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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An Overview of the Molten Salt Thermal Properties Database–Thermophysical, Version 4.0 (MSTDB-TP V.4.0)

A central repository of thermophysical and thermochemical properties of molten salt compositions of relevance to molten salt reactors (MSRs) is vital in supporting the broad community of MSR developers, who are at various stages of developing and deploying their reactor designs. In general, these MSR designs differ significantly from developer to developer (e.g., with respect to the hardness of the neutron spectra, level of fissile loading, target multicomponent temperatures and power levels, and moderating capabilities). Therefore, the fuel and coolant salts being considered vary greatly: they may be chlorides or fluorides, they utilize different actinides at different ratios, and the cations in the melt are selected based on perceived advantages and disadvantages. Considering the general need for thermal properties, and the vastness of the array of potential candidate salt mixtures, the Molten Salt Thermal Properties Database (MSTDB) was initiated in 2018 with the goal of providing thermophysical and thermochemical characterization of key molten salt compounds and mixtures across their temperature and compositional domains. The MSTDB is thus divided into the thermophysical arm (MSTDB-TP) and the thermochemical arm (MSTDB-TC). The MSTDB is an effort funded by the Department of Energy, Office of Nuclear Energy (DOE-NE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, and the MSR Campaign. This report provides an overview of the MSTDB-TP v4.0 in terms of the data contained within, the state of the tools used to access the data, the availability of predictive models that leverage the raw data in the database, the preliminary status of developmental efforts that are currently underway, and an account of future goals for MSTDB-TP. The primary goal for the update from MSTDB-TP v.3.1 to v4.0 was the incorporation of surface tension data into the database; this property is important for thermal hydraulics modeling and species transport in other tools that have been developed under the NEAMS program. A breakdown of the surface tension data that have been added into MSTDB-TP v4.0 is provided herein, and the manner in which the quality of the data has been assessed is also documented. For MSTDB-TP v4.0, newly published thermophysical property data—primarily from collaborative experimental efforts under the MSR Campaign—have been incorporated into the database, and the resulting expansion is documented here. Because of the size to which MSTDB-TP has grown, the raw data format has now been recast into JavaScript Object Notation (JSON) format for easier connection with the MSTDB-TP application programming interface (API). Saline; the pre-existing comma-separated value (CSV) format has been deprecated but is still maintained, accessible, and up to date. As a final effort in packaging the MSTDB-TP v4.0 update, the graphical user interface (GUI) for MSTDB has been updated to allow full accessibility to the density and viscosity predictive models, which are based on Redlich-Kister expansions of MSTDB-TP raw data. Some other major aspects of this report, in terms of preliminary and future work, include: (1) documentation of the formalism and preliminary testing of a kinetic theory model that may act as a predictive model for thermal conductivity; (2) documentation of the candidate predictive models that may be considered in the future for surface tension, making use of the surface tension data now in MSTDB-TP v4.0; (3) a preliminary account of a data collection process that will enable the filling of additional gaps within MSTDB-TP, namely with data which have been collected computationally (e.g., through ab initio molecular dynamics).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Radiation-induced alteration of apatite on the surface of Mars: first in situ observations with SuperCam Raman onboard Perseverance

Abstract Planetary exploration relies considerably on mineral characterization to advance our understanding of the solar system, the planets and their evolution. Thus, we must understand past and present processes that can alter materials exposed on the surface, affecting space mission data. Here, we analyze the first dataset monitoring the evolution of a known mineral target in situ on the Martian surface, brought there as a SuperCam calibration target onboard the Perseverance rover. We used Raman spectroscopy to monitor the crystalline state of a synthetic apatite sample over the first 950 Martian days (sols) of the Mars2020 mission. We note significant variations in the Raman spectra acquired on this target, specifically a decrease in the relative contribution of the Raman signal to the total signal. These observations are consistent with the results of a UV-irradiation test performed in the laboratory under conditions mimicking ambient Martian conditions. We conclude that the observed evolution reflects an alteration of the material, specifically the creation of electronic defects, due to its exposure to the Martian environment and, in particular, UV irradiation. This ongoing process of alteration of the Martian surface needs to be taken into account for mineralogical space mission data analysis.

