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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Application of machine learning for the estimation of electron energy distribution from optical emission spectra

Abstract This paper discusses the use of probabilistic deep neural networks for the prediction of the electron energy probability function in low-temperature non-thermal plasmas. The neural networks are trained using optical emission spectroscopy and Langmuir probe measurements, with the goal of providing a reliable estimate of the electron energy probability function solely from optical emission data. The performance of both non-Bayesian and Bayesian networks is evaluated. It is found that Bayesian models are preferable as they assign a higher level of uncertainty to their prediction especially when the dataset used to train them is small. This work describes one of the many potential applications of machine learning in plasma science and technology.

Physics↗
DOE OSTI2016

WE-H-207A-09: Theoretical Limits to Molecular Biomarker Detection Using Magnetic Nanoparticles

Purpose: Estimate the limits of molecular biomarker detection using magnetic nanoparticle methods like in vivo ELISA. Methods: Magnetic nanoparticles in an alternating magnetic field produce a magnetization that can be detected at exceedingly low levels because the signal at the harmonic frequencies is uniquely produced by the nanoparticles. Because the magnetization can also be used to characterize the nanoparticle rotational freedom, the bound state can be found. If the nanoparticles are coated with molecules that bind the desired biomarker, the rotational freedom reflects the biomarker concentration. The irreducible noise limit is the thermal noise or Johnson noise of the tissue and the contrast that can be measured must be larger than that limit. The contrast produced is a function of the applied field and depends strongly on nanoparticle volume. We have estimated the contrast using a Langevin function of a single composite variable to approximate the full stochastic Langevin equation for nanoparticle dynamics. Results: The thermal noise for a bandwidth reasonable for spectroscopy suggests mid zeptomolar (10–21) to low attomolar (10–18) concentrations can be measured in a volume that is 10cm in scale. The suggested sensitivity is far below the physiologically concentrations of almost all critical biomarkers including cytokines (picomolar), hormones (nanomolar) and heat shock proteins. Conclusion: The sensitivity of in vivo ELISA concentration measurements should be sufficient to measure physiological concentrations of critical biomarkers like cytokines in vivo. Further the sensitivity should be sufficient to measure concentrations of other biomarkers that are six to eight orders of magnitude lower in concentration than immune signaling molecules like cytokines. NIH - 1U54CA151662-01 Department of Radiology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Multi-variate joint PDF for non-Gaussianities: exact formulation and generic approximations

We provide an exact expression for the multi-variate joint probability distribution function of non-Gaussian fields primordially arising from local transformations of a Gaussian field. This kind of non-Gaussianity is generated in many models of inflation. We apply our expression to the non-Gaussianity estimation from Cosmic Microwave Background maps and the halo mass function where we obtain analytical expressions. We also provide analytic approximations and their range of validity. For the Cosmic Microwave Background we give a fast way to compute the PDF which is valid up to more than 7σ for f{sub NL} values (both true and sampled) not ruled out by current observations, which consists of expressing the PDF as a combination of bispectrum and trispectrum of the temperature maps. The resulting expression is valid for any kind of non-Gaussianity and is not limited to the local type. The above results may serve as the basis for a fully Bayesian analysis of the non-Gaussianity parameter.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Estimating the Benefits of Sustainable Aviation Fuel Usage at Chicago O’Hare International Airport on Ultrafine Particle Exposure and Mortalities Reductions

The expanding commercial aviation sector necessitates diverse energy sources, and sustainable aviation fuels (SAFs) have emerged as a promising option. Widespread SAF adoption can help meet transportation fuel demand and offer health benefits for people residing near airports or along airport landing and takeoff (LTO) pathways, where elevated levels of aircraft-derived air pollution often exist. Blending SAF with traditional jet fuels can reduce ultrafine particle (UFP) emissions, which may improve health of near airport population. We analyzed a population of about 8 million people in 1925 census tracts around the Chicago O’Hare International Airport (ORD). We conducted a risk assessment to estimate anticipated UFP reductions for three adoption scenarios using blends of traditional jet fuels with 5, 25, and 50% SAF across all flights landing and taking off from ORD. We calculated baseline estimates of UFP emissions using ORD flight data, a dispersion model, and a calibration function derived from mobile monitoring data. We used this baseline UFP emission profile across the study area to estimate population-weighted UFP, as well as the attributable case reductions (ACRs) and attributable mortality rate reductions (AMRRs) across the demographic distribution around the airport, based on the SAF blending scenarios. We found a positive association of SAF blending with UFP reductions, particularly near the airport and along LTO flight pathways. Our study showed that the population-weighted UFP across different demographics was similar. ACRs were largely dependent on individual demographic populations, while AMRRs for all populations were relatively similar, with an estimated 0.3 (95% range: 0.2−0.3), 1.1 (0.9−1.4), and 1.8 (1.5−2.2) fewer mortalities per 100,000 people per year expected with the adoption of 5, 25, and 50% SAF blends, respectively. This study indicates that communities near ORD, across a range of demographics, may benefit similarly from SAF adoption, thus highlighting how SAF adoption may offer an opportunity to improve health outcomes like aviation UFP-related mortalities around airports.

