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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

ESS-DIVE guidelines for archiving terrestrial model data

This dataset contains supporting documents and images for ESS-DIVE terrestrial model data archiving guidelines.Terrestrial models are broadly defined as numerical models that couple both land dynamics and energy, water, carbon, or nutrient fluxes. We created these guidelines based on input from the U.S. Department of Energy’s Biological and Environmental Research land modeling community. The guidelines are intended to help modelers determine which components of their terrestrial model data associated with publication should be archived. Based on input from the land modeling community, the guidelines recommend archiving both model input and testing data, as well as code, script, and metadata. The guidelines also recommend archiving model data output, depending on the limitations set by data repositories. Lastly, we provide recommendations for bundling data files for publication as well as a discussion about tools that can facilitate model data archiving and reuse.This dataset is an archive of the associated GitHub repository for our model archiving guidelines (https://github.com/ess-dive-community/essdive-model-data-archiving-guidelines). The ‘README.pdf’ file gives a general introduction to the guidelines, and the ‘instructions.pdf’ file provides more detailed steps for following the guidelines. We also provide 2 figures in this data package: 1) a decision tree (model_data_guidelines_decision_tree.png) that can help users determine which components of their model data to archive. and 2) the ‘model_data_guidelines_flmd.png’ file depicts the different files that can be archived in addition to the model data itself. Lastly, we include 3 digitized tables from our associated manuscript and 3 CSV files with anonymized input from DOE scientists about the importance of different aspects of model data archiving from which we developed the guidelines.Dataset updates for v1.1.0: We updated this data package on 2021-11-22 in response to review comments on our related manuscript. In this update we removed one figure so that the model archiving guidelines are conveyed in text rather than an image. We updated the file-level metadata (FLMD) figure to be in accord with the most recent FLMD recommendations. We made minor edits to the README file to update the recommended citation and added two co-authors. We also added 6 new data files (3 are anonymized input from DOE scientists that helped to inform guidelines, and 3 are digitized tables from our manuscript.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Simultaneous prediction of structural properties in epitaxially–grown GaN with quantum and conventional multi–output learning algorithms

Hundreds of GaN thin film crystal plasma–assisted molecular beam epitaxy synthesis experiment records spanning two decades were organized into a dataset correlating the growth experiment design parameters with discrete, binary determinations of crystallinity and surface morphology. Conventional data science techniques as well as both quantum and classical multi–output supervised machine learning algorithms were implemented to investigate the relationships between the operating parameter data and the structural figures of merit. Correlation coefficients, decision tree nodes, p–values, and SHAP values all support substrate temperature and gallium effusion cell conditions as being statistically significant for simultaneously influencing GaN crystallinity and surface morphology. Here, a conventional deep neural network learned best from the data, followed by a quantum–classical hybrid gradient boosting algorithm. When combined with calculations of uncertainty intervals based on VennAbers predictors, machine learning predictions of both structural properties show good agreement with results reported in published experimental literature.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Innovations in Voluntary Renewable Energy Procurement: Methods for Expanding Access and Lowering Cost for Communities, Governments, and Businesses (Technical Report)

This guide explores five innovative options for voluntarily procuring renewable energy generation or systems. The options and their descriptions are shown in Table 1. These methods can be replicated by a variety of stakeholders—including local governments, not-for-profit organizations, businesses, and utilities. The three decision trees in Figure 1 indicate which method may best suit a particular stakeholder’s needs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Search for a Higgs-like boson decaying to two photons in $p\bar{p}$ collisions at $\sqrt{s} = 1.96$ TeV

