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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

First observation of strange baryon enhancement with effective energy in pp collisions at the LHC

The production of (multi-)strange hadrons is measured at midrapidity in proton-proton collisions at $\sqrt{s}$ = 13 TeV as a function of the local charged-particle multiplicity in the pseudorapidity interval |η| < 0.5 and of the very-forward energy measured by the ALICE Zero-Degree Calorimeters. The latter provides information on the effective energy, i.e. the energy available for particle production in the collision once subtracted from the centre-of-mass energy. The yields of K$^{0}_{s}$, $Λ$ + $\overline{Λ}$, and $Ξ$ – + $\overline{Ξ}$ + per charged-particle increase with the effective energy. In addition, this work exploits a multi-differential approach to decouple the roles of local multiplicity and effective energy in such an enhancement. The results presented in this article provide new insights into the interplay between global properties of the collision, such as the initial available energy in the event, and the locally produced final hadronic state, connected to the charged-particle multiplicity at midrapidity. Notably, a strong increase of strange baryon production with effective energy is observed for fixed charged-particle multiplicity at midrapidity. These results are discussed within the context of existing phenomenological models of hadronisation implemented in different tunes of the PYTHIA 8 event generator.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Probing lepton number violation and Majorana nature of neutrinos at the LHC

Abstract Observation of lepton number (L) violation by two units at colliders would provide evidence for the Majorana nature of neutrinos. We study signals ofL-violation in the context of two popular models of neutrino masses, the type-II seesaw model and the Zee model, wherein small neutrino masses arise at the tree-level and one-loop level, respectively. We focus onL-violation signals at the LHC arising through the processpp→ℓ ± ℓ ′± + jets within these frameworks. We obtain sensitivity toL-violation in the type-II seesaw model for triplet scalar masses up to 700 GeV and in the Zee model for charged scalar masses up to 4.8 TeV at the high-luminosity LHC with an integrated luminosity of 3 ab −1 .

Physics↗
DOE OSTI2024

Sterile neutrino dark matter within the ν SMEFT

Sterile neutrinos with masses at the keV scale and mixing to the active neutrinos offer an elegant explanation of the observed dark matter (DM) density. However, the very same mixing inevitably leads to radiative photon emission and the non-observation of such peaked X-ray lines rules out this minimal sterile neutrino DM hypothesis. We show that in the context of the Standard Model effective field theory with sterile neutrinos (νSMEFT), higher dimensional operators can produce sterile neutrino DM in a broad range of parameter space. In particular, νSMEFT interactions can open the large mixing parameter space due to their destructive interference, through operator mixing or matching, in the X-ray emission. We also find that, even in the zero mixing limit, the DM density can always be explained by νSMEFT operators. The testability of the studied νSMEFT operators in searches for electric dipole moments, neutrinoless double beta decay, and pion decay measurements is discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Search for long-lived particles decaying in the CMS muon detectors in proton-proton collisions at s = 13 TeV

A search for long-lived particles (LLPs) decaying in the CMS muon detectors is presented. A data sample of proton-proton collisions at s = 13 TeV corresponding to an integrated luminosity of 138 fb − 1 , recorded at the LHC in 2016–2018, is used. The decays of LLPs are reconstructed as high multiplicity clusters of hits in the muon detectors. In the context of twin Higgs models, the search is sensitive to LLP masses from 0.4 to 55 GeV and a broad range of LLP decay modes, including decays to hadrons, τ leptons, electrons, or photons. No excess of events above the standard model background is observed. The most stringent limits to date from LHC data are set on the branching fraction of the Higgs boson decay to a pair of LLPs with masses below 10 GeV. This search also provides the best limits for various intervals of LLP proper decay length and mass. Finally, this search sets the first limits at the LHC on a dark quantum chromodynamic sector whose particles couple to the Higgs boson through gluon, Higgs boson, photon, vector, and dark-photon portals, and is sensitive to branching fractions of the Higgs boson to dark quarks as low as 2 × 10 − 3 . © 2024 CERN, for the CMS Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Panorama of new-physics explanations to the MiniBooNE excess

