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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Impact of QCD energy evolution on observables in heavy-ion collisions

Here, we study how the inclusion of energy dependence as dictated by quantum chromodynamic (QCD) small-x evolution equations affects key observables in ultra-relativistic heavy-ion collisions. Specifically, we incorporate JIMWLK evolution into the IP-Glasma framework, which serves as the initial condition for a simulation pipeline that includes viscous relativistic hydrodynamics and a hadronic afterburner. This approach enables a consistent modeling of highly energetic nuclei across varying Bjorken-x values, which are relevant for different collision energies and rapidity regions. In comparison to the standard IP-Glasma setup without small-x evolution, we observe pronounced changes in particle multiplicities and spectral distributions, especially in smaller systems and at the highest available energies. We further explore effects on anisotropic flow observables and correlations between mean transverse momentum and elliptic flow. Our findings underscore the critical role of nonlinear QCD evolution in accurately modeling the early stages of heavy-ion collisions, as well as its implications for extracting transport properties of the quark-gluon plasma.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Testing predictions of electron scale turbulent pedestal transport in two DIII-D ELMy H-modes

In this study, we present linear and nonlinear gyrokinetic analyses in the pedestal region of two DIII-D ELMy H-mode discharges using the CGYRO code. The otherwise matched discharges employ different divertor configurations to investigate the impact of varying recycling and particle source on pedestal profiles. Linear gyrokinetic simulations find electrostatic ion-scale instabilities (ion temperature gradient and trapped electron modes, ITG-TEM) are present just inside the top of the pedestal with growth rates that are enhanced significantly by parallel velocity shear. In the sharp gradient region, E x B shearing rates are comparable or larger than ion scale growth rates, suggesting the suppression of ITG-TEM modes in this region. Instead, the electron temperature profiles are found to be correlated with and just above the electron temperature gradient (ETG) instability thresholds. Using gradients varied within experimental uncertainties, nonlinear electron-scale gyrokinetic simulations predict electron heat fluxes from ETG turbulence, that when added to neoclassical ion thermal transport simulated by NEO, account for 30-60% of the total experimental heat flux. In addition, the neoclassical electron particle flux is found to contribute significantly to the experimental fluxes inferred from SOLPS-ITER analysis. Additional nonlinear gyrokinetic simulations are run varying input gradients to develop a threshold-based reduced model for ETG transport, finding a relatively simple dependence on η e = L ne /L Te . Predictive transport simulations are used to validate this pedestal-specific ETG model, in conjunction with a model for neoclassical particle transport. In both discharges, the predicted electron temperatures are always overpredicted, indicative of the insufficient stiffness in the ETG pedestal model to account for all of the experimental electron thermal transport. In the case of the closed divertor discharge with lower particle source, the predicted electron density is close to the experiment, consistent with the magnitude of neoclassical particle transport in that discharge. However, the density profiles are overpredicted in the open divertor discharge (larger particle source), due to insufficient model transport. In conclusion, the implications for other mechanisms accounting for the remainder of transport in the sharp gradient region in the two discharges are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Impact of n > 1 neoclassical tearing modes on fast ions, plasma rotation, and the onset of disruptive tearing modes in DIII-D ITER baseline scenario

We present integrated TRANSP-kick analyses quantitatively characterizing how n > 1 magnetic islands redistribute fast ions (FIs) and modify momentum balance in low-torque DIII-D H-mode scenarios operated with the ITER normalized parameter set and shape (ITER baseline scenario, or IBS). In this plasma scenario, disruptive neoclassical tearing modes (NTMs) are most commonly seeded by nonlinear 3-wave coupling when the differential rotation between the q = 1 and q = 2 surfaces ($δf_{1, 2}$) approaches zero. Analysis of a DIII-D IBS database of shots unstable to the 2/1 NTM demonstrates that the flattening rotation profile is correlated with n > 1 NTM amplitude growth, with a strong drop in $δf_{1, 2}$ occurring when the radial magnetic perturbation amplitude at the rational surface ($δB$ r s ) is estimated to be about 20 G. The interpretive TRANSP-kick simulations show that the experimentally observed reduction in core rotation is due to the fast-ion kicks. Nonlinearity in momentum losses versus NTM amplitude is found to be correlated with overlap of FI island chains, occurring when $δB$ r s is about 35 G, in rough agreement with the experimental results. This agreement suggests the primary mechanism for the conductive momentum loss in the core is due to chaotic FI orbits and FI losses developing from overlapping resonant FI island chains.

