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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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89 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Search for $t\bar{t}H/A \rightarrow t\bar{t}t\bar{t}$ production in proton–proton collisions at $\sqrt{s}=13$ $\text {TeV}$ with the ATLAS detector

A search is presented for a heavy scalar (H) or pseudo-scalar (A) predicted by the two-Higgs-doublet models, where the H/A is produced in association with a top-quark pair $(t\bar{t}H/A),$ and with the H/A decaying into a $t\bar{t}$ pair. The full LHC Run 2 proton–proton collision data collected by the ATLAS experiment is used, corresponding to an integrated luminosity of $139~\text {fb}^{-1}.$ Events are selected requiring exactly one or two opposite-charge electrons or muons. Data-driven corrections are applied to improve the modelling of the $t\bar{t}$ +jets background in the regime with high jet and b-jet multiplicities. These include a novel multi-dimensional kinematic reweighting based on a neural network trained using data and simulations. An H/A-mass parameterised graph neural network is trained to optimise the signal-to-background discrimination. In combination with the previous search performed by the ATLAS Collaboration in the multilepton final state, the observed upper limits on the $t\bar{t}H/A \rightarrow t\bar{t}t\bar{t}$ production cross-section at 95% confidence level range between 14 fb and 5.0 fb for an H/A with mass between 400 GeV and 1000 GeV , respectively. Assuming that both the H and A contribute to the $t\bar{t}t\bar{t}$ cross-section, tan β values below 1.7 or 0.7 are excluded for a mass of 400 GeV or 1000 GeV , respectively. The results are also used to constrain a model predicting the pair production of a colour-octet scalar, with the scalar decaying into a $t\bar{t}$ pair.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Variational Path Sampling of Rare Dynamical Events

This article reviews the concepts and methods of variational path sampling. These methods allow computational studies of rare events in systems driven arbitrarily far from equilibrium. Based upon a statistical mechanics of trajectory space and leveraging the theory of large deviations, they provide a perspective from which dynamical phenomena can be studied with the same types of ensemble reweighting ideas that have been used for static equilibrium properties. Applications to chemical, material, and biophysical systems are highlighted.

Singh, Aditya N↗
DOE OSTI2021

Removal of correlated background in a high-order harmonic transient absorption spectra with principal component regression

We demonstrate a 40x mean noise power reduction (NPR) in core-to-valence extreme ultraviolet (XUV) femtosecond transient absorption spectroscopy with a high harmonic generation (HHG) light source. An adaptive iteratively reweighted principal component regression (airPCR) is used to analyze and suppress spectrally correlated HHG intensity fluctuations. The technique requires significantly less user input and leads to a higher mean NPR than a previously introduced edge-pixel PCR method that relies on the manual identification of signal-free spectral regions. Both techniques are applied in a time-resolved XUV absorption study of the 2 snp 1 P o ( n ≥ 2) autoionizing Rydberg states of helium, demonstrating sub-10 −3 optical density sensitivity.

Faccialà, Davide (ORCID:0000000250720394)↗
DOE OSTI2024

Updates to the NuMI Flux Simulation at MicroBooNE (V.1.0)

The NuMI flux prediction being used by all experiments at Fermilab that are sensitive to the NuMI beam is based on Geant v4.9.2.03 (G4.9) and uses the default FTFP-BERT physics list with no custom cross sections. In order to correct these predictions to match world data, PPFX, the Package to Predict the FluX, is used to reweight the flux prediction. In kinematic regions of overlap between the prediction and data, mainly NA49 measurements are used to correct for various hadron production processes. Specifcally, PPFX looks at the entire ancestry chain of the neutrino, up to the incident 120 GeV proton beam, and applies corrections for various hadron interactions based on said data. These corrections are implemented as a function of the Feynman-x (x F ) and transverse momentum (p T ) of the incident hadron.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

