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DOE OSTI2026

thornado+FLASH-X: A Hybrid Discontinuous Galerkin–Implicit-explicit and Finite-volume Framework for Neutrino-radiation Hydrodynamics in Core-collapse Supernovae

We present neutrino-transport algorithms implemented in the toolkit for high-order neutrino-radiation hydrodynamics (thornado) and their coupling to self-gravitating hydrodynamics within the adaptive mesh refinement–based multiphysics simulation framework FLASH-X. thornado, developed primarily for simulations of core-collapse supernovae (CCSNe), employs a spectral, six-species two-moment formulation with algebraic closure and special-relativistic observer corrections accurate to $\mathcal{O}(v/c)$, and uses discontinuous Galerkin (DG) methods for phase-space discretization combined with implicit-explicit time stepping. A key development is a nonlinear neutrino–matter coupling algorithm based on nested fixed-point iteration with Anderson acceleration, enabling fully implicit treatment of collisional processes, including energy-coupling interactions such as neutrino–electron scattering and pair production. Coupling to finite-volume (FV) hydrodynamics is achieved through a hybrid DG-FV representation of the fluid variables and operator-split evolution within FLASH-X. The implementation is verified using basic transport tests with idealized opacities and relaxation and deleptonization problems with tabulated microphysics. Spherically symmetric CCSN simulations demonstrate accuracy and robustness of the coupled scheme, including close agreement with the CCSN simulation code Chimera. An axisymmetric CCSN simulation further demonstrates the viability of DG-based neutrino transport for multidimensional supernova modeling within FLASH-X. thornado’s neutrino-transport solver is GPU-enabled using OpenMP offloading or OpenACC, and all CCSN applications included in this work use the GPU implementation. Together, these results establish a foundation for future enhancements in physics fidelity, numerical algorithms, and computational performance, for increasingly realistic large-scale CCSN simulations.

Endeve, Eirik [Oak Ridge National Laboratory (ORNL↗

DOE OSTI2025

Geometric GNNs for charged particle tracking at GlueX

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this task. In this study, we evaluate the GNN model for track finding on the data from the GlueX experiment at Jefferson Lab. We use simulation data to train the model and test on both simulation and real GlueX measurements. We demonstrate that GNN-based track finding outperforms the currently used traditional method at GlueX in terms of segment-based efficiency at a fixed purity while providing faster inferences. We show that the GNN model can achieve significant speedup by processing multiple events in batches, which exploits the parallel computation capability of graphical processing units (GPUs). Finally, we compare the GNN implementation on GPU and field-programmable gate array and describe the trade-off.

batched GNN pipeline↗

DOE OSTI2024

IDAES-PSE 2.5.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.5.0 Release Highlights Upcoming Changes IDAES will be switching to the new Pyomo solver interface in the next release. Whilst this will hopefully be a smooth transition for most users, there are a few important changes to be aware of. The new solver interface uses a different version of the IPOPT writer (“ipopt_v2”) and thus any custom configuration options you might have set for IPOPT will not carry over and will need to be reset. By default, the new Pyomo linear presolver will be activated with ipopt_v2. Whilst are working to identify any bugs in the presolver, it is possible that some edge cases will remain. IDAES will begin deploying a new set of scaling tools and APIs over the next few releases that make use of the new solver writers. The old scaling tools and APIs will remain for backward compatibility but will begin to be deprecated. New Models, Tools and Features New diagnostics check for near-parallel variables and constraints. New diagnostics tools for identifying causes of infeasibility in models. New example for creating a custom model of a liquid-liquid extractor unit operation. Bug Fixes Fixed bug in Gibbs reactor that caused it to appear to have additional spurious degrees of freedom. Fixed bug in the Modular Property Framework that would cause errors when trying to use phase-based material balances with phase equilibria. Fixed bug in Modular Properties Framework that caused errors when initializing models with non-vapor-liquid phase equilibria. Testing and Robustness Deployed the IDAES Diagnostics Toolbox to confirm that there are no structural or numerical issues in the core model libraries. Additional robustness tests for core model, and some associated improvements in the converge tester class. Fixed a number of issues that were causing unexpected warnings to be emitted during testing. Deprecations and Removals Removed examples for RIPE tool which has not been supported for a number of releases.