ITER baseline scenario↗
DOE OSTI2020

Human influence on joint changes in temperature, rainfall and continental aridity

Despite the pervasive impact of drought on human and natural systems, the large-scale mechanisms conducive to regional drying remain poorly understood. Here we use a multivariate approach to identify two distinct externally forced fingerprints from multiple ensembles of Earth system model simulations. The leading fingerprint, F M1 (x), is characterized by global warming, intensified wet–dry patterns and progressive large-scale continental aridification, largely driven by multidecadal increases in greenhouse gas (GHG) emissions. The second fingerprint, F M2 (x), captures a pronounced interhemispheric temperature contrast, associated meridional shifts in the intertropical convergence zone and correlated anomalies in precipitation and aridity over California, the Sahel and India. F M2 (x) exhibits nonlinear temporal behaviour: the intertropical convergence zone moves southwards before 1975 in response to increases in hemispherically asymmetric sulfate aerosol emissions, and it shifts northwards after 1975 due to reduced sulfur dioxide emissions and the GHG-induced warming of Northern Hemisphere landmasses. Both fingerprints are statistically identifiable in observations of joint changes in temperature, rainfall and aridity during 1950–2014. Furthermore, we show that the reliable simulation of these changes requires combined forcing by GHGs, direct and indirect effects of aerosols, and large volcanic eruptions. Our results suggest that GHG-induced aridification may be modulated regionally by future reductions in sulfate aerosol emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Uncovering extreme nonlinear dynamics in solids through time-domain field analysis

Time-domain analysis of harmonic fields with sub-cycle resolution is now experimentally viable due to the emergence of sensitive, on-chip techniques for petahertz-scale optical-field sampling. We demonstrate how such a time-domain, field-resolved analysis uncovers the extreme nonlinear electron dynamics responsible for high-harmonic generation within solids. Time-dependent density functional theory was used to simulate harmonic generation from a solid-state band-gap system driven by near- to mid-infrared waveforms. Particular attention was paid to regimes where both intraband and interband emission mechanisms play a critical role in shaping the nonlinear response. We show that a time-domain analysis of the harmonic radiation fields identifies the interplay between intra- and interband dynamical processes underlying the nonlinear light generation. With further analysis, we show that changes to the dominant emission regime can occur after only slight changes to the peak driving intensity and central driving wavelength. Furthermore, time-domain analysis of harmonic fields also reveals, for the first time, the possibility of rapid changes in the dominant emission mechanism within the temporal window of the driving pulse envelope. Finally, we examine the experimental viability of performing time-domain analysis of harmonic fields with sub-cycle resolution using realistic parameters.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Uncertainty analysis of correlated parameters in automated reaction mechanism generation

Abstract Uncertainty analysis is a useful tool for inspecting and improving detailed kinetic mechanisms because it can identify the greatest sources of model output error. Owing to the very nonlinear relationship between kinetic and thermodynamic parameters and computed concentrations, model predictions can be extremely sensitive to uncertainties in some parameters while uncertainties in other parameters can be irrelevant. Error propagation becomes even more convoluted in automatically generated kinetic models, where input uncertainties are correlated through kinetic rate rules and thermodynamic group values. Local and global uncertainty analyses were implemented and used to analyze error propagation in Reaction Mechanism Generator (RMG), an open‐source software for generating kinetic models. A framework for automatically assigning parameter uncertainties to estimated thermodynamics and kinetics was created, enabling tracking of correlated uncertainties. Local first‐order uncertainty propagation was implemented using sensitivities computed natively within RMG. Global uncertainty analysis was implemented using adaptive Smolyak pseudospectral approximations as implemented in the MIT Uncertainty Quantification Library to efficiently compute and construct polynomial chaos expansions to approximate the dependence of outputs on a subset of uncertain inputs. Cantera was used as a backend for simulating the reactor system in the global analysis. Analyses were performed for a phenyldodecane pyrolysis model. Local and global methods demonstrated similar trends; however, many uncertainties were significantly overestimated by the local analysis. Both local and global analyses show that correlated uncertainties based on kinetic rate rules and thermochemical groups drastically reduce a model's degrees of freedom and have a large impact on the determination of the most influential input parameters. These results highlight the necessity of incorporating uncertainty analysis in the mechanism generation workflow.

