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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Probing energetic light dark matter with multi-particle tracks signatures at DUNE

The search for relativistic scattering signals of cosmogenic light dark matter at terrestrial detectors has received increasing attention as an alternative approach to probe dark-sector physics. Large-volume neutrino experiments are well motivated for searches of dark matter that interacts very weakly with Standard Model particles and/or that exhibits a small incoming flux. We perform a dedicated signal sensitivity study for a detector similar to the one proposed by the DUNE Collaboration for cosmogenic dark-matter signals resulting from a non-minimal multi-particle dark-sector scenario. The liquid argon time projection chamber technology adopted for the DUNE detectors is particularly suited for searching for complicated signatures owing to good measurement resolution and particle identification, as well as dE/dx measurements to recognize merged tracks. Taking inelastic boosted dark matter as our benchmark scenario that allows for multiple visible particles in the final state, we demonstrate that the DUNE far detectors have a great potential for probing scattering signals induced by relativistic light dark matter. Detector effects and backgrounds have been estimated and taken into account. Model-dependent and model-independent expected sensitivity limits for a DUNE-like detector are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Probing exotic charged Higgs decays in the Type-II 2HDM through top rich signal at a future 100 TeV pp collider

The exotic decay modes of non-Standard Model Higgs bosons are efficient in probing the hierarchical Two Higgs Doublet Models (2HDM). In particular, the decay mode H ± → HW ± serves as a powerful channel in searching for charged Higgses. In this paper, we analyze the reach for H ± → HW ± → $t\bar{t}W$ at a 100 TeV pp collider, and show that it extends the reach of the previously studied ττW final states once above the top threshold. Top tagging technique is used, in combination with a boosted decision tree classifier. At the low tan β region, almost the entire hierarchical Type-II 2HDM parameter space can be probed via the combination of all exotic decay channels.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Zwanziger’s pairwise little group on the celestial sphere

We generalize Zwanziger’s pairwise little group to include a boost subgroup. We do so by working in the celestial sphere representation of scattering amplitudes. We propose that due to late time soft photon and graviton exchanges, matter particles in the asymptotic states in massless QED and gravity transform under the Poincaré group with an additional pair of pairwise celestial representations for each pair of matter particles. We demonstrate that the massless abelian and gravitational exponentiation theorems are consistent with the proposed pairwise Poincaré transformation properties. For massless QED we demonstrate that our results are consistent with the effects of the Faddeev-Kulish dressing and the abelian exponentiation theorem for celestial amplitudes found in arXiv:2012.04208. We discuss electric and magnetic charges simultaneously as it is especially natural to do so in this formalism.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A comparative study of Higgs boson production from vector-boson fusion

The data taken in Run II at the Large Hadron Collider have started to probe Higgs boson production at high transverse momentum. Future data will provide a large sample of events with boosted Higgs boson topologies, allowing for a detailed understanding of electroweak Higgs boson plus two-jet production, and in particular the vector-boson fusion mode (VBF). We perform a detailed comparison of precision calculations for Higgs boson production in this channel, with particular emphasis on large Higgs boson transverse momenta, and on the jet radius dependence of the cross section. We study fixed-order predictions at next-to-leading order and next-to-next-to-leading order QCD, and compare the results to NLO plus parton shower (NLOPS) matched calculations. The impact of the NNLO corrections on the central predictions is mild, with inclusive scale uncertainties of the order of a few percent, which can increase with the imposition of kinematic cuts. We find good agreement between the fixed-order and matched calculations in non-Sudakov regions, and the various NLOPS predictions also agree well in the Sudakov regime. We analyze backgrounds to VBF Higgs boson production stemming from associated production, and from gluon-gluon fusion. At high Higgs boson transverse momenta, the Δy$_{jj}$ and/or m$_{jj}$ cuts typically used to enhance the VBF signal over background lead to a reduced efficiency. We examine this effect as a function of the jet radius and using different definitions of the tagging jets. QCD radiative corrections increase for all Higgs production modes with increasing Higgs boson p$_{T}$, but the proportionately larger increase in the gluon fusion channel results in a decrease of the gluon-gluon fusion background to electroweak Higgs plus two jet production upon requiring exclusive two-jet topologies. We study this effect in detail and contrast in particular a central jet veto with a global jet multiplicity requirement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Observation of tW production in the single-lepton channel in pp collisions at $ \sqrt{s} $ = 13 TeV