Science & Technology - Other Topics↗
DOE OSTI2025

Better practices for inferring ecosystem water use strategy from eddy covariance data

Eddy covariance data are critical for inferring ecosystem water use strategies. Yet, such inferences are sensitive to a range of assumptions applied across studies, hindering our understanding of water use strategies within and across eddy covariance sites. A recent analysis across 151 FLUXNET2015 and AmeriFlux-FLUXNET datasets found that poor model performance was the key driver of non-robust inferences of ecosystem water use strategies. Here, we leverage this previous analysis to (i) identify the specific assumptions that improve inference model performance across most sites, (ii) explain the mechanisms behind the performance improvements, and (iii) check whether better performance improves water use inference. We find that the common practice of fitting a model to canopy conductance (G c ) derived from the evapotranspiration (ET) observations, rather than to observed ET itself, artificially amplifies data errors and degrades the model performance. Next, accounting for vegetation dynamics by applying a growing season filter or incorporating satellite LAI data improves performance, but the former practice may remove soil water stress periods. Lastly, using the leaf-to-air vapor pressure deficit (VPD l ) derived from ET observations as a model input may artificially inflate performance. Based on these results, we recommend selecting observed ET (rather than derived G c ) as the response variable, carefully accounting for vegetation dynamics, and avoiding derived VPD l as a model input; these best practices improve model performance by c. 20% and robustness by c. 80% across all eddy covariance sites. Nevertheless, the performance improvements do not always correspond to more robust inference of water use strategies, as model parameter selection and surface energy budget closure corrections still strongly influence the ecosystem water use parameter estimation in a site-specific manner.

AmeriFlux↗
DOE OSTI2026

Atomic‐Scale Surface Imaging of Bulk Epitaxial CsPbBr 3 Perovskite Single Crystals on Mica Using Light Assisted Scanning Tunneling Microscopy at Low‐Temperature (80 K)

Epitaxial single-crystalline CsPbBr 3 perovskite films on mica, prepared ex situ, are explored using a low-temperature scanning tunneling microscope (STM) by probing the unoccupied electronic states of their surface in ultra-high vacuum (UHV) at 80 K. Light-assisted STM measurements under a broadband illumination with visible light were employed to enhance and stabilize surface conductivity. STM imaging across the surface of macroscopic bulk CsPbBr 3 films reveals large flat terraces characterized by a specific type of surface reconstruction, consisting of parallel rows of U-shaped atomic nanostructures. These structures are spaced by 12 Å and exhibit an internal feature size of 5.1 Å. Density functional theory (DFT) calculations reproduce the experimental observations and reveal a competition between different orthorhombic CsPbBr 3 (110) surface reconstructions: a Cs-rich structure, identified as the most energetically stable, and three alternative Pb–Br-rich reconstructions, which are slightly higher in energy yet remain consistent with the STM data. Additional analyses that explicitly account for the mica substrate exclude the cubic CsPbBr 3 phase and other orthorhombic surface orientations, while showing that variations in the mica surface termination do not alter the preferred CsPbBr 3 (110) reconstruction. In conclusion, this combined approach thereby confirms our assignment and resolves previous STM interpretations of CsPbBr 3 .

36 MATERIALS SCIENCE↗
DOE OSTI2025

Optical interference effect in strong-field electronic coherence spectroscopy

Here, we investigate strong-field-induced electronic coherences in argon and molecular nitrogen ions created by high-intensity, few-cycle infrared laser pulses. This is a step toward the long-sought goal of strong-field coherent control in molecular chemistry. We employ high-intensity, few-cycle infrared laser pulses in a pump-probe setup to investigate a recent prediction that electronic coherences in nitrogen molecules change the ion yields versus pump-probe delay. [Yuen et al., Phys. Rev. A 109, L011101 (2024)]. The predicted coherence signals in molecular nitrogen could not be resolved above the optical interference of the pump and probe pulses; a simultaneous measurement clearly resolved the induced cation fine-structure coherence in strong-field-ionized argon. The results of our comparison with simulations suggest that optical interference effects manifest differently in each ionic species and must be carefully accounted for when interpreting experimental data. We find that nonsequential double ionization in the low-intensity region of the focal volume can reduce the visibility of coherence generated by two-pulse sequential ionization, and we quantify the importance of pulse shape and spectral characteristics for isolating the desired coherence signals.

charge migration↗
DOE OSTI2024

An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models

Abstract Background Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER. Results The HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a $$\sim$$ ∼ $3000 Xilinx Alveo U50 FPGA accelerator card, $$\sim$$ ∼ 227× faster than the optimized SSV implementation in nhmmer . Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer’s SSV with one thread and 35× faster than nhmmer with four threads, and uses $$\sim$$ ∼ 31% the energy of a traditional high end Intel CPU. Conclusions HAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

WFIP3 - BARG site - NREL Profiling Lidar (Windcube v2.1) / Derived Data

This dataset contains derived data from the profiling lidar (Windcube v2.1) deployed at WFIP3's BARG. The derived data files herein are based on the lidar's STA files (i.e., the 10-min average data files). Note: this version of the data has been corrected to account for the motion of the barge.