Gao, Connie W.↗
DOE OSTI2021

Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data

Image-like data from quantum systems promises to offer greater insight into the physics of correlated quantum matter. However, the traditional framework of condensed matter physics lacks principled approaches for analyzing such data. Machine learning models are a powerful theoretical tool for analyzing image-like data including many-body snapshots from quantum simulators. Recently, they have successfully distinguished between simulated snapshots that are indistinguishable from one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from such approaches. Here, we develop a set of nonlinearities for use in a neural network architecture that discovers features in the data which are directly interpretable in terms of physical observables. Applied to simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, we uncover that the key distinguishing features are fourth-order spin-charge correlators. Our approach lends itself well to the construction of simple, versatile, end-to-end interpretable architectures, thus paving the way for new physical insights from machine learning studies of experimental and numerical data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A new route to obtain fluorescence X-ray absorption spectra of compounds and to remove the self-absorption induced nonlinearity in the spectra

A new route to obtain fluorescence X-ray absorption spectra of compounds and to remove the self-absorption induced nonlinearity in the spectra is described. The fluorescent intensity If is linearly proportional to the absorption coefficient μ. For studies of surface structures around an element (κ) the fluorescence detection is often the mode of choice. However, the measurement may suffer from a self-absorption (SA) effect which nonlinearly distorts the spectra. The effect is severe when κ is concentrated or the measurements are carried out in certain geometries. Here, the correlations among emission events in compounds are examined following resonance X-ray core-electron excitation within κ. Under conditions leading to SA, If emitted from κ apparently has a conjugated relationship with the fluorescent intensities simultaneously emitted from other elements (ξ). Normalizing the former (κ) by the latter (ξ) will largely remove SA effects and reduce this nonlinear problem to a tractable linear problem. This does result in a moderate reduction of the spectral amplitude due to the so-called secondary emission from ξ excited by the emission from κ. Nonetheless, the resulting spectra will allow one to accurately determine bond distances and disorder and, in some respects, can be superior to spectra obtained via the absorption channel. For μξ < μκ and grazing incidence geometry, the amplitude reduction can be small and simple normalization is sufficient to restore the spectral integrity with remarkable accuracy. This has been instrumental in unravelling the surface and subsurface structures around cations in amorphous Ga–In–O and Zn–Sn–O films which are otherwise inaccessible due to severe SA effects. This method has also been applied to several samples with μξ ≃ μκ to examine its applicability. For these samples, the amplitude reduction is 12 ± 4% versus their standards for the data measured with the classical 45°/45° geometry. This experimental method is easy to implement. Since If from κ and ξ are measured by the same detector system, it is also superior to other methods in removing systematic errors such as detector system nonlinearity, electronic noise, and some beam instabilities, and in removing spectral imperfections due to, for example, SA effects, diffraction effects and sample inhomogeneity. The distortions resulting from the latter can be severe in the spectra measured in transmission mode.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Active oscillatory associative memory

Traditionally, physical models of associative memory assume conditions of equilibrium. Here, we consider a prototypical oscillator model of associative memory and study how active noise sources that drive the system out of equilibrium, as well as nonlinearities in the interactions between the oscillators, affect the associative memory properties of the system. Our simulations show that pattern retrieval under active noise is more robust to the number of learned patterns and noise intensity than under passive noise. To understand this phenomenon, we analytically derive an effective energy correction due to the temporal correlations of active noise in the limit of short correlation decay time. We find that active noise deepens the energy wells corresponding to the patterns by strengthening the oscillator couplings, where the more nonlinear interactions are preferentially enhanced. Using replica theory, we demonstrate qualitative agreement between this effective picture and the retrieval simulations. Our work suggests that the nonlinearity in the oscillator couplings can improve memory under nonequilibrium conditions.

Chemistry↗
DOE OSTI2025

Description of di-hadron saturation signals within a universal nuclear parton distribution function approach

Di-hadron and di-jet correlation measurements in proton-nucleus (𝑝+𝐴) and electron-nucleus collisions are widely motivated as sensitive probes of novel, nonlinear quantum chromodynamices saturation dynamics in hadrons, which are particularly accessible in the dense nuclear environment at low values of Bjorken-𝑥 (𝑥 A ). Current measurements at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC) observe a significant suppression in the per-trigger yield at forward rapidities compared to that in proton-proton collisions, nominally consistent with the “mono-jet” production expected in a saturation scenario. However, the width of the azimuthal correlation remains unmodified, in contradiction to the qualitative expectations from this physics picture. I investigate whether the construction of these observables leaves them sensitive to effects from simple nuclear shadowing as captured by, for example, universal nuclear parton distribution function (nPDF) analyses. I find that modern nPDF sets, informed by recent precision measurements sensitive to the shadowing of low-𝑥 A gluon densities in LHC and other data, can describe all or the majority of the di-hadron/jet suppression effects in 𝑝+𝐴 data at both RHIC and the LHC, while giving a natural explanation for why the azimuthal correlation width is unmodified. Notably, this is achieved via a (𝑥 A ,𝑄 2 )-differential suppression of overall cross sections only, without requiring additional physics dynamics which alter the interevent correlations.

