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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Measurement of the top-quark pole mass in dileptonic $t\overline{t}$ + 1-jet events at $\sqrt{s}=13$ TeV with the ATLAS experiment

A measurement of the top-quark pole mass $m$$^{pole}_{t}$ is presented in $t\bar{t}$ events with an additional jet, $t\bar{t}$+ 1-jet, produced in pp collisions at $\sqrt{s} = 13 TeV. The data sample, recorded with the ATLAS experiment during Run 2 of the LHC, corresponds to an integrated luminosity of 140 fb −1 . Events with one electron and one muon of opposite electric charge in the final state are selected to measure the $t\bar{t}$ + 1-jet differential cross-section as a function of the inverse of the invariant mass of the $t\bar{t}$ + 1-jet system. Iterative Bayesian Unfolding is used to correct the data to enable comparison with fixed-order calculations at next-to-leading-order accuracy in the strong coupling. The process pp → $t\bar{t}$j(2 → 3), where top quarks are taken as stable particles, and the process pp → $b\bar{b}$l + vl – $\overline{ν}$j (2 → 7), which includes top-quark decays to the dilepton final state and off-shell effects, are considered. The top-quark mass is extracted using a χ 2 fit of the unfolded normalized differential cross-section distribution. The results obtained with the 2 → 3 and 2 → 7 calculations are compatible within theoretical uncertainties, providing an important consistency check.

Hadron-Hadron Scattering↗
DOE OSTI2025

Search for resonant leptoquark production via lepton-jet signatures in pp collisions at $\sqrt{s}=13$ TeV and $\sqrt{s}=13.6$ TeV with the ATLAS detector

This paper presents a search for physics beyond the Standard Model targeting a heavy resonance visible in the invariant mass of the lepton-jet system. The analysis focuses on final states with a high-energy lepton and jet, and is optimised for the resonant production of leptoquarks — a novel production mode mediated by the lepton content of the proton originating from quantum fluctuations. Four distinct and orthogonal final states are considered: e+light jet, μ+light jet, e+b-jet, and μ+b-jet, constituting the first search at the Large Hadron Collider for resonantly produced leptoquarks with couplings to electrons and muons. Events with an additional same-flavour lepton, as expected from higher-order diagrams in the signal process, are also included in each channel. The search uses proton-proton collision data from the full Run 2, corresponding to an integrated luminosity of 140 fb −1 at a centre-of-mass energy of $\sqrt{s}=13$ TeV, and from a part of Run 3 (2022–2023), corresponding to 55 fb −1 at $\sqrt{s}=13.6$ TeV. No significant excess over Standard Model predictions is observed. The results are interpreted as exclusion limits on scalar leptoquark ($\tilde{S}$ 1 ) production, substantially improving upon previous ATLAS constraints from leptoquark pair production for large coupling values. The excluded $\tilde{S}$ 1 $\tilde{S}$ 1 mass ranges depend on the coupling strength, reaching up to 3.4 TeV for quark-lepton couplings y de = 1.0, and up to 4.3 TeV, 3.1 TeV, and 2.8 TeV for y sμ , y be , and y bμ couplings set to 3.5, respectively.

Hadron-Hadron Scattering↗
DOE OSTI2025

Energy scale and resolution for anti-$k_t$ jets with radius parameters $R$ = 0.2 and 0.6 measured in proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

Jets with different radius parameters R are an important tool for probing quantum chromodynamics processes at different angular scales. Jets with small R = 0.2 are instrumental in measurements of the substructure of large-R jets resulting from collimated hadronic decays of energetic W, Z, and Higgs bosons, top quarks, and of potential new resonances. This paper presents measurements of the energy scale, resolution, and associated uncertainties of jets with radius parameters R = 0.2 and 0.6, obtained using the ATLAS detector. The results are based on 37 fb -1 of proton–proton collision data from the Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV. A new in situ method for measuring jet energy scale differences between data and Monte Carlo simulations is presented. The systematic uncertainties in the jet energy scale for central jets $(|\eta | < 1.2)$ typically vary from 1% to about 5% as a function of $|\eta |$ at very low transverse momentum, $p_T$ of around 20 GeV for both R = 0.2 and 0.6 jets. The relative energy resolution ranges from (35 ± 6)% at $p_T$ = 20 GeV to (6 ± 0.5)% at $p_T$ = 300 GeV for central R = 0.2 jets, and is found to be slightly worse for R + 0.6 jets. Finally, the effect of close-by hadronic activity on the jet energy scale is investigated and is found to be well modelled by the ATLAS Monte Carlo simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Search for a new pseudoscalar decaying into a pair of bottom and antibottom quarks in top-associated production in $\sqrt{s} = 13$ TeV proton–proton collisions with the ATLAS detector