17 WIND ENERGY↗
DOE OSTI2026

Energy Emissions Accounting Methods Can Determine Whether Direct Air Capture with Storage Achieves Net Removal

The voluntary carbon market within the United States has expanded rapidly in recent years and enabled private companies and other organizations to provide revenue streams to carbon dioxide removal (CDR) technologies. For a CDR technology to participate in the voluntary carbon market (VCM), the emissions associated with constructing and operating the technology must be less than the CO 2 captured from the atmosphere. Assessing the extent to which this is true for direct air capture with storage (DACS), a relatively energy-intensive CDR technology, strongly depends on the accounting method used to assess the emissions intensity of purchased energy. We simulate the hourly weather-dependent operation of sorbent- and solvent-based DACS in California, Louisiana, Texas, and Wyoming, representing a wide range of local weather and electric and natural gas grid compositions. In all cases, the single most important emissions accounting decision is the method used to estimate the emissions intensity of purchased grid electricity, which varies the calculated net removal by −1049% to +108%. All other factors influencing net removal introduce a variation of at most ±14%. No electricity emissions accounting method is universally conservative across all scenarios, and none is objectively more accurate. High-spatiotemporal-resolution, high-quality, publicly available data sets and models for electricity emissions accounting do not currently exist and are urgently needed to enable standardization of emissions accounting methods to more accurately determine the true emissions impacts of DACS and other energy-intensive facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification

Non-invasive simulations of coronary hemodynamics have improved clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree, which ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. We assimilate patient-specific measurements of myocardial blood flow from clinical CT myocardial perfusion imaging to estimate branch-specific coronary artery flows. Simulated noise in the clinical data is used to estimate the joint posterior distributions of the model parameters using adaptive Markov Chain Monte Carlo sampling. Additionally, the posterior predictive distribution for the relevant quantities of interest is determined using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. This leads to improved correlations between high- and low-fidelity model outputs. Our framework accurately recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement noise uncertainty. We observe substantial reductions in confidence intervals for estimated quantities of interest compared to single-fidelity Monte Carlo estimation and state-of-the-art multi-fidelity Monte Carlo methods. This holds especially true for quantities of interest that showed limited correlation between the low- and high-fidelity model predictions. In addition, the proposed multi-fidelity Monte Carlo estimators are significantly cheaper to compute than traditional estimators, under a specified confidence level or variance. The proposed pipeline for personalized and uncertainty-aware predictions of coronary hemodynamics is based on routine clinical measurements and recently developed techniques for CT myocardial perfusion imaging. The proposed pipeline offers significant improvements in precision and reduction in computational cost.

Bayesian parameter estimation↗
DOE OSTI2026

SO(3)-invariant PCA with application to molecular data

Principal component analysis (PCA) is a fundamental technique for dimensionality reduction and denoising; however, its application to three-dimensional data with arbitrary orientations -- common in structural biology -- presents significant challenges. A naive approach requires augmenting the dataset with many rotated copies of each sample, incurring prohibitive computational costs. In this paper, we extend PCA to 3D volumetric datasets with unknown orientations by developing an efficient and principled framework for SO(3)-invariant PCA that implicitly accounts for all rotations without explicit data augmentation. By exploiting underlying algebraic structure, we demonstrate that the computation involves only the square root of the total number of covariance entries, resulting in a substantial reduction in complexity. We validate the method on real-world molecular datasets, demonstrating its effectiveness and opening up new possibilities for large-scale, high-dimensional reconstruction problems.