AS↗

DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗

DOE OSTI2026

Key Drivers for Offshore Wind Deployment in the Western United States

The western coast of the United States has abundant natural resources, including wind, sun, and water. Despite strong ocean winds, offshore wind energy (OSW) in the Western United States is in the early stages of technology development due largely to the deep water off the coast, which requires floating platforms rather than fixed‐bottom turbines. Floating OSW increases the technical difficulty of installation and thus the cost relative to both fixed‐bottom OSW and land‐based turbines. OSW is a potential generation option to help meet increasing demand on the west coast of the United States because it likely has fewer land‐use conflicts than other technologies and complements sources in the existing electricity supply by providing energy during times of high system stress. This paper examines the possible drivers and barriers to OSW deployment on the West Coast using the National Laboratory of the Rockies' state‐of‐the‐art capacity expansion model and the Regional Energy Deployment System (ReEDS) model. We use ReEDS to explore a multitude of future scenarios looking at key drivers for OSW deployment, including variations on the cost of OSW, electricity demand growth, and the availability of competing technologies to examine the factors that may play a role in OSW growth. Assuming coastal state policies such as renewable portfolio and clean energy standards remain in place, we find that OSW can play a role in meeting electricity demand and provide energy during stressful grid conditions and find that deployment from the least‐cost investment model ranges from 7.6 to 38 GW by 2045.

17 WIND ENERGY↗

DOE OSTI2026

Impact of vertical and seasonal variation in leaf traits on simulating soybean canopy photosynthesis via 1D and 3D modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax 25 ), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax 25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax 25 profile achieved comparable performance to the scheme with the measured Vcmax 25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R 2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R 2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Status of the Jefferson Lab Eta Factory (JEF) experiment

The Jefferson Lab Eta Factory (JEF) experiment is an experiment running in Hall D at Jefferson Lab that focuses on studying the decays of the ¿ meson. These decays provide a rich laboratory for searching for new charge conjugation violating / parity conserving (CVPC) processes, looking for hints of Beyond Standard Model physics, and probing higher order terms in Chiral Perturbation Theory. The flagship channel is the rare decay ¿ ¿ p0¿¿, the measurement of which required an upgrade to the existing equipment in Hall D. The experiment uses the GlueX detector, a fixed-target large acceptance spectrometer based on a solenoid magnet containing drift chambers for tracking charged particles and a lead-scintillator barrel calorimeter in the central region and an array of 4 × 4 × 45 cm3 lead glass blocks in the forward region for detecting neutral particles. During the last two years the inner 80×80 cm2 region of the forward calorimeter has been replaced by an array of 2 × 2 × 20 cm3 lead tungstate crystals, which provide factors of two improvement in energy and position resolution. The first round of data taking with this configuration took place this year. A first look at the data will be presented.

Taylor, Simon [Thomas Jefferson National Accelerat↗

DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗

DOE OSTI2026

Secondary-Side Active Rectifier Synchronization and Control in LCC-LC Compensated Inductive Power Transfer Systems

This paper proposes a communication-less synchronization and output power control strategy for LCC-LC compensated inductive power transfer systems with a secondaryside active rectifier. By replacing the passive rectifier with an active bridge, the proposed architecture eliminates the need for a separate secondary-side DC-DC converter and enables direct regulation of battery power. The key challenge in such a system is achieving robust synchronization of the secondary active bridge with the primary inverter without access to primary-side signals. To address this issue, the secondary-side variables are analyzed and the current through the secondary series inductor is identified as a load-independent synchronization variable with a fixed phase relationship to the primary excitation. A fixed-frequency second order generalized (SOGI)-based phase locked loop (PLL) is used to estimate the switching phase and frequency, and a phase-shift-based output power controller is developed using a phasor-transformer model of the active bridges. A modified control formulation is further introduced to remove phase-dependent loop-gain variation and simplify controller design. Simulation results validate the proposed method under both fixed and varying switching-frequency conditions, demonstrating successful frequency tracking from 82 kHz to 88 kHz and accurate output power regulation over a wide operating range. The proposed method offers a compact and effective solution for high-power IPT systems by enabling secondary-side synchronization and control without wireless communication or an additional DC-DC conversion stage.