MEC modeling

2p-2h interactions are crucial for describing ν-Ar scattering, especially in the "dip region" between the Quasi-Elastic (QE) peak and Δ-resonance production. They involve the ejection of two nucleons, leading to two holes in the nuclear ground state. The underlying nuclear dynamics involve short-range correlations (SRC), long-range correlations (Random Phase Approximation, RPA), and the interplay of one- and two-body currents. Several models attempt to describe 2p-2h interactions like Valencia and SuSAv2-MEC. Despite progress, discrepancies exist between models and with data. I Will give an overview of 2p2h from the perspective of two Models Valencia and SuSAv2-MEC. I will introduce my current work to apply reweighting from SuSAv2 to Valencia on argon which will help improving the systematic uncertainty on MEC for SBN and DUNE.

Hassinin, Karim [Houston U.] (ORCID:00090009778353↗
DOE OSTI2026

Transforming the $v$ World: A New Multivariate Transformer Energy Estimator for NOvA

The NOvA Transformer Energy Estimator (Transformer_EE) is a universal machine learning tool currently used to infer the incoming beam neutrino energy and the outgoing lepton energy in both near andfar detectors. It uses a unique, highly flexible framework for simultaneous multivariate prediction that supports many possible loss functions. A spectral reweighting and flattening scheme lessens training bias. A feature noising subroutine enables adversarial-like training, mitigating sensitivities to certain systematic effects at marginal resolution loss at inference time. The state of the Transformer_EE will be reviewed, and its robustness with respect to several NOvA Near and Far Detector systematics highlighted.

Tong, Leon [Minnesota U.] (ORCID:0000000231625965)↗
DOE OSTI2026

Pre-Equilibrium De-Excitations in Neutrino-Nucleus Interactions

Pre-equilibrium de-excitation is a well-established stage of nucleon-induced nuclear reactions but has not previously been incorporated into neutrino event generators. This work implements the Koning-Duijvestijn exciton model within the MARLEY neutrino event generator to simulate energy redistribution and particle emission before compound nucleus equilibrium. The implementation includes modular calculations of particle-hole state densities, internal transition rates, emission rates, and Monte Carlo cascade sampling, and is validated against the TALYS-2.2 nuclear reaction code. A new event record is also proposed to enable future uncertainty reweighting studies. This work establishes the first framework for studying pre-equilibrium effects in neutrino-induced reactions, with the goal of improving predictions of particle multiplicities, γ-ray production, and MeV-scale detector signals relevant to experiments such as DUNE.

Visser, Erin [Michigan State U., East Lansing (mai↗
DOE OSTI2023

The Pantheon+ Analysis: Forward Modeling the Dust and Intrinsic Color Distributions of Type Ia Supernovae, and Quantifying Their Impact on Cosmological Inferences

Abstract Recent studies have shown that the observed color distributions of Type Ia supernovae (SNe Ia) can be well described by a combination of a dust distribution and an intrinsic color distribution. Using the Pantheon+ sample of 1701 SN Ia, we apply a new forward-modeling fitting method (Dust2Dust) to measure the parent dust and color distributions, including their dependence on host-galaxy mass. At each fit step, the SN Ia selection efficiency is determined from a large simulated sample that is reweighted to reflect the proposed distributions. We use five separate metrics to describe the goodness of fit: distribution of fitted light-curve color c , cosmological residual trends with c , cosmological residual scatter with c , fitted color–luminosity relationship β SALT2 , and intrinsic scatter σ int . We present the results and the uncertainty in 12-dimensional space. Furthermore, we measure that the uncertainty on this modeling propagates to an upper threshold uncertainty in the equation of state of dark energy w of 0.014(1) for the Pantheon+ cosmology analysis and contributes negligible uncertainty to the Hubble constant H 0 . The Dust2Dust code is made publicly available at https://github.com/djbrout/dustdriver .