We present a search for the Standard Model (SM) Higgs boson decaying to two photons using the 9.6 fb -1 of p$\bar{p}$ collisions at √s = 1.96 TeV collected by the D0 detector at the Tevatron from July 2002 to September 2011. The data corresponds to the complete data-set in the RunII period of the Tevatron. This thesis employs a multivariate technique, boosted decision trees, to optimize the discrimination of signal from background. No significant excess of data above theoretical prediction is observed, so limits on the ratio of the production cross section times the branching ratio to the SM value are set at 95% confidence level as a function of hypothetical Higgs boson mass. The expected and observed limits on this ratio are 8.7 and 12.8 respectively at 125 GeV. We also interpret the data in the fermiophobic Higgs model. The result has reached a sensitivity of 114 GeV, and we set a lower limit on the fermiophobic Higgs mass of M Hf > 113 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Search for the Standard Model Higgs Boson in ZH → μ+μ-b$\bar{b}$ Production at DØ and Evidence for the H→ b$\bar{b}$ Decay at the Tevatron

search for ZH → μ+μ-b$\bar{b}$ is presented, using a Run 2 dataset with an integrated luminosity of 9.7 fb -1 collected by the DØ detector. Selected events contain at least two reconstructed jets and a Z candidate reconstructed with two opposite-sign charged muons. Random forests of decision trees are trained to distinguish between signal and background events in two orthogonal b-tag samples. The ZH → μ+μ-b$\bar{b}$b analysis is then combined with ZH → e+e-b$\bar{b}$ analysis. For the combined results of ZH → ℓ+ℓ-b$\bar{b}$b, no Higgs signal is observed, limits are set on the ZH cross-section BR(H→ b$\bar{b}$) for different Higgs masses, from 90 to 150 GeV. For a Standard Model (SM) Higgs boson of mass 125 GeV, the observed cross-section limit is 7.1 times the SM cross-section with an expected sensitivity of 5.1 times the SM cross section. The result of ZH → ℓ+ℓ-b$\bar{b}$b channel has been combined with searches in other Higgs decay channels at the Tevatron, which led to the first evidence of H → b$\bar{b}$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Search for the standard model higgs boson in eτ final states

Presented in this dissertation is a search for the standard model (SM) Higgs boson using the DØ detector at Fermilab in Batavia, IL. The SM is a fantastically accurate theory describing the fundamental interactions and particles of the Universe. The only undiscovered particle in the SM is the Higgs boson, which is hypothesized to be responsible for electroweak symmetry breaking and giving mass to all other particles. Considered in this search is the process H + X → eτ h jj, where e is an electron, τ h is the hadronic decay of a tau, and j is a jet, using p $\bar{p}$ collisions at center of mass energy√s = 1.96 TeV. This search includes three production modes: associated production, gluon fusion and vector boson fusion. It also utilizes two decay channels: H→ ττ and H → WW. A new technique, dubbed the Global Boosted Decision Tree, is introduced which offers a means of providing continuity to a multivariate search as a function of a particular parameter, in this case, the mass of the Higgs boson. The observed (expected) limit on the ratio of cross section times branching fraction to the SM at 95% confidence level is 14.6 (16.0) at m H = 125 GeV. This result is combined with the related channel H + X → μτ h jj and produced an observed (expected) limit of 9.0 (11.3) at m H = 125 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Measurement of the Forward-Backward Asymmetry in the Production of $B^{\pm}$ Mesons in $p\bar{p}$ Collisions.

We present a measurement of the forward-backward asymmetry in the production of B ± mesons, A FB (B ± ), using B ± → J/ψK ± decays in 10.4 fb -1 of p$\bar{p}$ collisions at √ s = 1.96 TeV collected by the DØ experiment during Run II of the Tevatron collider. A nonzero asymmetry would indicate a preference for a particular flavor, i.e., b quark or $\bar{b}$ antiquark, to be produced in the direction of the proton beam. We extract A FB (B ± ) from a maximum likelihood fit to the difference between the numbers of forward- and backward-produced B ± mesons, using a boosted decision tree to reduce background. Corrections are made for reconstruction asymmetries of the decay products. We measure an asymmetry consistent with zero: A FB (B ± ) = [-0.24 ± 0.41 (stat)± 0.19 (syst)]%. The standard model estimate from next-to-leading-order Monte Carlo is A SM FB (B ± ) = [2.31 ± 0.34 (stat.)± 0.51 (syst.)]%. There is a difference of ≈ 3 standard deviations between this prediction and our result, which suggests that more rigorous determination of the standard model prediction is needed to interpret these results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Factors Affecting Route Selection and Survival of Steelhead Kelts at Snake River Dams in 2012 and 2013