Fraiman, Michael [Tel Aviv Univ., Tel Aviv (Israel↗
DOE OSTI2026

Livewire User Guide

The Livewire Data Platform houses a catalog of transportation- and mobility-related project data, as well as a publications database, making it easy to search and share data. It allows transportation researchers, industry, and academic partners to increase the visibility of their projects within the research community, securely share and preserve data, and leverage datasets from other projects. Public data on Livewire are open to anyone with a Livewire account. This guide will help Livewire users understand how to store project data as a data steward, as well as access data as a data consumer.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

New Data-Driven Constraints on the Sign of Gluon Polarization in the Proton

Recently, the possible existence of negative gluon helicity Δ g has been observed to be compatible with existing empirical constraints, including from jet production in polarized proton-proton collisions at the Relativistic Heavy Ion Collider, and lattice QCD data on polarized gluon Ioffe time distributions. We perform a new global analysis of polarized parton distributions in the proton with new constraints from the high- x region of deep-inelastic scattering (DIS). A dramatic reduction in the quality of the fit for the negative Δ g replicas compared to those with positive Δ g suggests that the negative Δ g solution cannot simultaneously account for high- x polarized DIS data along with lattice and polarized jet data. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Explainable Machine Learning for Functional Data

Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).

Elastic Shape Analysis↗
DOE OSTI2025

HexWeather: Hexagonal Spatial Data Aggregation for Weather-Driven Grid Resilience Analysis

Extreme weather accounts for over 8 0 % of major U.S. power outages since 2000, highlighting the need for spatial tools that align weather data with the irregular boundaries of electric infrastructure. This paper introduces HexWeather, a modular, resolution-aware framework for aggregating historical and forecasted weather data using Uber's H3 hexagonal spatial indexing system. Unlike traditional methods that rely on state or county-level grids, HexWeather enables weather analysis across custom geographies such as utility service areas where public datasets are often unavailable or misaligned. Using Open-Meteo data, we evaluate how H3 resolution affects anomaly detection, spatial variability, and forecast uncertainty across three scales: state, county, and utility. Results show that while coarse resolutions suffice for broad trend tracking, finer resolutions are essential for identifying localized variability and operational risks. By applying metrics like Z-score standard deviation and interquartile range, HexWeather quantifies the spatial spread of both historical anomalies and forecasted conditions, allowing users to assess resolution adequacy for each analysis. This framework supports rapid weather data reuse, reproducible anomaly detection, and predictive modeling for infrastructure resilience. By bridging spatial misalignment in traditional datasets and enabling retrospective and forward-looking analysis within the same pipeline, HexWeather lays the groundwork for better post event analysis, outage prediction, and resilience planning.

Morris, Jacob [ORNL]↗
DOE OSTI2026

Thriving in the Carbon-Aware Market: How to Account for Emissions in the Era of Carbon-Centered Trade Policies

Emerging global policies, such as the European Union's enacted Carbon Border Adjustment Mechanism and similar policies under development in Canada, Australia and the United Kingdom, will place a premium on goods traded into their territories with higher embedded emissions than those produced domestically. U.S. manufacturers could stand to benefit from such policies; given the investments U.S. industry has made to reduce the energy and emission intensities of its operations. For example, the overall GHG intensity of U.S. steel production in 2019 was ~0.96 t CO2/t steel, less than half that of China (~1.97 t CO2/t steel), and bested only by Italy. To realize these benefits, transparent, accurate, interoperable and accepted embedded emissions accounting and calculation methods are required. Achieving this requires overcoming challenges related to data availability, boundary definitions, and product definitions among others, both at individual facilities as well as through value chains. We will present technical findings on methodology considerations and data-availability constraints for determining the emissions of traded goods, using steel as a pilot and leveraging publicly available data. Issues such as determining the appropriate scope for emissions accounting, implications of the specificity of product chosen, emissions allocation in multi-product facilities, and enumeration of emissions for products manufactured across multiple facilities will be discussed. By sharing the results of our efforts, we aim to inform the development and execution of embedded emissions accounting methods from a technical perspective such that U.S. manufacturers can thrive in emerging global markets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A Computational Review of Privacy-Preserving Mechanisms for the Smart Grid