10 SYNTHETIC FUELS↗
DOE OSTI2021

ambrosia: An R package for calculating and analyzing food demand that is responsive to changing incomes and prices

The ambrosia R package was developed to calculate food demand for staples and non-staple commodities that is responsive to changing levels of incomes and prices. ambrosia implements the framework to quantify food demand as established by Edmonds et al. (Edmonds et al. 2017) and allows the user to explore and estimate different variables related to the food demand system. Currently ambrosia provides 3 main functions: (1) calculation of food demand for any given set of parameters including income levels and prices (2) estimation of parameters within a given a dataset. Note: ambrosia is used to calculate parameters for the food demand model implemented in the Global Change Analysis Model(GCAM) (3) exploration and preparation of raw data before starting a parameter estimation

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Methodology for deriving the telescope focus function and its uncertainty for a heterodyne pulsed Doppler lidar

Doppler lidars provide two measured parameters, radial velocity and signal-to-noise ratio, from which winds and turbulent properties are routinely derived. Attenuated backscatter, which gives quantitative information on aerosols, clouds, and precipitation in the atmosphere, can be used in conjunction with the winds and turbulent properties to create a sophisticated classification of the state of the atmospheric boundary layer. Calculating attenuated backscatter from the signal-to-noise ratio requires accurate knowledge of the telescope focus function, which is usually unavailable. Inaccurate assumptions of the telescope focus function can significantly deform attenuated backscatter profiles, even if the instrument is focused at infinity. Here, we present a methodology for deriving the telescope focus function using a co-located ceilometer for pulsed heterodyne Doppler lidars. The method was tested with Halo Photonics StreamLine and StreamLine XR Doppler lidars but should also be applicable to other pulsed heterodyne Doppler lidar systems. The method derives two parameters of the telescope focus function, the effective beam diameter and the effective focal length of the telescope. Additionally, the method provides uncertainty estimates for the retrieved attenuated backscatter profile arising from uncertainties in deriving the telescope function, together with standard measurement uncertainties from the signal-to-noise ratio. The method is best suited for locations where the absolute difference in aerosol extinction at the ceilometer and Doppler lidar wavelengths is small.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

THE SOURCE DENSITY AND OBSERVABILITY OF PAIR-INSTABILITY SUPERNOVAE FROM THE FIRST STARS

Theoretical models predict that some of the first stars ended their lives as extremely energetic pair-instability supernovae (PISNe). With energies approaching 10{sup 53} erg, these supernovae are expected to be within the detection limits of the upcoming James Webb Space Telescope (JWST), allowing observational constraints to be placed on the properties of the first stars. We estimate the source density of PISNe using a semi-analytic halo mass function based approach, accounting for the effects of feedback from star formation on the PISN rate using cosmological simulations. We estimate an upper limit of {approx}0.2 PISNe per JWST field of view at any given time. Feedback can reduce this rate significantly, e.g., lowering it to as little as one PISN per 4000 JWST fields of view for the most pessimistic explosion models. We also find that the main obstacle to observing PISNe from the first stars is their scarcity, not their faintness; exposures longer than a few times 10{sup 4} s will do little to increase the number of PISNe found. Given this, we suggest a mosaic style search strategy for detecting PISNe from the first stars. Even rather high-redshift PISNe are unlikely to be missed by moderate exposures, and a large number of pointings will be required to ensure a detection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Accelerating rates of cognitive decline and imaging markers associated with β-amyloid pathology

Objective: Our objective is to estimate points along the spectrum of β-amyloid pathology at which rates of change of several measures of neuronal injury and cognitive decline begin to accelerate. Methods: In 460 patients with mild cognitive impairment (MCI), we estimated the points at which rates of florbetapir PET, fluorodeoxyglucose (FDG) PET, MRI, and cognitive and functional decline begin to accelerate with respect to baseline CSF Aβ 42 . Points of initial acceleration in rates of decline were estimated using mixed-effects regression. Results: Rates of neuronal injury and cognitive and even functional decline accelerate substantially before the conventional threshold for amyloid positivity, with rates of florbetapir PET and FDG PET accelerating early. Temporal lobe atrophy rates also accelerate prior to the threshold, but not before the acceleration of cognitive and functional decline. Conclusions: A considerable proportion of patients with MCI would not meet inclusion criteria for a trial using the current threshold for amyloid positivity, even though on average, they are experiencing cognitive/functional decline associated with prethreshold levels of CSF Aβ 42 . Lastly, future trials in early Alzheimer disease might consider revising the criteria regarding β-amyloid thresholds to include the range of amyloid associated with the first signs of accelerating rates of decline.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Comment on “Tunneling-tip-induced collapse of the charge gap in the excitonic insulator Ta 2 NiSe 5 ”

