAR analysis shows that InSAR complements backscatter-based methods by detecting inundation in areas with weak double-bounce signals. These findings suggest that combining InSAR with backscatter-based methods can improve detection of flooded vegetation, which is especially relevant for the upcoming NISAR mission that will offer frequent global L-band observations.

Coastal inundation↗

DOE OSTI2025

The nuclear charge radius of 13 C

The size is a key property of a nucleus. Accurate nuclear radii are extracted from elastic electron scattering, laser spectroscopy, and muonic atom spectroscopy. The results are not always compatible, as the proton-radius puzzle has shown most dramatically. Beyond helium, precision data from muonic and electronic sources are scarce in the light-mass region. The stable isotopes of carbon are an exception. We present a laser spectroscopic measurement of the root-mean-square (rms) charge radius of 13 C and compare this with ab initio nuclear structure calculations. Measuring all hyperfine components of the 2 3 S → 2 3 P fine-structure triplet in 13 C 4+ ions referenced to a frequency comb allows us to determine its center-of-gravity with accuracy better than 2 MHz although second-order hyperfine-structure effects shift individual lines by several GHz. We improved the uncertainty of R c ( 13 C) determined with electrons by a factor of 6 and found a 3σ discrepancy with the muonic atom result of similar accuracy.

Müller, Patrick [Technical Univ. of Darmstadt (Ger↗

DOE OSTI2022

Exploring New Ways to Classify Industries for Energy Analysis and Modeling

As the US moves closer to embracing a net zero greenhouse gas emissions position, combustion processes outside the power sector are becoming urgent concerns. Industry is an important end user of energy and relies on fossil fuels used directly for process heating and as feedstocks for a diverse range of applications. Fuel and energy use by industry is heterogeneous, meaning that even a single product group can vary broadly in its production routes and associated energy usage. In the US, the North American Industry Classification System (NAICS) serves as the basis for data collection and reporting. In turn, data based on NAICS is the foundation of most US energy modeling. Thus, the effectiveness of NAICS at representing energy use is a limiting condition for plans to improve energy efficiency and alternatives to fossil fuels in industry. Facility-level data to build more detail into heterogeneous sectors is scarce. This work explores alternative classification schemes for industry based on energy use characteristics, and provides a validation of an approach to make facility-level energy use estimates based on publicly available data from the greenhouse gas reporting program. First, several approaches to industrial taxonomies and their usefulness for industrial energy modeling are summarized. Data from Industrial Assessment Centers is analyzed using unsupervised machine learning techniques to detect clusters. Cladistics, an approach from biology, is adapted to energy and process characteristics of industries. A cladogram is presented for evolutionary directions in the iron and steel sector. Cladograms are a promising tool for constructing scenarios and summarizing directions of sectoral innovation. Finally, validation is performed for facility-level energy estimates from the US EPA Greenhouse Gas Reporting Program. This validation assists in making this data source available for use in energy modeling. Together, this work explores alternative approaches for categorizing industries in a way that aids understanding energy use, and presenting pathways for the future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Precision Measurements of the Neutron Magnetic Form Factor to High Momentum Transfer using Durand's Method

Protons and neutrons, collectively known as nucleons, along with electrons, constitute the funda- mental building blocks of the visible universe. Understanding their internal structure is crucial for addressing key scientific questions about our origin and existence. Elastic electron-nucleon scatter- ing provides insights into the spatial distributions of charge and current within nucleons through their electromagnetic form factors. Accurate knowledge of these form factors over a broad range of Q2, the squared four-momentum transfer in the scattering process, reveals details about the nucleon’s internal structure. However, high-Q2 data of the nucleon electromagnetic form factor is scarce due to the challenges associated with such measurements. This thesis reports preliminary results from high-precision measurements of the neutron magnetic form factor (Gn M ) to unprecedented Q2 using Durand’s method, also known as the “ratio” method. Systematic errors are greatly reduced by extracting Gn M from the ratio of neutron-coincident (D(e, e'n)) to proton-coincident (D(e, e'p)) quasi-elastic electron scattering from deuteron. The scattered electrons were detected in the BigBite spectrometer, which features multiple Gas Elec- tron Multiplier (GEM) layers with large active area for high-precision tracking at very high rates. Simultaneous nucleon detection was performed by the Super BigBite spectrometer, which utilizes a dipole magnet with large solid angle acceptance at forward angles and a novel hadron calorimeter with very high and comparable detection efficiencies for both protons and neutrons. This setup could handle very high luminosity, making high-Q2 measurements feasible. Data were collected at five Q2 points: 3, 4.5, 7.4, 9.9, and 13.6 (GeV/c)2. Preliminary results are reported for all, with the lowest two Q2 points in good agreement with existing world data, while the higher points significantly extend the Q2 range in which Gn M is known accurately. The precision of the highest Q2 point is expected to remain unmatched for years to come.

Datta, Provakar↗