Smart grid technologies have rapidly become one of the largest and most comprehensive sources of data for the modern utility. For the most part, data streams are seen as an essential tool that enable utilities to carry their day-to-day business operations, but they also create the need for efficient and secure data management strategies. In the context of the smart grid, ensuring data privacy is becoming an increasing concern due to a combination of factors that range from shifts in operational paradigms and rapid technology evolution to changes in legislation. Furthermore, researchers have highlighted the risks associated with improperly protected energy records. For example, energy consumption data from homes could be used to infer the behaviors and habits of home occupants through activity recognition or user profiling (Fan, 2017), which may lead to unfair service pricing, targeted advertising, or other personal security violations. Similarly, Electric Vehicles’ (EVs) charging metadata could be used to reveal private information about the owner such as their payment methods, preferred charging stations, and other locational and timing information that could be used to reconstruct the vehicle owner’s behaviors. The privacy of user data, even when used for statistical analysis or machine learning training processes, also needs to be carefully considered, as an individual’s private traits may still be vulnerable if their inclusion/exclusion greatly impacts the result or could be linked to a public dataset through cross-reference. The breach of user privacy also has severe impacts for organizations that store, transmit, or work on the data in the form of diminishing the public’s trust in them while potentially incurring legal consequences (e.g., fines and suspensions under the European Union General Data Protection Regulation, Health Insurance Portability and Accountability Act, etc.). Because of these risks, several privacy-preserving mechanisms are available to help organizations comply with privacy legislations and prevent the unauthorized and malicious use of user data. In light of these concerns, this report focuses on performing a computational review of privacy-preserving mechanisms that have received a significant amount of interest in literature. It specifically focuses on 1) homomorphic encryption, 2) zero-knowledge proofs, 3) differential privacy, and 4) federated learning. It is worth noting that although many of the methods presented in this document rely on cryptographic primitives, their intent is not to provide perfect secrecy, but rather to enable users to maintain privacy, and thus they shall not be compared or equated to other constructs that are aimed to address cybersecurity constructs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Localized Interactions in Neutrino Simulations

The Deep Underground Neutrino Experiment (DUNE) requires precise modeling of neutrino--nucleus interactions to achieve its neutrino-oscillation measurement goals. GENIE, the Monte Carlo event generator used by DUNE, exhibits a known discrepancy with MicroBooNE measurements of transverse kinematic imbalance (TKI): the data display a larger high-TKI tail while maintaining a peak similar to that predicted by the baseline GENIE model. Previous variations of final-state interaction (FSI) strength affected both the peak and the tail and therefore did not resolve the discrepancy. This work investigates whether inconsistencies between local and global treatments of intranuclear physics contribute to the observed mismodeling. The GENIE FSI routines were modified to use the particle position when generating nucleons, thereby introducing a local-density treatment in the hA and hN intranuclear models for their 2018 and 2025 implementations. Meson-exchange-current (MEC) localization was also tested for the hN 2018 model. Comparisons of the intranuclear scattering-center momentum and its radial dependence confirm that the localization was implemented as intended. Localization produces modest changes in the proton momentum spectrum, primarily at low momentum, but only a small change in the TKI distribution between approximately \SI{0.25}{\giga\electronvolt} and \SI{0.40}{\giga\electronvolt}. These changes are insufficient to account for the discrepancy with MicroBooNE data. Although a localized treatment improves the internal consistency of the GENIE model, the origin of the TKI discrepancy remains unresolved.

Bulla, Braden [Unlisted, US, IL]↗
DOE OSTI2026

Studies of Localization Effects on Transverse Kinematic Imbalance in the GENIE Generator

The forthcoming Deep Underground Neutrino Experiment (DUNE) requires precise modeling of neutrino--nucleus interactions to achieve its neutrino-oscillation measurement goals. GENIE, the Monte Carlo event generator used by DUNE and other Fermilab-based experiments as a central value model for neutrino-nucleus cross sections, exhibits known discrepancies in comparison to MicroBooNE cross section data, especially when considering neutrino scattering events' transverse kinematic imbalance (TKI). For quasielastic-like measurements, the data show a larger transverse missing momentum tail while maintaining a peak similar to that predicted by the baseline GENIE model. Previous attempts at a solution include variations of final-state interaction (FSI) strengths, incorrectly decreasing both the peak and tail, thus leaving the discrepancy unresolved. This work investigates whether inconsistencies between local and global treatments of intranuclear physics contribute to the observed mismodeling. In this work, GENIE FSI routines have been modified to directly utilize intranuclear particle positions, thereby introducing a newly localized momentum treatment in correlation with the nominal density in both the hA and hN intranuclear cascade models for their 2018 and 2025 variants. Meson-exchange-current (MEC) localization was also tested for the hN 2018 model. Comparisons of the intranuclear nucleons' ("scattering center") momentum and its radial dependence validate the localization implementation. However, while such consistent localization produces modest changes in the proton momentum spectrum at low-to-medium momenta, only small changes are observed in the TKI distributions. These changes are insufficient to account for the discrepancy with MicroBooNE data. Although a localized treatment improves the internal consistency of the GENIE model, the origin of the TKI discrepancy remains unresolved by such a solution.

Bulla, Braden [Ctr. Coll., Danville; Fermilab]↗