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1992

Multivariable frequency domain identification via 2-norm minimization

The author develops a computational approach to multivariable frequency domain identification, based on 2-norm minimization. In particular, a Gauss-Newton (GN) iteration is developed to minimize the 2-norm of the error between frequency domain data and a matrix fraction transfer function estimate. To improve the global performance of the optimization algorithm, the GN iteration is initialized using the solution to a particular sequentially reweighted least squares problem, denoted as the SK iteration. The least squares problems which arise from both the SK and GN iterations are shown to involve sparse matrices with identical block structure. A sparse matrix QR factorization method is developed to exploit the special block structure, and to efficiently compute the least squares solution. A numerical example involving the identification of a multiple-input multiple-output (MIMO) plant having 286 unknown parameters is given to illustrate the effectiveness of the algorithm.

Bayard, David S.↗
NASA NTRS1999

Yaw and pitch visual-vestibular interaction in weightlessness

Both yaw and pitch visual-vestibular interactions at two separate frequencies of chair rotation (0.2 and 0.8 Hz) in combination with a single velocity of optokinetic stimulus (36 degrees/s) were used to investigate the effects of sustained weightlessness on neural strategies adopted by astronaut subjects to cope with the stimulus rearrangement of spaceflight. Pitch and yaw oscillation in darkness at 0.2 and 0.8 Hz without optokinetic stimulation, and constant velocity linear optokinetic stimulation at 18, 36, and 54 degrees/s presented relative to the head with the subject stationary, were used as controls for the visual-vestibular interactions. The results following 8 days of space flight showed no significant changes in: (1) either the horizontal and vertical vestibulo-ocular reflex (VOR) gain, phase, or bias; (2) the yaw visual-vestibular response (VVR); or (3) the horizontal or vertical optokinetic (OKN) slow phase velocity (SPV). However, significant changes were observed: (1) when during pitch VVR at 0.2 Hz late inflight, the contribution of the optokinetic input to the combined oculomotor response was smaller than during the stationary OKN SPV measurements, followed by an increased contribution during the immediate postflight testing; and (2) when during pitch VVR at 0.8 Hz, the component of the combined oculomotor response due to the underlying vertical VOR was more efficiently suppressed early inflight and less suppressed immediately postflight compared with preflight observations. The larger OKN response during pitch VVR at 0.2 Hz and the better suppression of VOR during pitch VVR at 0.8 Hz postflight are presumably due to the increased role of vision early inflight and immediately after spaceflight, as previously observed in various studies. These results suggest that the subjects adopted a neural strategy to structure their spatial orientation in weightlessness by reweighting visual, otolith, and perhaps tactile/somatic signals.

NASA Center JSC↗
NASA NTRS2011

Feature Acquisition with Imbalanced Training Data

This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.

Thompson, David R.↗
NASA NTRS2018

Global High Resolution Crustal Magnetic Field at the Surface of the Moon from Low-Altitude Lunar Prospector Magnetic Gradient Data

We derived new vector gradients based models of crustal magnetic field at the lunar surface with data from the Lunar Prospector (LP) satellite using two model parameterization approaches: a global set of 35820 1° spaced (~30 km) equal area monopoles at 20 km below the surface (O’Brien and Parker, 1994; Olsen et al., 2017) and combined results of subsets of 100000 0.66° spaced monopoles at the same depth. We use the scheme of iteratively reweighted least-squares inversion to compute the initial model. Then the amplitudes of these monopoles are determined by minimizing the misfit to the components together with the global average of |Br| at the ellipsoid surface (i.e. applying a L1 model regularization of Br). In previous approaches using vector fields for modeling, we found that external field contamination leads to spurious anomalies in the downward continued field models even with stringent data selection criteria and ad-hoc noise removal techniques (e.g., satellite’s position in the Moon’s wake w.r.t. the solar wind and in the Earth’s magnetotail, internal/external dipoles fields removal, low-order polynomial removal, joint equivalent source cross-validation technique and visually removing remaining anomalous segments). On the other hand, with the use of gradients-only data (along-track first finite differences), we were able to completely bypass the ad-hoc techniques. Similar processing of Kaguya magnetic data, which have only higher altitude coverage over the most of the Moon except in the region of the South Pole–Aitken (SPA) basin, completely misses some of the anomalies seen in the Lunar Prospector data. The combined Lunar Prospector and Kaguya gradient-based models also severely degrade the derivation of the anomaly fields in many regions. Euler analysis of isolated anomaly features from the Reiner Gamma swirl suggests top depths of about 0.3 to 1.5 km and center depths of 10-14 km; in the region between Stein and Vallier craters north of the SPA basin our analysis suggests top depths of around 1.5 km and center depths of 13-15 km. With the spectral depth determination techniques, the SPA basin region yields depths to the base of magnetization ranging between 15 and 40 km. Three-dimensional modeling and the bulk magnetization determinations of the sources constrained by the Euler and spectral methods is underway.