A search for a pseudoscalar a produced in association with a top-quark pair, or in association with a single top quark plus a W boson, with the pseudoscalar decaying into b-quarks $(a → b\bar{b})$, is performed using the full Run 2 data sample using a dileptonic decay mode signature. The search covers pseudoscalar boson masses between 12 and 100 GeV and involves both the kinematic regime where the decay products of the pseudoscalar are reconstructed as two standard b-tagged small-radius jets, or merged into a large-radius jet due to its Lorentz boost. No significant excess relative to expectations is observed. Assuming a branching ratio BR$(a → b\bar{b})$ = 100%, the range of pseudoscalar masses between 50 and 80 GeV is excluded at 95% confidence level for a coupling of the pseudoscalar to the top quark of 0.5, while a coupling of 1.0 is excluded at 95% confidence level for the masses considered, with the coupling defined as the strength modifier of the Standard Model Yukawa coupling.

Aad, G. [Aix-Marseille Univ., Marseille (France)] ↗
DOE OSTI2025

A continuous calibration of the ATLAS flavour-tagging classifiers via optimal transportation maps

A calibration of the ATLAS flavour-tagging algorithms using a new calibration procedure based on optimal transportation maps is presented. Simultaneous, continuous corrections to the b-jet, c-jet, and light-flavour jet classification probabilities from jet-tagging algorithms in simulation are derived for b-jets using $t\bar{t} \rightarrow e\mu \nu \nu bb$ data. After application of the derived calibration maps, closure between simulation and observation is achieved for jet flavour observables used in ATLAS analyses of Large Hadron Collider (LHC) Run 2 proton-proton collision data. This continuous calibration opens up new possibilities for the future use of jet flavour information in LHC analyses and also serves as a guide for deriving high-dimensional corrections to simulation via transportation maps, an important development for a broad range of inference tasks.

Aad, G. [Aix-Marseille Université] (ORCID:00000002↗
DOE OSTI2025

Measurement of high-mass $t\bar{t}\ell ^{+}\ell ^{-}$ production and lepton flavour universality-inspired effective field theory interpretations at $\sqrt{s}=13$ $\text {T}\text {e}\hspace{-1.00006pt}\text {V}$ with the ATLAS detector

Measurements of $t\bar{t}\ell ^{+}\ell ^{-}$ production in the region of high dilepton invariant mass with effective field theory (EFT) interpretations are presented. They are performed using final states with three isolated leptons (electrons or muons) and are based on $\sqrt{s} = 13$ TeV proton–proton collision data with an integrated luminosity of $140\,\textrm{fb}^{-1}$, recorded from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider. Measurements of the $t\bar{t}\ell ^{+}\ell ^{-}$ signal strength and cross-section upper-limits are performed inclusively in lepton flavour and separately for electrons and muons. The study also aims to probe anomalous four-fermion interactions including to test for possible lepton flavor universality violation. No significant deviations from the Standard Model predictions are observed and the measurements are interpreted through the EFT formalism to provide new constraints on the relevant operators.

Aad, G. [CNRS/IN2P3] (ORCID:0000000266654934)↗
DOE OSTI2025

Measurements of Higgs boson production via gluon–gluon fusion and vector-boson fusion using $H\rightarrow WW^*\rightarrow \ell \nu \ell \nu$ decays in pp collisions with the ATLAS detector and their effective field theory interpretations

Higgs boson production cross-sections via gluon–gluon fusion and vector-boson fusion in proton–proton collisions are measured in the $H\rightarrow WW^*\rightarrow \ell \nu \ell \nu$ decay channel. The Large Hadron Collider delivered proton–proton collisions at a centre-of-mass energy of 13 TeV between 2015 and 2018, which were recorded by the ATLAS detector, corresponding to an integrated luminosity of $140\,\text {fb}^{-1}.$ The total cross-sections for Higgs boson production by gluon–gluon fusion and vector-boson fusion times the $H\rightarrow WW^*$ branching ratio are measured to be $12.4^{+1.3}_{-1.2}\,\text {pb}$ and $0.79^{+0.18}_{-0.16}\,\text {pb},$ respectively, in agreement with the Standard Model predictions. Higgs boson production is further characterised through measurements of Simplified Template Cross-Sections in a total of fifteen kinematic fiducial regions. A new scheme of kinematic fiducial regions has been introduced to enhance the sensitivity to CP-violating effects in Higgs boson interactions. Both schemes are used to constrain CP-even and CP-odd dimension-six operators in the Standard Model effective field theory.

