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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Measurements of the Higgs boson production and decay rates and constraints on its couplings from a combined ATLAS and CMS analysis of the LHC pp collision data at $ \sqrt{s}=7 $ and 8 TeV

Combined ATLAS and CMS measurements of the Higgs boson production and decay rates, as well as constraints on its couplings to vector bosons and fermions, are presented. The combination is based on the analysis of five production processes, namely gluon fusion, vector boson fusion, and associated production with a W or a Z boson or a pair of top quarks, and of the six decay modes H → ZZ, W W , γγ, ττ, bb, and μμ. All results are reported assuming a value of 125.09 GeV for the Higgs boson mass, the result of the combined measurement by the ATLAS and CMS experiments. The analysis uses the CERN LHC proton-proton collision data recorded by the ATLAS and CMS experiments in 2011 and 2012, corresponding to integrated luminosities per experiment of approximately 5 fb$^{−1}$ at $ \sqrt{s}=7 $ TeV and 20 fb$^{−1}$ at $ \sqrt{s}=8 $ TeV. The Higgs boson production and decay rates measured by the two experiments are combined within the context of three generic parameterisations: two based on cross sections and branching fractions, and one on ratios of coupling modifiers. Several interpretations of the measurements with more model-dependent parameterisations are also given. The combined signal yield relative to the Standard Model prediction is measured to be 1.09 ± 0.11. The combined measurements lead to observed significances for the vector boson fusion production process and for the H → ττ decay of 5.4 and 5.5 standard deviations, respectively. The data are consistent with the Standard Model predictions for all parameterisations considered.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Erratum to: Measurement of the inclusive jet cross-section in proton-proton collisions at $ \sqrt{s}=7 $ TeV using 4.5 fb -1 of data with the ATLAS detector

It was found that the non-perturbative corrections calculated using Pythia with the Perugia 2011 tune did not include the effect of the underlying event. The affected correction factors were recomputed using the Pythia 6.427 generator. These corrections are applied as baseline to the NLO pQCD calculations and thus the central values of the theoretical predictions have changed by a few percent with the new corrections. This has a minor impact on the agreement between the data and the theoretical predictions. Figures 2 and 6 to 13, and all the tables have been updated with the new values. A few sentences in the discussion in sections 5.2 and 9 were altered or removed.[Figure not available: see fulltext.]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗
DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗
DOE OSTI2018

Subnanogram proteomics: Impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples

One of the greatest challenges for mass spectrometry (MS)-based proteomics is the limited ability to analyze small samples. Here we investigate the relative contributions of liquid chromatography (LC), MS instrumentation and data analysis methods with the aim of improving proteome coverage for sample sizes ranging from 0.5 ng to 50 ng. We show that the LC separations utilizing 30-µm-i.d. columns increase signal intensity by >3-fold relative to those using 75-µm-i.d. columns, leading to 32% increase in peptide identifications. The Orbitrap Fusion Lumos mass spectrometer significantly boosted both sensitivity and sequencing speed relative to earlier generation Orbitraps (e.g., LTQ-Orbitrap), leading to a ~3× increase in peptide identifications and 1.7× increase in identified protein groups for 2 ng tryptic digests of bacterial lysate. The Match Between Runs algorithm of open-source MaxQuant software further increased proteome coverage by ~ 95% for 0.5 ng samples and by ~42% for 2 ng samples. The present platform is capable of identifying >3000 protein groups from tryptic digestion of cell lysates equivalent to 50 HeLa cells and 100 THP-1 cells (~10 ng total proteins), respectively, and >950 proteins from subnanogram bacterial and archaeal cell lysates. The present ultrasensitive LC-MS platform is expected to enable deep proteome coverage for subnanogram samples, including single mammalian cells.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2017

Nuclear Data Sheets for A=138☆

Experimental nuclear structure data from various reactions and decays are compiled and evaluated for all known nuclides with mass number A=138 (Sn,Sb,Te,I,Xe,Cs,Ba,La,Ce,Pr,Nd,Pm,Sm,Eu,Gd,Tb). Detailed evaluated nuclear structure information is presented for each reaction and decay and the best values combining all available data are recommended for level energies, half-lives, γ-ray energies and intensities, decay properties (energies, intensities and placement of radiations), and other spectroscopic properties. This work supersedes the previous full evaluation of A=138 by A.A. Sonzogni (2003So13).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Nuclear Data Sheets for A=50

The experimental nuclear structure and decay data are evaluated for all the known nuclides of mass 50 (Cl, Ar, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni). Here, detailed evaluated nuclear structure information are presented in individual decay and reaction datasets and the best values are recommended in the Adopted dataset for level properties, γ and β radiations, and other spectroscopic information. No excited states are known in 50Cl and 50Ni, and limited information is available for 50 Ar, 50 K, 50 Fe and 50 Co. This work supersedes earlier evaluations of A=50 by 2011El01, 1995Bu09, 1990Bu18, 1984Al29, and 1976Au07.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Nuclear Data Sheets for A=82☆