Gradient↗
DOE OSTI2020

Temporal Analysis and Scene Change Detection in Multispectral Overhead Imagery

Scene change detection can be a tedious and time consuming process especially when concerning large geographical areas, and the process can be even more cumbersome when analyzing changes in an area over large spans of time. Developing a useful way to help analysts recognize at what points in time significant changes to a scene have occurred can allow them to better focus their efforts in characterizing events. Applications include: Facility monitoring, Construction chronology, Monitoring of vehicle/aircraft activity, Characterization of larger sequences of events. In large areas exceeding hundreds to thousands of square kilometers in size, it can be difficult localizing when scene changes have occurred. Analysts can spend hours going through imagery to try to identify new construction, monitor facility activities, monitor vehicle movement, etc. where the object of interest may only be a few square meters. Our goal is to help cut down this time by giving analysts change maps with hot spots of change, allowing them to focus on regions that have experienced actual change in time frames they're interested in. Additionally, by combining these change maps into layers within a data cube, analysts can examine the change maps from a temporal perspective, allowing events to be characterized over spans of time. By opening the data cube in an imaging software capable of separating the layers, we can analyze the change maps sequentially, allowing us to examine scene changes occurring over time. As an example, we examined overhead imagery from Planet Labs of what appears to be a parking lot on Fort Irwin over the course of a year using ENVI, a geospatial satellite imagery analysis software. Using ENVI, we generate a graph of changes over time, and notice a particular segment near the end of our analysis window where no changes are detected. Examination of the actual satellite imagery reveals that during this time span, the parking lot was empty. This could be due to facility shutdown for maintenance or upgrades, or possibly even total workforce/vehicle fleet movement. Information like this could help analysts better characterize events, as well as to help create clearer timelines in larger sequences of events. Workflow steps: - Collect multiple maps of the same AOI (Area of Interest) during a time span of interest; - Generate change maps from AOI maps; - Generate data cube from change maps. An analyst can use the data cube to help inspect an AOI for activities within a time span of interest. If an event of interest is discovered, the analyst can then refer to the maps corresponding to the appropriate dates and times in the data cube to see precisely what is transpiring. The biggest objective being worked on is improving the change detection methodology employed. We currently use PCA-EM (Principal Component Analysis with Expectation Maximization), but we are currently focusing on implementing IR-MAD (Iteratively Reweighted Multivariate Alteration Detection) to be used in conjunction with PCA-EM in an effort to decrease false positivity and noise in the change maps we generate.

42 ENGINEERING↗
DOE OSTI2024

Rejection Sampling with Autodifferentiation -- Case study: Fitting a Hadronization Model

We present an autodifferentiable rejection sampling algorithm termed Rejection Sampling with Autodifferentiation (RSA). In conjunction with reweighting, we show that RSA can be used for efficient parameter estimation and model exploration. Additionally, this approach facilitates the use of unbinned machine-learning-based observables, allowing for more precise, data-driven fits. To showcase these capabilities, we apply an RSA-based parameter fit to a simplified hadronization model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Rare Lepton Decays and Differentiable Hadronization Models - From Signatures of New Physics to Data-driven Event Generation