Jets & heavy flavor physics↗
DOE OSTI2020

Widening and distortion of the particle energy distribution by chromaticity in quasi-isochronous rings

This paper reports the observation of beam energy widening and distortion from Gaussian due to a nonvanishing horizontal chromaticity at a quasi-isochronous storage ring. The result originates from an average path-length dependence on the betatron oscillation amplitudes, which is intimately correlated to the transverse chromaticities. It is the first experimental validation of the impact of such a nonlinear transverse-longitudinal coupling effect on the equilibrium beam characteristics in a storage ring. The results could be important for quasi-isochronous rings, steady-state microbunching, nonscaling fixed-field alternate gradient accelerators, etc., where very small momentum compaction or large chromaticity is required.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Topology and geometry under the nonlinear electromagnetic spotlight

For many materials, a precise knowledge of their dispersion spectra is insufficient to predict their ordered phases and physical responses. Instead, these materials are classified by the geometrical and topological properties of their wavefunctions. A key challenge is to identify and implement experiments that probe or control these quantum properties. In this Review, we describe recent progress in this direction, focusing on nonlinear electromagnetic responses that arise directly from quantum geometry and topology. Furthermore, we give an overview of the field by discussing theoretical ideas, experiments and the materials that drive them. We conclude by discussing how these techniques can be combined with device architectures to uncover, probe and ultimately control quantum phases with emergent topological and correlated properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Theoretical and global simulation analysis of collisional microtearing modes

Microtearing modes (MTMs) are suggested as a candidate for anomalous thermal transport in tokamak H-mode discharges. This study investigates MTMs in tokamak plasmas, employing simulations in the BOUT++ framework. It simplifies and linearizes the governing equations in detailed linear simulations. The study meticulously evaluates various conductivity models under diverse plasma conditions and collision regimes. The research thoroughly assesses different conductivity models across a range of plasma conditions and collision regimes. A unified dispersion relation that includes both MTM and Drift-Alfvén Wave (DAW) instabilities is derived, showing that DAW and MTM instabilities occur at varying distances from the rational surface. Specifically, MTMs become unstable near the rational surface but stabilize farther away, while drift-Alfvén instability appears farther from the rational surface. The study also re-derives MTM dispersion relations using Ohm's law and the vorticity equation, providing a thorough analysis of electromagnetic and electrostatic interactions in tokamaks. Global simulations demonstrate an inverse correlation between MTM growth rates and collisionality, and a direct correlation with temperature gradients. The nonalignment of the rational surface with the peak ω*e stabilizes the MTMs. Nonlinear simulations highlight electron temperature relaxation as the primary saturation mechanism for MTMs, with magnetic flutter identified as the dominant mode of electron thermal transport.

Fan, K. (ORCID:0000000227518809)↗
DOE OSTI2025

Exploring entanglement and spectral split correlations in three-flavor collective neutrino oscillations

In environments with prodigious numbers of neutrinos, such as core-collapse supernovae, neutron star mergers, or the early Universe, neutrino-neutrino interactions are dynamically significant. They can dominate neutrino flavor evolution and force it to be nonlinear, causing collective neutrino oscillations. Such collective oscillations have been studied numerically, for systems with up to millions of neutrinos, using mean-field or one-particle effective approximations. However, such a system of interacting neutrinos is a quantum many-body system, wherein quantum correlations could play a significant role in the flavor evolution—thereby motivating the exploration of many-body treatments that follow the time evolution of these correlations. In many-body flavor evolution calculations with two neutrino flavors, the emergence of spectral splits in the neutrino energy distributions has been found to be correlated with the degree of quantum entanglement across the spectrum. In this work, for the first time, we investigate the emergence of spectral splits in the three-flavor many-body collective neutrino oscillations. We find that the emergence of spectral splits resembles the number and location found in the mean-field approximation but not in the width. Moreover, unlike in the two-flavor many-body calculations, we find that additional degrees of freedom make it more difficult to establish a correlation between the location of the spectral splits and the degree of quantum entanglement across the neutrino energy spectrum. The observation from the two-flavor case, that neutrinos nearest to the spectral split frequency exhibit the highest level of entanglement, is more difficult to ascertain in the three-flavor case because of the presence of multiple spectral splits across different pairwise combinations of flavor and/or mass states. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Machine learning models for estimating contamination across different curbside collection strategies