Aad, G. [CPPM, Aix-Marseille Université, CNRS/IN2P↗
DOE OSTI2025

The environmental impact, carbon emissions and sustainability of computing in the ATLAS experiment

ATLAS, a general-purpose experiment at the Large Hadron Collider (LHC), makes use of a large internationally-distributed computing infrastructure, including over 10 6 TB of managed data on disk and tape and almost one million simultaneously running CPU cores. Upgrades for the High-Luminosity LHC (HL-LHC) will increase the required computing resources by a factor of 3–4 by the beginning of the 2030s, and by an order of magnitude before the conclusion of data taking at the beginning of the 2040s. These resources are spread over around 100 computing sites worldwide. Efforts are underway within the experiment to evaluate and mitigate various aspects of the environmental impact of the sites, with the additional long-term goal of making recommendations to the sites that will significantly reduce the total expected environmental impact in the HL-LHC era. These efforts take several forms: building awareness in the experiment community, adjusting aspects of the computing policy, and modifications of data center configurations, either in ways that take advantage of particular features of ATLAS workloads or in generic ways that reduce the environmental impact of the computing resources. This paper describes the ongoing investigations and approaches that have already provided useful and actionable outcomes.

Aad, G. [CNRS/IN2P3] (ORCID:0000000266654934)↗
DOE OSTI2025

Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network

The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpreted in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2002

Artificial Immune System Approaches for Aerospace Applications

Artificial Immune Systems (AIS) combine a priori knowledge with the adapting capabilities of biological immune system to provide a powerful alternative to currently available techniques for pattern recognition, modeling, design, and control. Immunology is the science of built-in defense mechanisms that are present in all living beings to protect against external attacks. A biological immune system can be thought of as a robust, adaptive system that is capable of dealing with an enormous variety of disturbances and uncertainties. Biological immune systems use a finite number of discrete "building blocks" to achieve this adaptiveness. These building blocks can be thought of as pieces of a puzzle which must be put together in a specific way-to neutralize, remove, or destroy each unique disturbance the system encounters. In this paper, we outline AIS models that are immediately applicable to aerospace problems and identify application areas that need further investigation.

KrishnaKumar, Kalmanje↗
DOE OSTI2026

Self-driving thin film laboratory: autonomous epitaxial atomic-layer synthesis via real-time computer vision analysis of electron diffraction

Emerging materials science platforms with the ability to make autonomous decisions on the fly are fundamentally changing the outlook and protocols for materials optimization and discovery. Because AI-driven self-navigating schemes can effectively reduce the total number of iterations needed to arrive at the "answer" (i.e. the best stochiometric composition for a desired physical property, optimum materials processing parameters, etc.) by significant margins, they have the potential to revolutionize materials and chemical manufacturing processes at large in research laboratory settings as well as in industrial plants. Here, we demonstrate a successful implementation of real-time closed-loop autonomous navigation of a multi-dimensional materials synthesis parameter space for fabricating phase-pure epitaxial films of a metastable phase of a functional oxide in a combinatorial pulsed laser deposition chamber. Sequential epitaxial growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision analysis of reflection high-energy electron diffraction (RHEED) images of the unit-cell level film being deposited. The autonomous scheme regularly resulted in > 30-fold reduction in the number of required experiments compared to a comprehensive mapping of the parameter space. The real-time workflow developed here can be readily extended to a variety of thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous optimization of semiconductor manufacturing.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗
NASA NTRSDate not supplied

Space-based Sensor for Extreme Fire Weather Events

Catastrophic bushfires are becoming increasingly prevalent as climate change advances. Impacts extend beyond national borders. Multinational efforts can inform new science and management practices. Space-based sensors and integrated data facilities will play an important role. This paper describes a collaborative project between a consortium of Australian universities and NASA Centers to develop and implement a small satellite platform comprising highly integrated thermal and lightning sensors coupled with AI-based edge computing to help predict, detect, and track bushfires, supporting mitigation activities. This will fill an important capability gap since Australia does not currently have any sovereign Earth observation satellites. This program is enabled by and builds on Australia-NASA collaboration and will also support fire science and management activities in the broader global context.

wildfire↗
DOE OSTI2024

Reimagining DOE Lab-University Partnership for the AI Era

This report describes observations and suggestions from a workshop on needs for partnerships between national laboratories and universities in the AI era. The workshop took place over two days in March of 2024 at Texas A&M University’s Bush School of Government and Public Service Washington, D.C., teaching site. Through good fortune this happened to be at the peak of cherry blossom season and the weather was beautiful. In attendance were professors and leadership from universities across the nation, members of four national laboratories, and a representative of the Office of Critical and Emerging Technologies in Department of Energy (DOE). This group spanned a broad range of disciplines—applied mathematics, materials science, nuclear security, intelligence, and more. The workshop also had the benefit of insights from Charlie McMillan, former director of Los Alamos National Laboratory, and retired Air Force Lieutenant General Jack Shanahan, who led AI efforts at the Pentagon.