In 2012 and 2013, Pacific Northwest National Laboratory (PNNL) conducted a study that summarized the passage route proportions and route-specific survival rates of steelhead kelts that passed through Federal Columbia River Power System (FCRPS) dams. To accomplish this, a total of 811 steelhead kelts were tagged with Juvenile Salmon Acoustic Telemetry System (JSATS) transmitters. Acoustic receivers, both autonomous and cabled, were deployed throughout the FCRPS to monitor the downstream movements of tagged kelts. Kelts were also tagged with passive integrated transponder tags to monitor passage through juvenile bypass systems (JBS) and detect returning fish. The current study evaluated data collected in 2012 and 2013 to identify environmental, temporal, operational, individual, and behavioral variables that were related to forebay residence time, route of passage, and survival of steelhead kelts at FCRPS dams on the Snake River. Multiple approaches, including 3-D tracking, bivariate and multivariable regression modeling, and decision tree analyses were used to identify the environmental, temporal, operational, individual, and behavioral variables that had the greatest effect on forebay residence time, route of passage, and route-specific and overall dam passage survival probabilities for tagged kelts at Lower Granite (LGR), Little Goose (LGS), and Lower Monumental (LMN) dams. In general, kelt behavior and discharge appeared to work independently to affect forebay residence times. Kelt behavior, primarily approach location, migration depth, and “searching” activities in the forebay, was found to have the greatest influence on their route of passage. The condition of kelts was the single most important factor affecting their survival. The information gathered in this study may be used by dam operators and fisheries managers to identify potential management actions to improve in-river survival of kelts or collection methods for kelt reconditioning programs to aid the recovery of Snake River steelhead populations.

13 HYDRO ENERGY↗
DOE OSTI2013

SEARCH FOR THE STANDARD MODEL HIGGS BOSON IN ITS ASSOCIATED PRODUCTION WITH A W VECTOR BOSON IN pp COLLISIONS AT p s = 1:96TEV

In this dissertation, results from a search for the Standard Model (SM) Higgs boson is shown. The SM is the theoretical framework which describes particles of matter and force carrier gauge bosons. To solve the mass problem in the SM, the Higgs mechanism was introduced in 1963. The Higgs mechanism causes an electroweak symmetry breaking and a new massive scalar boson was postulated. This particle is the Higgs boson. A search for the Higgs boson has been ongoing at the Tevatron where protons and antiprotons were allowed to collide at a center-of-mass energy of 1:96 TeV. For a low mass Higgs, that is a Higgs with a mass lower than 135 GeV, the dominant decay mode is Higgs to a pair of b-quarks (H ! bb ). This work concentrated on a Higgs whose mass is in the range of 100 150 GeV, with a W vector boson produced with the Higgs boson. The final state chosen is the one which contains a lepton a neutrino and a pair of b-quarks. This study used data provided by the DZERO experiment. Results presented here are the outcome of analyzing 5:3 fb􀀀1 of data from RunII period. The analysis used different techniques to increase the sensitivity of the study. Data were subdivided based on lepton flavor, number of jets in sample, jets identified as b-jets and dates of collected data. A multivariate analysis technique based on boosted decision trees were used to separate signal from background processes, physical and instrumental. A good agreement between data and simulated events was observed. An observed (expected) upper limit of 4:5 (4:8) for a Higgs of mass 115 GeV was set on the ratio of the Higgs production to its decay branching ratio at the 95% confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Vehicle Modeling for use in the CAFE model: Process description and modeling assumptions