This dissertation is partitioned into two parts: phenomenological studies focused on rare lepton decays as probes of heavy and light new physics, and the development of differentiable, data-driven hadronization models. Part I develops the phenomenology of new physics signatures stemming from rare charged lepton flavor violating decays probed by experiments at the intensity frontier. These include interactions mediated by both high-scale effective operators and light new physics, manifesting in multi-lepton final states ($\mu \to 5e$), elastic nuclear transitions ($\mu \to e$ conversion), baryon-number-violating muon capture, and time-dependent signals from ultralight dark matter ($\mu \to e \phi, \tau \to \ell \phi$). Part II develops two distinct strategies for advancing differentiable and data-driven hadronization models. One involves comprehensive reweighting frameworks for hadronization that enable efficient uncertainty estimation, facilitate parameter tuning, and interface naturally with differentiable programming paradigms. The other introduces machine-learning-based methods for extracting microscopic fragmentation dynamics directly from macroscopic observables through the deformation of existing models -- effectively providing solutions to the inverse problem of hadronization. Altogether, these studies advance the interpretability, flexibility, and precision of theoretical predictions for both high-intensity and high-energy experiments.

Menzo, Tony [Cincinnati U.] (ORCID:000000022013457↗
DOE OSTI2026

QuadSync: Quadrifocal tensor synchronization via Tucker decomposition

In structure from motion, quadrifocal tensors capture more information than their pairwise counterparts (essential matrices), yet they have often been thought of as impractical and only of theoretical interest. In this work, we challenge such beliefs by providing a new framework to recover n cameras from the corresponding collection of quadrifocal tensors. We form the block quadrifocal tensor and show that it admits a Tucker decomposition whose factor matrices are the stacked camera matrices, and which thus has a multilinear rank of (4,4,4,4) independent of n. We develop the first synchronization algorithm for quadrifocal tensors, using Tucker decomposition, alternating direction method of multipliers, and iteratively reweighted least squares. We further establish relationships between the block quadrifocal, trifocal, and bifocal tensors, and introduce an algorithm that jointly synchronizes these three entities. Numerical experiments demonstrate the effectiveness of our methods on modern datasets, indicating the potential and importance of using higher-order information in synchronization.

Miao, Daniel [University of Minnesota]↗
DOE OSTI2026

Pre-Equilibrium De-Excitations for Neutrino-Nucleus Interactions

The Deep Underground Neutrino Experiment (DUNE) is sensitive to MeV-scale energy depositions from low-energy astrophysical neutrinos, including those from core-collapse supernovae. Interpreting these detector signals requires accurate modeling of the nuclear de-excitation that follows from the neutrino-nucleus interaction. The MARLEY (Model of Argon Reaction Low-Energy Yields) event generator specializes in the low-energy regime. MARLEY currently assumes the residual nucleus equilibrates immediately after the primary interaction. This omits the intermediate pre-equilibrium stage in which energy redistributes among nucleons until statistical equilibrium is reached. While pre-equilibrium effects are well established for nucleon-induced reactions, they have not previously been studied for neutrino-nucleus interactions. This work addresses that gap by implementing a two-component exciton model, which is the first dedicated treatment of pre-equilibrium de-excitation for neutrino-nucleus interactions, restructured around MARLEY's existing class hierarchy to prepare for direct integration, including particle-hole state densities, internal transition rates, and pre-equilibrium particle emission. The calculations show encouraging agreement with the TALYS-2.2 nuclear reaction code for neutron-nucleus interactions. We further propose a concrete integration path into the full MARLEY event generator, including derived class structure and an extended event record for pre-equilibrium vertices in support of future reweighting. Remaining work focuses on refining the emission width calculation, adding $\gamma$-ray emission, and completing this integration to quantify the impact of pre-equilibrium effects on the expected low-energy neutrino signals in DUNE and similar experiments.

Visser, Erin [Michigan State U., East Lansing (mai↗