Fernandes, Arnold Anthony [ORNL] (ORCID:0009000631↗

DOE OSTI2025

Isoconversional Kinetic Analysis of Oxygen Desorption for Sr 0.75 Ca 0.25 FeO 3-δ Perovskite in Dry and Steam-Based Environments

The desorption kinetics of Sr 0.75 Ca 0.25 FeO 3-δ perovskite material are examined in both dry and steam-based environments using redox gaseous products of a laboratory-scale fixed bed. First, desorption kinetics associated with the dry environment are proposed based on Friedman’s isoconversional method. It is found from the reconstructed reaction model that the desorption kinetics of this perovskite are controlled by a three-step mechanism, where hypothetically, the phase-boundary reaction rapidly prevails first, followed by diffusion and/or nucleation, and finally, unimolecular decay or random nucleation-controlled reaction. Subsequent analyses of the Arrhenius parameters indicated that both the early and later desorption stages, respectively, controlled by the phase boundary and unimolecular/random nucleation reactions, are associated with low energetic demand. In contrast, a high energetic penalty is required for the diffusion/nucleation reaction. Further, a satisfactory a priori verification of the proposed kinetics suggested that the three-step reaction mechanism reasonably describes the desorption of this perovskite in a dry environment. With the presence of steam in the desorption environment, oxygen production is substantially inhibited, but the hypothetical three-step mechanism is still found to control the desorption. Meanwhile, the primary effects of the steam on these mechanisms include the widening of the conversion extent associated with the diffusion/nucleation reaction, along with a higher energetic demand compared to the dry environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Universal Relationship between Limiting Current and Electrochemical Transport Properties in Malonate-Based Polymer Electrolytes

There is considerable interest in developing high-performance electrolytes for rechargeable lithium batteries. For practical applications, the electrolyte must support large dc currents. However, the parameters most often reported in the literature, conductivity, κ, and current fraction, ρ + , reflect ion transport in the limit of infinitesimal currents. In this limit, the efficacy of an electrolyte is given by the product κρ + . The limiting current density, i lim , is the maximum current density that can be applied across an electrolyte; the cell voltage diverges if the applied current density exceeds ilim. This parameter reflects ion transport in the limit of large dc currents and is therefore of practical interest. It would therefore be convenient if i lim could be predicted from measurements of κρ + . In order to explore this possibility, we studied six malonate-based polymers and PEO at a fixed salt concentration (r = 0.08) and temperature (90°C) using symmetric cells with planar electrodes. Unfortunately, there is no correlation between ilim and κρ + . When the applied current density, i, is less than ilim, the cell voltage approaches a stable plateau, ϕ plateau . Here, we found a linear dependence between i and thickness-normalized plateau potential, ϕ plateau L –1 , irrespective of the magnitude of the applied current. In all seven polymer electrolytes, we found a linear correlation between ilim and the slopes of these lines, σ. In other words, measurements of σ can be used to predict the limiting current.

Jana, Rounak [Lawrence Berkeley National Laborator↗

DOE OSTI2026

Heat metrics and thresholds reshape population exposure and inequality signals

Extreme heat is intensifying worldwide, yet estimates of heat hazard and exposure inequality depend on both the heat metric and how extreme days are defined. Using summer 2022 across the Mediterranean, we quantify population heat exposure with four metrics—land surface temperature (LST), air temperature (Ta), heat index (HI), and wet-bulb globe temperature (WBGT)—under absolute (fixed-value) and relative (anomaly-based) thresholds. Under absolute thresholds, total heat exposure differs by more than two orders of magnitude across metrics (31.3 billion person-days for Ta vs 0.3 billion for HI). Geographic hotspots also diverge: WBGT concentrates in humid coastal North Africa (e.g. the Nile Delta), whereas Ta and LST are more widespread. Under relative thresholds, exposure totals converge and cross-metric hotspot agreement increases (e.g. Ta–WBGT top-tercile overlap increases from 10.7% to 29.0%), shifting hotspots toward densely populated southern Europe. Crucially, the exposure–deprivation relationship also reverses across threshold frameworks: absolute thresholds concentrate exposure in more deprived North Africa and the Middle East, whereas relative thresholds shift the burden toward less-deprived European cities. This sensitivity is decision-relevant: city rankings based on WBGT exposure duration are almost completely reordered when switching threshold frameworks. Threshold choice therefore systematically reshapes hotspot patterns and inequality signals. Reporting both absolute and relative exposures can reveal hidden hotspots and support more targeted heat-risk monitoring and intervention planning.