The MiniBooNE low-energy excess stands as an unexplained anomaly in short-baseline neutrino oscillation experiments. It has been shown that it can be explained in the context of dark sector models. Here, we provide an overview of the possible new-physics solutions based on electron, photon, and dilepton final states. We systematically discuss the various production mechanisms for dark particles in neutrino-nucleus scattering. Our main result is a comprehensive fit to the MiniBooNE energy spectrum in the parameter space of dark neutrino models, where short-lived heavy neutral leptons are produced in neutrino interactions and decay to e + e - pairs inside the detector. For the first time, other experiments will be able to directly confirm or rule out dark neutrino interpretations of the MiniBooNE low-energy excess.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

SIGHT: Stacked Integration of Geospatial Hierarchical Typologies for Inferring Building Characteristics

Building characteristics are often absent in building stock datasets, particularly in regions most vulnerable to climate change and requiring effective disaster management strategies. Traditional machine learning approaches, while widely used to predict building attributes, typically neglect the spatial context of the data, leading to less accurate and reliable outcomes. To address these challenges, this paper introduces a novel algorithm, the Stacked Integration of Geospatial Hierarchical Typologies. This algorithm adapts a meta-learning framework to incorporate geospatial context into the predictive modeling process. We demonstrate the utility of the algorithm through two primary use cases: building use type classification and building height prediction. The algorithm consistently achieved or exceeded a 0.94 macro average F1 score across five geographically distinct countries for building use type classification. For building height prediction, it accurately predicted heights with a root mean square error of 3.01 in a comprehensive study using roughly 3.6 million buildings in Japan. These results underscore the benefits of integrating spatial hierarchies into machine learning models, enhancing both predictive accuracy and reliability in geospatial modeling. This work introduces a new algorithm to address the pervasive data sparsity issue in existing building stock datasets.

Adams, Daniel [ORNL] (ORCID:0000000196950577)↗
DOE OSTI2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗
DOE OSTI2024

Siting Lab [SWR-24-95]

Siting Lab contains a collection of user-friendly tutorials and guides for working with the Siting Lab (https://data.openei.org/siting_lab) data within the context of the reV model. The python code examples demonstrate the creation and transformation of Siting Lab data into reV compliant format as well as working with the reV model inputs and outputs. Specifically, Siting Lab provides a collection of Jupyter Notebooks that serve as guides for working with data from NREL's spatial analysis portfolio. These notebooks teach users how to create and interact with reV data in order to facilitate external use of the model. The guides in this repository reference NREL's Supply Curve data as well as Siting Lab spatial data available on OEDI.

Lopez, Anthony↗
DOE OSTI2025

Testing gravitational physics by combining DESI DR1 and weak lensing datasets using the E_G estimator

The action of gravitational physics across space-time creates observable signatures in the behaviour of light and matter. We perform combined-probe studies using data from the Baryon Oscillation Spectroscopic Survey (BOSS) and Dark Energy Spectroscopic Instrument survey Data Release 1 (DESI-DR1), in combination with three existing weak lensing surveys, the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES), and the Hyper Suprime-Cam Survey (HSC), to test and constrain General Relativity (GR) in the context of the standard model of cosmology (LCDM). We focus on measuring the gravitational estimator statistic, E_G, which describes the relative amplitudes of weak gravitational lensing and galaxy velocities induced by a common set of overdensities. By comparing our amplitude measurements with their predicted scale- and redshift-dependence within the GR+LCDM model, we demonstrate that our results are consistent with the predictions of the Planck cosmology. The redshift span of the DESI dataset allows us to perform these E_G measurements at the highest redshifts achieved to date, z ~ 1.