The objective of this project is to develop and demonstrate a process that, at a minimum, provides more robust information that can be used to calibrate inputs applicable under the CAFE model’s existing structure. The project will be more fully successful if a process can be developed that minimizes the need for decision trees and replaces the synergy factors by inputs provided directly from a vehicle simulation tool. The report provides a description of the process that was developed by Argonne National Laboratory and implemented in Autonomie.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

Search for the Higgs Boson Produced in Association with a Vector Boson Using Like-Sign Dilepton Events in 1.96-TeV Proton-Antiproton Collisions

This thesis presents a search for the standard model and fermiophobic Higgs bosons produced in association with a vector boson using like-sign dilepton events collected by Collider Detector at Fermilab (CDF II). This analysis uses the data corresponding to an integrated luminosity of 9.4 fb -1 . The search is characterized by minimally biased cuts which maximize the acceptance of the Higgs bosons. The numbers of expected signal events passing requirements for like-sign dilepton are 5.6 for the fermiophobic Higgs on the mass of 110 GeV/c 2 assuming the standard model production cross section, and 1.7 for the standard model on the mass of 160 GeV/c 2 . The expectation of background events are 696.1, while the number of observed events are 624. To discriminate signal events from backgrounds, the search employs a multivariate analysis based on boosted decision trees. There are no significant disagreements in the BDT outputs, then, upper limits at a 95% confidence level are set on σ(p$\bar{p}$→V h) × B(h→WW ) for both Higgs bosons scenarios. The expected and observed limits are 5.9 and 9.2, respectively, relative to the standard model expectation for the standard model Higgs boson mass of 160 GeV/c 2 . For the fermiophobic Higgs boson corresponding to the mass of 110 GeV/c 2 , the expected and observed limits are 2.6 and 4.4, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Measurement of the E Polarization Observable for yd --> pi^-p(p_s), yd-->K^0Lambda(p_s), and yd-->pi^+pi^-d(0) using CLAS g14 data at Jefferson Lab

Photoproduction of mesons from the nucleon has a long and ongoing tradition for exploring nucleon excitations and the baryon-baryon interaction. Polarization observables which play a role in the photoproduction mechanism are, therefore, essential in addition to the differential cross section. The CLAS collaboration at Jefferson Lab, has been active in measuring these observables, but until now only on a proton targets. However, a comprehensive picture of the pseudoscalar meson photoproduction requires neutron data as well. That is, paired measurements of observables in p and n reactions are necessary to disentangle the photoproduction mechanism on the basis of isospin I = 0, and I = 1 photo-coupling transition amplitudes. The g14 experiment with 'HDIce,' a longitudinally polarized solid target of molecular hydrogen-deuteride with low background contamination from other nuclear species, provided an unique opportunity to measure several polarization observables|for the first time|on the neutron for different channels. In particular, we present our measurements of the E beam-target polarization observable, which requires circularly polarized beam and a longitudinally polarized target, for p pi^-, K^0Lambda, and K^0Sigma^0 channels in the energy range of 1.5 lte W lte 2.3 GeV. In addition, we also utilized the g14 dataset to investigate the intrinsic spin of a possible dibaryonic ND bound state by measuring the E (beam-target) observable on the d-pi^+/-d channel of the reaction yd --> pi^+pi^-d(0). Finally, this thesis also discusses a highly efficient multivariate analysis method called Boosted Decision Trees, which we employed extensively for this work and which has not been used before in CLAS data analysis.