Contaminated recyclables, which are frequently discarded as waste, pose a significant challenge to the implementation of a circular economy. These contaminated recyclables impede the circulation of resources, resulting in higher processing costs at material recovery facilities (MRFs). Over the past few decades, machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and random forest (RF) have evolved to provide new methods for predicting inbound contamination rates in addition to traditional statistical models. In this study, we applied ML models to predict inbound contamination rates using demographic features from 15 counties in the U.S. with different curbside collection strategies. In general, we found that ML models outperformed linear mixed models. Specifically, SVM models had the highest performance (R 2 = 0.75; mean absolute error (MAE) = 0.06), which may be due to their ability to model nonlinear relationships between features and inbound contamination rates. Further, the key predictor was population, with poverty rate being positively correlated and median age negatively correlated with inbound contamination rates. To improve the management of contamination and enhance the implementation of a circular economy, better models are needed to understand and estimate inbound contamination rates as well as identify critical factors in the present and future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗
DOE OSTI2023

Multifunctional Fiber-Reinforced Polymer Composites for Damage Detection and Memory

Self-structural health monitoring (SHM) functionalities for fiber-reinforced polymer composites have become highly sought after to ensure the structural safety of newly advancing components in the automotive, civil, mechanical, and aerospace industries. This paper introduces a self-damage detection and memory (SDDM) hybrid composite material, where the structural carbon fiber tow is transformed into a piezoresistive sensor network, and the structural glass fiber operates as electrical insulation. In this study, SDDM specimens were fabricated, and tensile and impact tests were performed. The tensile tests of SDDM specimens find two distinct loading peaks: first where the carbon fiber fails, and second where the glass fiber fails. A linear correlation was observed between the carbon fiber resistance and composite strain up to a threshold, beyond which a sharp nonlinear increase in resistance occurred. The resistance then approached infinity, coinciding with the first loading peak and failure of the carbon fiber elements. This demonstrates the potential for a damage early warning threshold. Additionally, the effect of stitching the sensor tow in a zig-zag pattern over a large area was investigated using tailored fiber placement (TFP) of 1-loop, 3-loop, and 5-loop specimens. Tensile testing found that increasing the number of loops improved the sensor’s accuracy for strain sensing. Furthermore, impact tests were conducted, and as the impact energy progressively increased, the sensor resistance permanently increased. This illustrates a capability for self-memory of microdamage throughout the life cycle of the structure, potentially useful for predicting the remaining life of the composite.

Demo, Luke B. (ORCID:0000000208806522)↗
DOE OSTI2026

Bayesian Exploration and Surrogate Emulation of Nonlinear Beam-Response Geometry in the LBNF Beamline

Next-generation long-baseline neutrino experiments aim to achieve multi-MW proton beam power while reducing accelerator-induced systematic uncertainties. At Fermilab, the LBNF beamline is designed for 1.2 MW operation with PIP-II and is upgradeable to 2.4 MW. DUNE will probe the three-flavor neutrino paradigm and search for CP violation, requiring precise neutrino-flux normalization and improved control of accelerator-related uncertainties. Within the LBNF beamline, the System for On-Axis Neutrino Detection (SAND) will constrain flux uncertainties using precision near-detector measurements, while the Muon Monitor System (MuMS) will provide beamline diagnostics sensitive to the proton beam, target, and horn configuration. However, the pion phase space relevant for DUNE depends simultaneously on many correlated parameters, including beam centroid, beam width, horn current and alignment, target position, optics shifts, and radiation-induced changes. Consequently, MuMS observables exhibit nonlinear and coupled responses that are difficult to characterize using traditional one-parameter scans. To address this challenge, we are developing a Bayesian Exploration framework coupled to physics-informed surrogate emulators trained on Geant4 beamline simulations. Gaussian-process emulators provide both fast predictions and uncertainty estimates, enabling adaptive selection of new simulation points in beam-parameter space. As an initial demonstration, we construct surrogate emulators for MuMS response observables using a verified simulation campaign spanning proton-beam steering conditions. The emulators reproduce the simulated dependence of MuMS centroid and gradient observables while providing predictive uncertainties, and serve as the foundation for future multidimensional exploration including beam width, horn current, and additional beamline parameters. This work establishes a framework for uncertainty-aware beam monitoring, adaptive simulation campaigns, and rapid beam-response inference for future DUNE operations.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