The evaluators present in this publication spectroscopic data and level schemes from radioactive decay and nuclear reactions studies for all isobars, Z=28(Ni) to Z=42(Mo), with mass number A=82. For {sup 82}Ni not much is known other than its identification. Most of the new data, i.e., after the last NDS (2003Tu03), are based on compilations included in XUNDL. Contributions of the various compilers in XUNDL are gratefully acknowledged.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Measurement of the top quark mass in the t t ¯ → dilepton channel from s = 8 TeV ATLAS data

The top quark mass is measured in the t¯t→dileptonchannel (lepton=e, μ) using ATLAS data recorded in the year 2012 at the LHC. The data were taken at a proton–proton centre-of-mass energy of √s=8TeVand correspond to an integrated luminosity of about 20.2fb-1. Exploiting the template method, and using the distribution of invariant masses of lepton–b-jetpairs, the top quark mass is measured to be mtop=172.99 ±0.41(stat)±0.74(syst)GeV, with a total uncertainty of 0.84GeV. Finally, acombination with previous ATLAS mtopmeasurements from √s=7TeVdata in the t¯t→dileptonand t¯t→lepton+jetschannels results in mtop=172.84 ±0.34(stat)±0.61(syst)GeV, with a total uncertainty of 0.70GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

From data to knowledge: The future of multi-omics data analysis for the rhizosphere

The rhizosphere is the interface between the root system of a plant and its surrounding soil. The microbiome of the rhizosphere, which is the totality of all microbes present there, represents a complex microbial ecosystem that nourishes the terrestrial biosphere. In order to untangle the complexity of the rhizosphere, and of the rhizospheric microbiome in particular, an integrated multi-omics approach can be applied to reveal the composition of the rhizospheric microbiome (through 16S ribosomal amplicons and metagenomics), the functional properties of the microbiome (through metatranscriptomics and metaproteomics), and the signaling network within the rhizosphere (through metametabolomics). The successful application of integrated multi-omics to rhizospheric science depends on the availability of rhizosphere-specific data and on the appropriate software used to analyze omics data from the rhizosphere. Here, we analyze the availability of software suites that are normally applied to surrogate disciplines (e.g., soil and plants) but which can be used for rhizospheric science. We also identify potential issues, challenges, and opportunities for rhizosphere science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Understanding the linkage between electric vehicle charging network coverage and charging opportunity using GPS travel data

Using GPS travel survey data from three metropolitan areas, this paper estimates the relationship between plug-in electric vehicle (PEV) public charging infrastructure coverage and charging opportunity. The former is defined as the percentage of public areas with charging stations installed and the latter represents the drivers probability of accessing a public charger at their trip stops (stop-based) or during at least one stop of their travel day (trip chain-based). Understanding this relationship is important to PEV manufacturers and electric vehicle supply equipment (EVSE) providers, as well as government agencies, for evaluating different levels of EVSE deployment and estimating the impact of its availability on PEV adoption. Heuristic procedures are developed to estimate stop- and trip chain-based charging opportunities. Charging opportunity reflects the visibility of EVSE as the incremental security that charging stations enable, while evaluating the potential of a driver to access EVSE at travel stops or during the day, for different levels of EVSE deployment on the transportation network. The metrics proposed do not assess the probability of completing trips with electric vehicles of a given range. The analysis results indicate that drivers’ trip destinations concentrate in a few popular places and EVSE deployment in these areas could achieve relatively high levels of charging opportunity. This finding is also robust to regional variations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology

We present a hydrogen/deuterium exchange workflow coupled to tandem mass spectrometry (HX-MS 2 ) that supports the acquisition of peptide fragment ions alongside their peptide precursors. The approach enables true auto-curation of HX data by mining a rich set of deuterated fragments, generated by collisional-induced dissociation (CID), to simultaneously confirm the peptide ID and authenticate MS 1 -based deuteration calculations. The high redundancy provided by the fragments supports a confidence assessment of deuterium calculations using a combinatorial strategy. The approach requires data-independent acquisition (DIA) methods that are available on most MS platforms, making the switch to HX-MS 2 straightforward. Importantly, we find that HX-DIA enables a proteomics-grade approach and wide-spread applications. Considerable time is saved through auto-curation and complex samples can now be characterized and at higher throughput. We illustrate these advantages in a drug binding analysis of the ultra-large protein kinase DNA-PKcs, isolated directly from mammalian cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Kalman filter data assimilation: Targeting observations and parameter estimation

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