A measurement of the cross section of the associated production of a single top quark and a W boson in final states with a muon or electron and jets in proton-proton collisions at $ \sqrt{s} $ = 13 TeV is presented. The data correspond to an integrated luminosity of 36 fb$^{−1}$ collected with the CMS detector at the CERN LHC in 2016. A boosted decision tree is used to separate the tW signal from the dominant t$ \overline{\mathrm{t}} $ background, whilst the subleading W+jets and multijet backgrounds are constrained using data-based estimates. This result is the first observation of the tW process in final states containing a muon or electron and jets, with a significance exceeding 5 standard deviations. The cross section is determined to be 89 ± 4 (stat) ± 12 (syst) pb, consistent with the standard model.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Improved calorimetric particle identification in NA62 using machine learning techniques

Measurement of the ultra-rare $K^+$ → $π$ +$ν$$\overline{ν}$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2×10 -5 for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10 -5 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Search for neutral long-lived particles that decay into displaced jets in the ATLAS calorimeter in association with leptons or jets using pp collisions at $\sqrt{\textrm{s}}$ = 13 TeV

A search for neutral long-lived particles (LLPs) decaying in the ATLAS hadronic calorimeter using 140 fb -1 of proton-proton collisions at $\sqrt{s}$ = 13 TeV delivered by the LHC is presented. The analysis is composed of three channels. The first targets pair-produced LLPs, where at least one LLP is produced with sufficiently low boost that its decay products can be resolved as separate jets. The second and third channels target LLPs respectively produced in association with a W or Z boson that decays leptonically. In each channel, different search regions target different kinematic regimes, to cover a broad range of LLP mass hypotheses and models. No excesses of events relative to the background predictions are observed. Higgs boson branching fractions to pairs of hadronically decaying neutral LLPs larger than 1% are excluded at 95% confidence level for proper decay lengths in the range of 30 cm to 4.5 m depending on the LLP mass, a factor of three improvement on previous searches in the hadronic calorimeter. The production of long-lived dark photons in association with a Z boson with cross-sections above 0.1 pb is excluded for dark photon mean proper decay lengths in the range of 20 cm to 50 m, improving previous ATLAS results by an order of magnitude. Finally, long-lived photo-phobic axion-like particle models are probed for the first time by ATLAS, with production cross-sections above 0.1 pb excluded in the 0.1 mm to 10 m range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Extracting the asymptotic behavior of S-matrix elements from their phases

The asymptotic kinematic limits of S-matrices are dominated by large logarithms which, roughly speaking, fall into two categories: those which are controlled by a renormalization group (RG) scale, which we may think of as logs involving ratios of invariant mass scales, and those which are functions of ratios of rapidities, so called “rapidity logs”. It has been pointed out by Caron-Huot and Wilhlem [1] that RG anomalous dimension can be extracted from the phase of the S-matrix, which can greatly simplify calculations via unitarity methods. In this paper we generalize the results of [1] to show that the phase can be used to reconstruct rapidity anomalous dimensions, by performing a special type of complex boost. The methodology introduced allows one to calculate without the need for a rapidity regulator which can lead to significant simplifications. We demonstrate the use of this method to derive the rapidity anomalous dimensions in the Sudakov form factor and the two parton soft function at two loops order.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Semi-visible jets + X: illuminating dark showers with radiation

We investigate the potential to search for semi-visible jets (SVJs) at the Large Hadron Collider (LHC) using initial-state radiation (ISR). Both photon ISR and jet ISR channels are considered, using a benchmark signal model with the decay of a leptophobic Z$^{′}$ mediator forming two SVJs. We compare and extend several techniques to decompose the missing transverse momentum into per-jet contributions, in order to reconstruct the mediator mass and to define a new observable measuring the fraction of invisible dark hadrons. The presence of ISR facilitates the identification of the SVJs, and the resulting boost improves the resolution of the observables, especially for models with high invisible fractions. We combine the two observables to propose a complete search strategy and discuss an extension of the strategy to probe the whole model parameter space.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Search for supersymmetry using vector boson fusion signatures and missing transverse momentum in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