In this study, we investigate the discrepancy between the estimate of Q. He [], who observed a remarkable collapse of the exciton gap in Ta 2 NiSe 5 due to the electrostatic field between the scanning tunneling microscope (STM) tip and the sample, and that of a recent angle-resolved photoemission spectroscopy investigation [C. Chen , ]. It is proposed that a critical factor contributing to this discrepancy is due to He 's assumption of a constant work function of the STM tip. This assumption led to an underestimation of the tip-induced electric field. Using a literature value for the sample work function, a more substantial electric field strength is obtained, which resolves the apparent conflict between the doping estimates of these two techniques. Furthermore, our findings highlight the importance of the STM tip condition, which can significantly impact the tip work function and, consequently, influence the doping estimation in experiments involving tip-induced electric fields. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

The quasar-LBG two-point angular cross-correlation function at z ∼ 4 in the cosmos field

In order to investigate the origin of quasars, we estimate the bias factor for low-luminosity quasars at high redshift for the first time. In this study, we use the two-point angular cross-correlation function (CCF) for both low-luminosity quasars at −24<M{sub 1450}<−22 and Lyman-break galaxies (LBGs). Our sample consists of both 25 low-luminosity quasars (16 objects are spectroscopically confirmed low-luminosity quasars) in the redshift range 3.1<z<4.5 and 835 color-selected LBGs with z{sub LBG}{sup ′}<25.0 at z ∼ 4 in the COSMOS field. We have performed our analysis for the following two quasar samples: (1) the spectroscopic sample (the 16 quasars confirmed by spectroscopy), and (2) the total sample (the 25 quasars including 9 quasars with photometric redshifts). The bias factor for low-luminosity quasars at z ∼ 4 is derived by utilizing the quasar-LBG CCF and the LBG auto-correlation function. We then obtain the 86% upper limits of the bias factors for low-luminosity quasars, which are 5.63 and 10.50 for the total and the spectroscopic samples, respectively. These bias factors correspond to the typical dark matter halo masses, log (M{sub DM}/(h{sup −1}M{sub ⊙})) = 12.7 and 13.5, respectively. This result is not inconsistent with the predicted bias for quasars that is estimated by the major merger models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Trends in Carbon, Oxygen, and Nitrogen Core in the X-ray Absorption Spectroscopy of Carbon Nanomaterials: A Guide for the Perplexed

Successful deployment of carbon nanomaterials in many applications, such as sensing, energy storage, and catalysis, relies on the selection, synthesis, and tailoring of the surface properties. Predictive analysis of the behavior is difficult without detailed knowledge of the differences between various carbon nanomaterials and their surface functionalization, thus leaving the selection process to traditional trial-and-error work. The present characterization fills this knowledge gap for carbon nanomaterial surface properties with respect to chemical states and functionalization. We present an overview of the chemical trends that can be extracted from soft X-ray absorption spectroscopy (XAS) spectra on an extended set of nonideal carbon nanomaterials as a function of sp 2 bonded carbon and bond ordering. In particular, the surface chemical state, the presence of long-range order in the carbon matrix, and a qualitative estimation of the amount of oxygen and nitrogen and their respective functional group formation on the material surface, together with the detailed material fabrication parameters, are reported. The results expand our understanding of carbon nanomaterial functionalization, which can support material selection in practice, provided that the specifications of the application are known.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach

Purpose: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. Methods and Materials: We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. Results: The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). Conclusion: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

SPRUCE Root Production Assessed with Manual Minirhizotrons Resolved to Plant Functional Type, 2015-2021

This dataset contains raw root length and diameter for individual roots and estimated root population production measurements from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Measurements started at the beginning of whole ecosystem warming manipulations in 2015 through 2021 (2015-05-26 to 2021-09-01). Root morphology and estimated production were quantified throughout the peat profile with manual minirhizotrons deployed within SPRUCE plots. Images were processed using commercial software to quantify the length and diameter of individual roots. Roots were visually assigned to a plant functional type (PFT) of either (ericaceous) shrub, herb (sedges and Maianthemum trifolium), or tree (Larix laricina, Picea mariana) based on expert opinion. The biomass of individual roots was estimated using PFT-specific allometric equations (Iversen et al., 2018). Production per day was estimated as the length of new roots produced between imaging sessions, divided by the number of days between imaging sessions. These values were placed on a m2 aboveground area basis and scaled to a standard depth of 1m (roots are not evenly distributed with depth, do not interpret value as being on a m3 basis). Maximum and average (weighted by production length) depth of each PFT were also estimated within each minirhizotron tube. Annual production was interpolated as the average of four methods to scale these data (see Weber et al, 2026). Standing crop of roots was estimated for each tube as the maximum visible amount (both length and mass) of roots of that PFT for that year. These data expand the ability of researchers to accurately estimate the belowground dynamics of peatland vegetation, as well as the role that fine roots may play in impacting the fluxes of carbon within peatlands. This dataset contains three data files in comma-separate values (*.csv) format. This dataset contains one data file in comma-separate values (.csv) format. Additional metadata are provided: three data dictionaries and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Short-depth circuits for efficient expectation-value estimation