cosmology↗
DOE OSTI2025

Masked Symbol Modeling for Demodulation of Oversampled Baseband Communication Signals in Impulsive Noise-Dominated Channels

Recent breakthroughs in natural language processing show that attention mech- anism in Transformer networks, trained via masked-token prediction, enables models to capture the semantic context of the tokens and internalize the grammar of language. While the application of Transformers to communication systems is a burgeoning field, the notion of context within physical waveforms remains under-explored. This paper addresses that gap by re-examining inter-symbol con- tribution (ISC) caused by pulse-shaping overlap. Rather than treating ISC as a nuisance, we view it as a deterministic source of contextual information embedded in oversampled complex baseband signals. We propose Masked Symbol Model- ing (MSM), a framework for the physical (PHY) layer inspired by Bidirectional Encoder Representations from Transformers methodology. In MSM, a subset of symbol-aligned samples is randomly masked, and a Transformer predicts the missing symbol identifiers using the surrounding “in-between” samples. Through this objective, the model learns the latent syntax of complex baseband waveforms. We illustrate MSM’s potential by applying it to the task of demodulating sig- nals corrupted by impulsive noise, where the model infers corrupted segments by leveraging the learned context. Our results suggest a path toward receivers that interpret, rather than merely detect communication signals, opening new avenues for context-aware PHY layer design.

Bedir, Oguz↗
DOE OSTI2024

Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Hamiltonian learning using machine-learning models trained with continuous measurements

Here, we build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Pathways Analysis Summary: Decarbonization Potential for Industrial Subsectors - Preliminary Modeling Results

This provides a summary of draft modeling efforts undertaken by the U.S. Department of Energy (DOE) Industrial Efficiency and Decarbonization Office (IEDO) as an extension and expansion of the 2022 Industrial Decarbonization Roadmap. IEDO is providing these draft modeling results to support stakeholder engagement and inform office- and department wide strategy and decision making. Section 1 provides an overview of the context for this analysis and modeling as well as information on the decarbonization pillars characterized and the models themselves. Section 2 presents modeling results of one net-zero emissions pathway each for six industrial subsectors: cement, chemicals, food and beverage, iron and steel, petroleum refining, and pulp and paper. It is important to note that these pathways are just one example and there is no single pathway for any single industrial subsector. Competition across different possible pathways will be essential to industrial decarbonization success. Section 3 provides an overview of the “rest of industry” subsectors and a high-level overview of net-zero barriers, challenges, pathways, and technologies. IEDO will continue to consider net-zero pathways and modeling for these rest of industry subsectors. Additional details will be made available in the future on the IEDO website.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Search for dark matter produced in association with a pair of bottom quarks in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A search for dark matter (DM) particles produced in association with bottom quarks is presented. The analysis uses proton-proton collision data at a center-of-mass energy of $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb -1 . The search is performed in a final state with large missing transverse momentum and a pair of jets originating from bottom quarks. No significant excess of data is observed with respect to the standard model expectation. Results are interpreted in the context of a type-II two-Higgs-doublet model with an additional light pseudoscalar (2HDM+a). An upper limit is set on the mass of the lighter pseudoscalar, probing masses up to 260 GeV at 95% confidence level. Sensitivity to the parameter space with the ratio of the vacuum expectation values of the two Higgs doublets, tan β, greater than 15 is achieved, capitalizing on the enhancement of couplings between pseudoscalars and bottom quarks with high tan β.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Ensemble cure kinetics network (ECK-Net): A method to derive cure kinetics of thermosetting resin