This paper presents a search for supersymmetric particles in models with highly compressed mass spectra, in events consistent with being produced through vector boson fusion. The search uses 140 fb −1 of proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the ATLAS experiment at the Large Hadron Collider. Events containing at least two jets with a large gap in pseudorapidity, large missing transverse momentum, and no reconstructed leptons are selected. A boosted decision tree is used to separate events consistent with the production of supersymmetric particles from those due to Standard Model backgrounds. The data are found to be consistent with Standard Model predictions. The results are interpreted using simplified models of R-parity-conserving supersymmetry in which the lightest supersymmetric partner is a bino-like neutralino with a mass similar to that of the lightest chargino and second-to-lightest neutralino, both of which are wino-like. Lower limits at 95% confidence level on the masses of next-to-lightest supersymmetric partners in this simplified model are established between 117 and 120 GeV when the lightest supersymmetric partners are within 1 GeV in mass.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Search for the lepton-flavor-violating τ − → e ∓ ℓ ± ℓ − decays at Belle II

We present the result of a search for the charged-lepton-flavor violating decays τ − → e ∓ ℓ ± ℓ − , where ℓ is a muon or an electron, using a data sample with an integrated luminosity of 428 fb −1 recorded by the Belle II experiment at the SuperKEKB e + e − collider. The selection of e + e − → τ + τ − events containing a signal candidate is based on an inclusive-tagging reconstruction and on a boosted decision tree to suppress background. Upper limits on the branching fractions between 1.3 and 2.5 × 10 −8 are set at the 90% confidence level. These results are the most stringent bounds to date for four of the modes.

flavour physics↗
DOE OSTI2023

Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks

Abstract Studies conducted at sites across ecological research networks usually strive to scale their results to larger areas, trying to reach conclusions that are valid throughout larger enclosing regions. Network representativeness and constituency can show how well conditions at sampling locations represent conditions also found elsewhere and can be used to help scale-up results over larger regions. Multivariate statistical methods have been used to design networks and select sites that optimize regional representation, thereby maximizing the value of datasets and research. However, in networks created from already established sites, an immediate challenge is to understand how well existing sites represent the range of environments in the whole area of interest. We performed an analysis to show how well sites in the USDA Long-Term Agroecosystem Research (LTAR) Network represent all agricultural working lands within the conterminous United States (CONUS). Our analysis of 18 LTAR sites, based on 15 climatic and edaphic characteristics, produced maps of representativeness and constituency. Representativeness of the LTAR sites was quantified through an exhaustive pairwise Euclidean distance calculation in multivariate space, between the locations of experiments within each LTAR site and every 1 km cell across the CONUS. Network representativeness is from the perspective of all CONUS locations, but we also considered the perspective from each LTAR site. For every LTAR site, we identified the region that is best represented by that particular site—its constituency—as the set of 1 km grid locations best represented by the environmental drivers at that particular LTAR site. Representativeness shows how well the combination of characteristics at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location. LTAR representativeness was good across most of the CONUS. Representativeness for croplands was higher than for grazinglands, probably because croplands have more specific environmental criteria. Constituencies resemble ecoregions but have their environmental conditions “centered” on those at particular existing LTAR sites. Constituency of LTAR sites can be used to prioritize the locations of experimental research at or even within particular sites, or to identify the extents that can likely be included when generalizing knowledge across larger regions of the CONUS. Sites with a large constituency have generalist environments, while those with smaller constituency areas have more specialized environmental combinations. These “specialist” sites are the best representatives for smaller, more unusual areas. The potential of sharing complementary sites from the Long-Term Ecological Research (LTER) Network and the National Ecological Observatory Network (NEON) to boost representativeness was also explored. LTAR network representativeness would benefit from borrowing several NEON sites and the Sevilleta LTER site. Later network additions must include such specialist sites that are targeted to represent unique missing environments. While this analysis exhaustively considered principal environmental characteristics related to production on working lands, we did not consider the focal agronomic systems under study, or their socio-economic context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms

Fires, including wildfires, harm air quality and essential public services like transportation, communication, and utilities. These fires can also influence atmospheric conditions, including temperature and aerosols, potentially affecting severe convective storms. Here, we investigate the remote impacts of fires in the western United States (WUS) on the occurrence of large hail (size: $\geqslant$ 2.54 cm) in the central US (CUS) over the 20-year period of 2001–20 using the machine learning (ML), Random Forest (RF), and Extreme Gradient Boosting (XGB) methods. The developed RF and XGB models demonstrate high accuracy (> 90%) and F1 scores of up to 0.78 in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in four states (Wyoming, South Dakota, Nebraska, and Kansas). The key contributing variables identified from both ML models include the meteorological variables in the fire region (temperature and moisture), the westerly wind over the plume transport path, and the fire features (i.e., the maximum fire power and burned area). Importantly, the results confirm a linkage between WUS fires and severe weather in the CUS, corroborating the findings of our previous modeling study conducted on case simulations with a detailed physics model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Observed and Projected Changes of Global Monsoons: Current Status and Future Perspectives

The global monsoon system, encompassing the Asian-Australian, African, and American monsoons, sustains two-thirds of the world’s population by regulating water resources and agriculture. Monsoon anomalies pose severe risks, including floods and droughts. Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms. Observational data reveal a 20th-century shift: increased rainfall pre-1950s, followed by aridification and partial recovery post-1980s, driven by both internal variability (e.g., Atlantic Multidecadal Oscillation) and external forcings (greenhouse gases, aerosols), while ENSO drives interannual variability through ocean-atmosphere interactions. Future projections under greenhouse forcing suggest long-term monsoon intensification, though regional disparities and model uncertainties persist. Models indicate robust trends but struggle to quantify extremes, where thermodynamic effects (warming-induced moisture rise) uniformly boost heavy rainfall, while dynamical shifts (circulation changes) create spatial heterogeneity. Volcanic eruptions and proposed solar radiation modification (SRM) further complicate predictions: tropical eruptions suppress monsoons, whereas high-latitude events alter cross-equatorial flows, highlighting unresolved feedbacks. The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons. Critical challenges remain. Model biases and sparse 20th-century observational data hinder accurate attribution. The interplay between natural variability and anthropogenic forcings, along with nonlinear extreme precipitation risks under warming, demands deeper mechanistic insights. Additionally, SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation. Addressing these gaps necessitates enhanced observational networks, refined climate models, and interdisciplinary efforts to disentangle multiscale drivers, ultimately improving resilience strategies for monsoon-dependent regions.

climate extreme events↗
DOE OSTI2024

Exploring the role of judgement and shared situation awareness when working with AI recommender systems

Abstract AI-advised Decision Making is a form of human-autonomy teaming in which an AI recommender system suggests a solution to a human operator, who is responsible for the final decision. This work seeks to examine the importance of judgement and shared situation awareness between humans and automated agents when interacting together in the form of a recommender systems. We propose manipulating both human judgement and shared situation awareness by providing the human decision maker with relevant information that the automated agent (AI), in the form of a recommender system, uses to generate possible courses of action. This paper presents the results of a two-phase between-subjects study in which participants and a recommender system jointly make a high-stakes decision. We varied the amount of relevant information the participant had, the assessment technique of the proposed solution, and the reliability of the recommender system. Findings indicate that this technique of supporting the human’s judgement and establishing a shared situation awareness is effective in (1) boosting the human decision maker’s situation awareness and task performance, (2) calibrating their trust in AI teammates, and (3) reducing overreliance on an AI partner. Additionally, participants were able to pinpoint the limitations and boundaries of the AI partner’s capabilities. They were able to discern situations where the AI’s recommendations could be trusted versus instances when they should not rely on the AI’s advice. This work proposes and validates a way to provide model-agnostic transparency into recommender systems that can support the human decision maker and lead to improved team performance.