The evaluation of expectation values Tr[$\rho \mathcal{O}$] for some pure state $\rho$ and Hermitian operator $\mathcal{O}$ is of central importance in a variety of quantum algorithms. Near-optimal techniques have been developed in the past and require a number of measurements N approaching the Heisenberg limit N = O(1/ ε) as a function of target accuracy ε. The use of quantum phase estimation (QPE) requires, however, long circuit depths C = O(1/ ε) making its implementation difficult on near-term noisy devices. The more direct strategy of operator averaging is usually preferred as it can be performed using N = O(1/ε 2 ) measurements and no additional gates aside from those needed for the state preparation. In this work we use a simple but realistic model to describe the bound state of a neutron and a proton (the deuteron) to show that the latter strategy can require an overly large number of measurements in order to achieve a prefixed relative target accuracy ε r . Further, we propose to overcome this problem using a single step of QPE and classical postprocessing. This approach leads to a circuit depth C = O (ε μ ) (with μ ≥ 0) and to a number of measurements N = O (1/ε 2 + ν ) for 0 < ν ≤ 1 and a much smaller prefactor. We provide detailed descriptions of two implementations of our strategy for ν = 1 and ν ≈ 0.5 and derive appropriate conditions that a particular problem instance has to satisfy in order for our method to provide an advantage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Visualization of Current and Mapping of Elements in Quantum Dot Solar Cells

The delicate influence of properties such as high surface state density and organic-inorganic boundaries on the individual quantum dot electronic structure complicates pursuits toward forming quantitative models of quantum dot thin films ab initio. Our report describes the application of electron beam-induced current (EBIC) microscopy to depleted-heterojunction colloidal quantum dot photovoltaics (DH-CQD PVs), a technique which affords one a map of current production within the active layer of a PV device. The effects of QD sample size polydispersity as well as layer thickness in CQD active layers as they pertain to current production within these PVs are imaged and explained. The results from these experiments compare well with previous estimations, and confirm the ability of EBIC to function as a valuable empirical tool for the design and betterment of DH-CQD PVs. Lastly, extensive and unexpected PbS QD penetration into the mesoporous TiO 2 layer is observed through imaging of device cross sections by energy-dispersive X-ray spectroscopy combined with scanning transmission electron microscopy. Finally, the effects of this finding are discussed and corroborated with the EBIC studies on similar devices.

14 SOLAR ENERGY↗
DOE OSTI2020

The Lorentzian inversion formula and the spectrum of the 3d O(2) CFT

We study the spectrum and OPE coefficients of the three-dimensional critical O(2) model, using four-point functions of the leading scalars with charges 0, 1, and 2 ( s , $\phi$, and t ). We obtain numerical predictions for low-twist OPE data in several charge sectors using the extremal functional method. We compare the results to analytical estimates using the Lorentzian inversion formula and a small amount of numerical input. We find agreement between the analytic and numerical predictions. We also give evidence that certain scalar operators lie on double-twist Regge trajectories and obtain estimates for the leading Regge intercepts of the O(2) model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Search for resonances in events with photon and jet final states in proton-proton collisions at $ \sqrt{s} $ = 13 TeV

A search for resonances in events with the γ+jet final state has been performed using proton-proton collision data collected at $\sqrt s$ = 13 TeV by the CMS experiment at the LHC. The total data analyzed correspond to an integrated luminosity of 138 fb –1 . Models of excited quarks and quantum black holes are considered. Using a wide-jet reconstruction for the candidate jet, the γ+jet invariant mass spectrum measured in data is examined for the presence of resonances over the standard model continuum background. The background is estimated by fitting this mass distribution with a functional form. The data exhibit no statistically significant deviations from the expected standard model background. Exclusion limits at 95% confidence level on the resonance mass and other parameters are set. Excited light-flavor quarks (excited bottom quarks) are excluded up to a mass of 6.0 (3.8) TeV. Quantum black hole production is excluded for masses up to 7.5 (5.2) TeV in the Arkani-Hamed-Dimopoulos-Dvali (Randall-Sundrum) model. These lower mass bounds are the most stringent to date among those obtained in the γ+jet final state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