Mediterranean↗

DOE OSTI2025

Impact of Resonator-Assisted ZZ Cancellation on Cross-Resonance Gate Performance

Strong coupling in superconducting processors enables fast two-qubit gates but also produces static ZZ interactions that degrade performance. Flux-tunable couplers can suppress ZZ but introduce flux noise and additional hardware complexity. A driven-resonator RIP interaction offers a simple method to dynamically cancel ZZ [1]. Using this RIP-based cancellation scheme in a fixed-frequency transmon system, we compare cross-resonance gate behavior with and without ZZ suppression. Idling errors improve substantially when ZZ is cancelled, while CR calibration reveals clear tradeoffs in Hamiltonian composition and achievable gate speed. [1]: Huang, Z. et al. (2024). Physical Review Applied, 22(3), 034007.

Heidler, Paul [Fermilab]↗

DOE OSTI2026

CTRL-STEER: Closed-Loop Neuron Activation Control in Vision-Language-Action Models

Vision-Language-Action (VLA) models enable test-time behavioral steering via neuron-level interventions, but existing methods use fixed strengths and operate in open loop. This static modulation fails under evolving task dynamics, leading to overcorrection, oscillations, and reduced task success—especially for temporal attributes like speed. We propose CTRL-STEER, a control-theoretic framework that casts activation steering as closed-loop feedback with adaptive, time-varying interventions. Instead of assuming neurons encode temporal concepts, we steer along motion-aligned residual directions and regulate intervention magnitude via feedback. We instantiate this with both PID and reinforcement learning controllers that jointly optimize concept adherence and task success. Experiments on fine-tuned OpenVLA policies across four LIBERO suites show improved stability and a better steering–success trade-off over fixed-coefficient baselines, without retraining the base model.

Babu, Abhijith [Florida International University, ↗

DOE OSTI2025

Cell-Free-Based Thermophilic Biocatalyst for the Synthesis of Amino Acids from One-Carbon Feedstocks

Bioproduction from one-carbon compounds, such as formate, is an attractive prospect due to reduced energy requirements and the possibility for using CO 2 as a sustainable feedstock. Formate-fixing pathways engineered using Escherichia coli lysate-based cell-free expression (CFE) biocatalysts have the potential to route 100% of feedstock carbon toward chemical synthesis but are undermined by siphoning of in-pathway metabolites and cofactors by the CFE background metabolism. To address this limitation, we engineer a CFE-based thermophilic multienzyme biocatalyst for the synthesis of serine and glycine from formate, bicarbonate, and ammonia. After expression of the thermophilic formate-to-serine pathway in a one-pot reaction, the mesophilic E. coli CFE background machinery is removed by simple heat denaturation, eliminating the siphoning of cofactors, inpathway metabolites, and products. After bioprocess optimization, including pathway gene expression duration and chemical synthesis temperature, we achieve near stoichiometric conversion of formate and bicarbonate to serine and glycine, reaching 97% of stoichiometric yield. The use of a moderately thermophilic biocatalyst allowed chemical synthesis to take place at mesophilic temperatures, enabling the balance of optimal enzyme activity with minimal metabolite/cofactor thermal degradation. In a fed-batch experiment, the biocatalyst shows sustained chemical synthesis rates for 8 h, paving the way toward a continuous bioprocess. Finally, a sensitivity analysis of cofactor usage revealed that the most expensive cofactors, THF and NADPH, can be reduced by 5-fold without significantly lowering product yields. To the best of our knowledge, this is the first instance of expressing a thermophilic pathway in an E. coli lysate-based CFE system to generate a thermophilic biocatalyst for use at mesophilic temperatures. The CFEbased thermophilic formate-to-serine biocatalyst triples the combined serine and glycine yield previously obtained by a CFE-based mesophilic formate-to-serine biocatalyst (30%), and quadruple the yield obtained by a purified enzyme system (22%). Ultimately, this work opens the door to using E. coli lysate-based CFE for thermophilic biocatalyst generation to achieve high chemical synthesis yields.