Ho, Dao↗
DOE OSTI2018

Neutral Current Elastic Scattering and the Strange Spin Structure of the Proton

Neutrinos can be used as a unique and informative probe of the structure within nucleons. This thesis presents the tools and methodology for studying the strange quark spin in the nucleon through neutral current elastic neutrino-proton scattering in the MicroBooNE experiment located at the Fermi National Accelerator Lab. An automated boosted decision tree based proton identification algorithm is used to select proton in liquid argon TPC data with a 70\% efficiency. After a set of protons are selected, a logistic regression model is used to select neutral current elastic proton interactions in MicroBooNE. Neutral current elastic proton interactions are selected with an 11\% efficiency and 30\% purity. The number of selected events measured in MicroBooNE data as a function of $Q^2$ is compared directly to the number of selected events in MicroBooNE simulation. An event reweighting scheme is used to vary the expected number of eventsin MicroBooNE simulation based on the values o f the physics parameters of interest. The likelihood for any given value of the physical parameters can then be easily calculated and the probability distributions of the physics parameters can be sampled using Markov Chain Monte Carlo. Future improvements to the detector physics models in MicroBooNE are required for a precise determination of the strange quark spin structure in the proton. With the current level of uncertainty on the detector physics models, we are only able to constrain the net strange quark spin, $\Delta s$ to the range $-1.8 < \Delta s < 3.8$ with 95\% confidence.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

A Scalable HPC Insider Threat Monitoring System

Cyber Equations, Inc. is reporting on the results of its DOE SBIR Phase I project for the development of a Scalable HPC Insider Threat Monitoring System. This final report to the Department of Energy for Task DE-FOA-0001618 covers the period from 02/21/2017 to 02/20/2018 and summarizes the larger research accomplishments, which can be divided in theoretical and laboratory experiments. One of the many challenges for the U.S. Department of Energy (DOE) is the support of the high-performance computing (HPC) centers while providing quality information services, strategic vision, leadership, technical expertise, and capabilities necessary to enable the DOE to deliver effective, efficient, economical, and secure HPC services. In order to realize a secure DOE laboratory enterprise, DOE facilities operations must counter a wide range of cyber insider threats, from unsophisticated adversaries up to advanced, nation state sponsored actors. The DOE laboratories also presents an enormous insider attack surface of an ever-growing number of computers, networks, and devices all running a myriad of software. In addition, the DOE laboratories are connected to a variety of external organizations which multiplies the possibility of insider threat. During this SBIR project Cyber Equations developed a strategic combination of supervised and unsupervised machine learning techniques to control scale and classify whether entities, such as hosts, actors, or documents, are showing suspicious insider behavior. We conducted experiments using real cyber data to show that changes in observable behavior (e.g., changes in outbound traffic patterns) are indicative of attacks executing (e.g., insiders exfiltrating data, bots calling home, or an attack spreading). The features and methods we use, have been used in the anomaly detection research and development community for a significant amount of time and robust implementations are openly available. K-Means clustering, Decision Trees, and Support Vector Machine based classifiers are readily available through the R language and open source H20 machine learning framework. The Cyber Equations approach is novel in its use of these classifiers combined with Hadoop based parallel processing technology. The five stages of processing used by CyberInsider improve scalability and reduce the high number of false positives normally associated with network anomaly detection. In summary, the Cyber Equations has successfully accomplished all of its Phase I tasks and demonstrated the feasibility of the Insider Threat Monitoring System.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Improving Physics Based Electron Neutrino Appearance Identication with a Long Short-Term Memory Network

The NOvA experiment is a long baseline neutrino oscillation experiment with the objective to measure the oscillation probability of muon type neutrinos ($\nu_{\mu}$) into electron type neutrinos ($\nu_e$). NOvA measures the interactions of neutrinos from the NuMI beam in two functionally identical liquid scintillator detectors. The far detector detects the appearance of electron neutrinos, leading to measurement of the oscillation parameters under study. Using an off -axis beam with an 810 km baseline length, NOvA is sensitive to measuring the neutrino mass hierarchy, the CP violating parameter, and the octant of the mixing angle, $\theta_{23}$. The data presented in this thesis has been collected from October 2013 until May 2018. The first NOvA $\nu_e$ charged current identifier utilized an artificial neural network with the physical features of the highest energy reconstructed shower as inputs. The $\nu_ e$ charged current identifier in this thesis utilizes a Long Short-Term Memory network with the physical features of every reconstructed shower in a particular interaction. In addition to the Long Short-Term Memory network, there are two Boosted Decision Trees to assist in event level selection. In the analysis of the data, 54 $\nu_e$ candidate events were detected with an expected background of 15 events. The results of this analysis prefer the normal mass hierarchy with maximal mixing and $\frac{\delta_{CP}}{\pi}=1.92_{-1.19}^{0.08}$. Results from this analysis differ from the published NOvA analysis, due to the differences of electron identification techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Application of artificial neural network to prompt gamma neutron activation analysis for chemical warfare agents identification