This paper introduces an Ensemble Cure Kinetics Network (ECK-Net), a neural network (NN)–based framework for modeling the cure kinetics of thermosetting resins within a phenomenological context. ECK-Net replaces traditional analytic models, which require extensive chemical insight and multiple isothermal/non-isothermal experiments, with a data-driven surrogate that maps nonlinear relationships between temperature, degree of cure, and reaction rate from differential scanning calorimetry data. The proposed approach predicts input-dependent kinetic coefficients of a generalized nth-order reaction equation rather than reaction rates directly, enabling a single unified model to represent various epoxy systems without relying on iso-conversional analysis or predefined functional forms. To ensure robustness, multiple independently trained networks under different random initializations are blended through an ensemble strategy, effectively mitigating the stochastic variability inherent to neural networks. The framework is validated using experimental datasets from multiple resin systems, including aerospace-grade materials (Toray 3900-2, Cycom 5320-1, and Hexcel 8552) and a windmill-grade resin (RIMR 035c). The model accurately reproduces the temporal evolution of the degree of cure under manufacturers’ recommended cure cycles across all tested resins systems, yielding Pearson’s correlation coefficients of 0.992, 0.994, 0.993, 0.997, respectively. To demonstrate process-level applicability, the trained network was implemented within the Abaqus environment to simulate out-of-autoclave (OOA) curing process of the CFRP panel composed of Toray T830H-6K/3900-2D prepreg. The simulation results showed excellent agreement with experimental temperature response (maximum peak temperature, simulation: 189.6 °C, experiment: 188.5 °C) and the final degree of cure (simulation: 0.948, experiment: 0.960 ± 0.013), confirming ECK-Net’s capability as a reliable alternative to conventional cure kinetics modeling methods.

Composite curing↗
DOE OSTI2024

Modeling scale-up of particle coating by atomic layer deposition

Atomic layer deposition (ALD) is a promising technique to functionalize particle surfaces for energy applications including energy storage, catalysis, and decarbonization. In this work, we present a set of models of ALD particle coating to explore the transition from lab scale to manufacturing. Our models encompass the main particle coating manufacturing approaches including rotary bed, fluidized bed, and continuously vibrating reactors. These models provide key metrics, such as throughput and precursor utilization, required to evaluate the scalability of ALD manufacturing approaches and their feasibility in the context of energy applications. Our results show that designs that force the precursor to flow through fluidized particles transition faster to a transport-limited regime where throughput is maximized. They also exhibit higher precursor utilization. In the context of continuous processes, our models indicate that it is possible to achieve self-extinguishing processes with almost 100% precursor utilization. A comparison with past experimental results of ALD in fluidized bed reactors shows excellent qualitative and quantitative agreement.

25 ENERGY STORAGE↗
DOE OSTI2025

Integrating particle flavor into deep learning models for hadronization

Hadronization models used in event generators are physics-inspired functions with many tunable parameters. Since we do not understand hadronization from first principles, there have been multiple proposals to improve the accuracy of hadronization models by utilizing more flexible parametrizations based on neural networks. These recent proposals have focused on the kinematic properties of hadrons, but a full model must also include particle flavor. In this paper, we show how to build a deep learning-based hadronization model that includes both kinematic (continuous) and flavor (discrete) degrees of freedom. Our approach is based on generative adversarial networks and we show the performance within the context of the cluster hadronization model within the erwig event generator.

Chan, Jay↗
DOE OSTI2024

Search for stealth supersymmetry in final states with two photons, jets, and low missing transverse momentum in proton-proton collisions at s = 13 TeV

The results of a search for stealth supersymmetry in final states with two photons and jets, targeting a phase space region with low missing transverse momentum ( p T miss ), are reported. The study is based on a sample of proton-proton collisions at s = 13 TeV collected by the CMS experiment, corresponding to an integrated luminosity of 138 fb - 1 . As LHC results continue to constrain the parameter space of the minimal supersymmetric standard model, the low p T miss regime is increasingly valuable to explore. To estimate the backgrounds due to standard model processes in such events, we apply corrections derived from simulation to an estimate based on a control selection in data. The results are interpreted in the context of simplified stealth supersymmetry models with gluino and squark pair production. The observed data are consistent with the standard model predictions, and gluino (squark) masses of up to 2150 (1850) GeV are excluded at the 95% confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