bacteria↗

DOE OSTI2025

Power System Feature-Based Event Classification by Means of Multiple PMU Data

Abstract—Phasor Measurement Units (PMUs) provide time synchronized measurements across the power grid, enabling data driven event detection and classification for enhanced system monitoring and situational awareness. However, variations in event duration, spatial extent, and severity, along with coincident events, pose challenges for conventional classification models that require fixed-size inputs. This paper presents a feature-based framework that aggregates diverse attributes from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, and Multilayer Perceptron. A probabilistic post-processing scheme is further introduced to enable multi-label classification in the presence of overlapping events. Experiments using real-world PMU data demonstrate that the Random Forest model achieves 95% accuracy, while the proposed post-processing method yields an additional 3% improvement.

Nematirad, Reza↗

DOE OSTI2025

Measurements and interpretations of W ± Z production cross-sections in pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Measurements of integrated and differential cross-sections for W ± Z production in proton-proton collisions are presented. The data collected by the ATLAS detector at the Large Hadron Collider from 2015 to 2018 at a centre-of-mass energy of $\sqrt{s}=13$ TeV are used, corresponding to an integrated luminosity of 140 fb −1 . The W ± Z candidate events are reconstructed using leptonic decay modes of the gauge bosons into electrons or muons. The integrated cross-section per lepton flavour for the production of W ± Z is measured in the detector fiducial region with a relative precision of 4%. The measured value is compared with the Standard Model prediction at a precision of up to next-to-next-to-leading-order in QCD and next-to-leading-order in electroweak. Cross-sections for W + Z and W − Z production and their ratio are presented. The W ± Z production is also measured differentially as functions of various kinematic variables, including new observables sensitive to CP-violation effects. All measurements are compared with state-of-the-art Standard Model predictions from fixed-order calculations or Monte Carlo generators based on next-to-leading-order matrix elements interfaced with parton showers. An effective field theory interpretation of the measurements is performed, considering both CP-conserving and CP-violating dimension-6 operators modifying the W ± Z production. In the absence of observed deviations from the Standard Model, limits on CP-conserving Wilson coefficients are extracted using the transverse mass of the W ± Z system. For CP-violating coefficients a machine learning approach is used to construct an observable with enhanced sensitivity to CP-violation effects.

hadron-hadron scattering↗

DOE OSTI2025

FLAMES─Fast, Low-Storage, Accurate, and Memory-Efficient Adaptive Sampling─Approach to Resolve Spatially Dependent Dynamics of Molecular Liquids

Many critical phenomena in soft matter occur at large length scales, necessitating the resolution of their structure and dynamics at low wavenumbers. However, resolving wavenumber-dependent dynamics computationally via molecular dynamics simulations presents significant challenges, as these phenomena span several orders of magnitude in both time and length scales, resulting in high computational costs and memory demands. Here, this work highlights the computational and memory challenges associated with analyzing molecular trajectories in reciprocal space and demonstrates a method to address them. We introduce FLAMESFast, Low-storage, Accurate, and Memory-Efficient adaptive Sampling, which is a direct method for calculation of structure factors, allowing us to select only the required number of wavevectors for binning. We also use wavenumber-dependent time steps to extract dynamics. Our FLAMES approach effectively mitigates computational and memory/storage bottlenecks. We demonstrate the method using simulations of a model system, liquid octane, at various temperatures. Comparisons with experimental data and real space computation show that the FLAMES technique achieves high accuracy in resolving temperature- and spatially dependent dynamics while being significantly more computationally efficient and requiring less memory and storage than methods based on a uniform wavevector grid and fixed temporal spacing.

Chen, Guang [Argonne National Laboratory (ANL), Ar↗