Srivastava, Divya↗
DOE OSTI2022

Droplet Evaporation-Based Approach for Microliter Fuel Property Measurements

Small-volume, high-throughput screening techniques are sought to enable downselection from a large candidate pool of bio-blendstocks to a select few, having physical properties consistent with requirements of downsized, turbo-boosted internal combustion engines. Herein, this work presents a droplet evaporation-based approach to predict heat of vaporization, vapor pressure, diffusion coefficient, and Lennard–Jones parameters for an unknown fuel. Two different schemes, considering the isothermal evaporation of a moving droplet in ambient air, are proposed, which combine droplet velocity and temperature measurements, with some known properties to predict unknown properties. The schemes utilize an inverse solution of a transient model of droplet evaporation solved in an iterative fashion. A baseline scheme, which only requires droplet size change measurements, is evaluated using test data for three liquid fuels, comprising of alkanes and alcohols, as obtained in a temperature-controlled chamber. Results yield temperature-dependent heat of vaporization and vapor pressure predictions within 10 % and 22 %, respectively, of reference values. The advanced scheme, which additionally requires droplet temperature measurement, is numerically evaluated in the current work and will be experimentally validated in future efforts. The advanced scheme is found to significantly improve prediction quality, with deviations less than 2 % and 1 % for heat of vaporization and vapor pressure, while also predicting diffusion coefficient and Lennard–Jones parameters within 5 % and 8 %, respectively. The combined set of approaches, which primarily track droplet evaporation, can be incorporated into a small-volume, high-throughput fuel screening process.

09 BIOMASS FUELS↗
DOE OSTI2021

Progress and Challenges in Self-Healing Cementitious Materials

Concrete is the most used man-made material today consisting of a mixture of aggregates (sand, gravel, crashed rock) and paste. The paste component, made up of cement and water, cracks with time. Despite this shortcoming, research towards next-generation cement and concrete has only taken a boost in the past 10-15 years. In particular, self-healing cementitious materials is a research area that has attracted a great deal of attention. A number of reported novel formulations have demonstrated an increase in mechanical and chemical stability with respect to conventional Portland cement, through the addition of inorganic, organic, and even biological additives. This review reports on the latest advances in cement research related to the synthesis of self-healing cements applied in concrete structures. These include geopolymers, engineered cementitious materials (ECC), bacterial cement composites, microencapsulated self-healing materials, shape memory alloys (SMA), and polymer-cement composites. This work describes the performance of each cementitious material and the mechanism responsible for self-healing, including a section on the latest computational modeling studies towards this end. A detailed understanding of these various self-healing cement technologies, including their strengths and weaknesses, is critical at the time of determining the type of structures that these materials could be used for.

Fernandez, Carlos A.↗
DOE OSTI2022

Light Dark Matter Detection with Hydrogen-Rich Targets and Low-$T_c$ TES Detectors

Direct detection of nuclear scatterings of sub-GeV dark matter (DM) particles favors low-Z nuclei. Hydrogen nucleus, which has a single proton, provides the best kinematic match. The characteristic nuclear recoil energy is boosted by a factor of a few tens from those for larger nuclei used in traditional Weakly Interacting Massive Particles searches. Furthermore, hydrogen is optimal for detecting spin-dependent nuclear scatterings of sub-GeV DM, where large parameter space still remains unconstrained yet. In this paper, we first introduce several hydrogen-rich targets, which emit two classes of signals under kinetic excitations. One class of the signals is infrared photons, which are from fundamental vibrational and rotational modes of molecules and at several characteristic wavelengths. Another is acoustic phonons and optical phonons that decay into acoustic phonons. Then we discuss the technical status and future researches of low-T c transition-edge sensor (TES) detectors, which measure the infrared photons and acoustic phonons with desirable sensitivities. Utilization of hydrogen-rich targets and ultra-sensitive low-T c TES detectors for light DM detection requires both theoretical modeling and experimental prototyping.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