The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical warfare agents (CWA) non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a mechanically-cooled high-purity germanium (HPGe) detector, and gamma-ray peak analysis provides input to its chemical identification logic with a probabilistic decision tree (PDT). The effectiveness of the chemical identification algorithm is determined by the availability of a wide range of data to train the algorithm to identify chemical-fills with accuracy. INL has a collection of gamma-ray spectra of various chemical-fills from the field-deployed PINS systems over the years, and it was envisaged to leverage such a database with the Artificial Neural Network (ANN) technique. Therefore, an ANN-based chemical identification algorithm was developed as an independent verification of the current algorithm. The ANN-based algorithm’s performance was evaluated against the U.S. Army blind test data, and results were presented and discussed in this study.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

A Detailed Vehicle Simulation Process to Support CAFE and CO 2 Standards (MY 2021–2026 Final Rule Analysis)

In 1975, Congress passed the Energy Policy and Conservation Act (EPCA), requiring standards for corporate average fuel economy (CAFE), and charging the U.S. Department of Transportation (DOT) with the establishment and enforcement of these standards. The Secretary of Transportation has delegated these responsibilities to the National Highway Traffic Safety Administration (NHTSA). NHTSA has contracted the DOT Volpe National Transportation Systems Center (Volpe Center) to provide analytical support for NHTSA’s regulatory and analytical activities related to fuel economy standards. Unlike long-standing safety and criteria pollutant emissions standards, fuel economy standards apply to manufacturers’ overall fleets rather than to individual vehicle models. In developing the standards, NHTSA made use of the CAFE Compliance and Effects Modeling System (the “CAFE model”), which was developed by DOT’s Volpe Center for the 2005-2007 CAFE rulemaking and has been continually updated since. The model is the primary tool used by the agency to evaluate potential CAFE stringency levels by applying technologies incrementally to each manufacturer’s fleet until the requirements under consideration are met. The CAFE model relies on numerous technology-related and economic inputs such as market forecasts and technology cost and effectiveness estimates; these inputs are categorized by vehicle classification, technology synergies, phase-in rates, cost learning curve adjustments, and technology “decision trees.” The Volpe Center assists NHTSA in the development of the engineering and economic inputs to the CAFE model by analyzing the application of potential technologies to the current automotive industry vehicle fleet to determine the feasibility of future CAFE standards, the associated costs, and the benefits of the standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Optimizing Classifiers for Radionuclide Identification

Identifying threat nuclear materials is a critical for the prevention of acts of nuclear terrorism on the homeland. For this purpose, many radionuclide identification devices are deployed in the field. However, these will not necessarily be in the hands of non-experts, therefore these devices need to provide ready-made answers for the personnel in the field. This is where advanced algorithms are employed to both interpret the data and provide the identification of the nuclear material being interrogated. We took a machine learning approach to identification, by using training and validation data sets to create and optimize classifiers which determine which radionuclide is consistent with the data. The classifiers investigated were the Random Forest, Decision Tree, Support Vector Machine, and XG Boost and their performance was judged using the F1 Score for both hyperparameter tuning and comparison. In the end, we found out that the Random Forest Classifier worked the best based off the F1 Score they got which was 0.98.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