42 ENGINEERING↗
NASA NTRSDate not supplied

Repurposing Drilling Control Diagnostics for Subsurface Edge Detection and Boundary Advisement During Planetary Drilling

Informed decision-making during lunar drilling and sampling missions will require data monitoring tools and specialized ground data systems. Accurate and updated situational awareness, with ongoing data monitoring, is critical for timely responses by to incoming science data. Traverse plans and scheduled activities may need to be flexibly changed in order to react to unexpected data or situations. Unlike (for example) Mars missions, the relative lightspeed closeness of the Moon allows for near-real-time ground processing of incoming mission and instrument data. An Apollo-class lunar regolith drill will in a sense “travel” a meter or two vertically at a given subsurface characterization site. As the drill penetrates into lunar regolith, it is likely to encounter a range of material densities, orientations, fracture toughness, and (perhaps) ice percentages. Lunar drill telemetry can provide science teams with a valuable first look into the subsurface structure, the regolith bulk properties, and constituents at each drilled site. Real-time AI-based recognition and reaction to downhole situations has been developed for automated deeper drilling on Mars and beyond. We can leverage the same knowledge bases and pattern-matching as areal-time interpreter of the subsurface, a situational awareness tool during drilling operations. We recently (Sept. 2019) demonstrated this AI drilling monitoring and analysis capability, in control of in-situ drilling and sampling operations, mounted on a KREX-2 rover in Chile’s Atacama Desert. Terrestrial automated drilling log analyses in oil exploration have used similar machine learning techniques in classifying and identifying features in drilling logs –but these typically are designed assuming a drilling fluid influencing downhole measurements and data (permeability, resistivity). Drilling models and existing AI software designed to detect and respond to drilling faults and hard materials can be repurposed, for near-real-time (ground-based) interpretation of drilling telemetry –a potentially valuable advisory tool for strata boundaries and changes in drilling parameters. On the Moon, this approach could be used to study the structure and to some extent the composition of lunar regolith vs. borehole depth, based on recognizable variations in fracture hardness, drilling energy and penetration rates while actively drilling. Since the early 2000s, a series of increasingly-capable real-time drilling telemetry interpretation and characterization software tools have been developed. These subsurface models and software tools have monitored the real-time drilling data received, and automatically identified changes in drill behavior (e.g., encountering a harder target layer, bit inclusions, drill choking due to infall downhole, and others) correlating these with subsurface structures and features. We discuss the mappings between drill borehole parameters, faults or events detected, and modeled changes in rock layer boundaries, in examples drawn from field testing at analog sites in an Arctic impact crater, Rio Tinto, and Chile’s Atacama Desert. These demonstrate how subsurface structural boundaries led to fault detections and responses by the software.

drilling advisor↗
DOE OSTI2025

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

AI in Space: The Era of Autonomous Space Systems

The development of autonomy capabilities is the key to three vastly important strategic technical challenges facing NASA: the reduction of mission costs, the continuing return of quality science products through limited communications bandwidth, and the launching of a new era of solar system exploration characterized by sustained presence and in-depth scientific studies, including the search for life. Autonomy will benefit future NASA missions by migrating routine, traditionally ground-based functions to the spacecraft, by directly supporting the decoupling of spacecraft from the ground through new operations concepts, by enabling direct links between scientists and the space platforms carrying their instruments of investigation, and by the closing, of planning and control loops onboard, enabling space platforms to directly address uncertainty in the real-time mission context. The talk will survey ongoing, autonomy technology development projects at NASA, many of which have been or will soon be the subject of flight technology experiments, or are already targeted for mission use. The talk will also survey the exciting suite of future NASA space exploration missions, and make the case for the central role of autonomy in achieving the goals of these bold, unprecedented missions: cooperating rovers on the surface of Mars, the search for Earth-like planets around nearby stars, asteroid and comet landers, aerobots in planetary atmospheres, and a series of missions to intriguing Europa, perhaps culminating in a submersible to investigate its putative ocean. Finally, the talk will conclude with some farther-reaching speculations on how to create properties such as long-term survivability and evolvability in future space systems, such that they will be well equipped to extend humanity exploratory presence into the interstellar realm.

Doyle, Richard J.↗