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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Star formation rate and extinction in faint z ∼ 4 Lyman break galaxies

We present a statistical detection of 1.5 GHz radio continuum emission from a sample of faint z ∼ 4 Lyman break galaxies (LBGs). To constrain their extinction and intrinsic star formation rate (SFR), we combine the latest ultradeep Very Large Array 1.5 GHz radio image and the Hubble Space Telescope Advanced Camera for Surveys (ACS) optical images in the GOODS-N. We select a large sample of 1771 z ∼ 4 LBGs from the ACS catalog using B {sub F435W}-dropout color criteria. Our LBG samples have I {sub F775W} ∼ 25-28 (AB), ∼0-3 mag fainter than M{sub UV}{sup ⋆} at z ∼ 4. In our stacked radio images, we find the LBGs to be point-like under our 2'' angular resolution. We measure their mean 1.5 GHz flux by stacking the measurements on the individual objects. We achieve a statistical detection of S {sub 1.5} {sub GHz} = 0.210 ± 0.075 μJy at ∼3σ for the first time on such a faint LBG population at z ∼ 4. The measurement takes into account the effects of source size and blending of multiple objects. The detection is visually confirmed by stacking the radio images of the LBGs, and the uncertainty is quantified with Monte Carlo simulations on the radio image. The stacked radio flux corresponds to an obscured SFR of 16.0 ± 5.7 M {sub ☉} yr{sup –1}, and implies a rest-frame UV extinction correction factor of 3.8. This extinction correction is in excellent agreement with that derived from the observed UV continuum spectral slope, using the local calibration of Meurer et al. This result supports the use of the local calibration on high-redshift LBGs to derive the extinction correction and SFR, and also disfavors a steep reddening curve such as that of the Small Magellanic Cloud.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Redshift evolution and covariances for joint lensing and clustering studies with DESI Y1

ABSTRACT Galaxy–galaxy lensing (GGL) and clustering measurements from the Dark Energy Spectroscopic Instrument Year 1 (DESI Y1) data set promise to yield unprecedented combined-probe tests of cosmology and the galaxy–halo connection. In such analyses, it is essential to identify and characterize all relevant statistical and systematic errors. We forecast the covariances of DESI Y1 GGL + clustering measurements and the systematic bias due to redshift evolution in the lens samples. Focusing on the projected clustering and GGL correlations, we compute a Gaussian analytical covariance, using a suite of N-body and lognormal simulations to characterize the effect of the survey footprint. Using the DESI one percent survey data, we measure the evolution of galaxy bias parameters for the DESI luminous red galaxy (LRG) and bright galaxy survey (BGS) samples. We find mild evolution in the LRGs in $0.4 < z < 0.8$, subdominant to the expected statistical errors. For BGS, we find less evolution for brighter absolute magnitude cuts, at the cost of reduced sample size. We find that for a redshift bin width $\Delta z = 0.1$, evolution effects on DESI Y1 GGL is negligible across all scales, all fiducial selection cuts, all fiducial redshift bins. Galaxy clustering is more sensitive to evolution due to the bias squared scaling. Nevertheless the redshift evolution effect is insignificant for clustering above the 1-halo scale of $0.1h^{-1}$ Mpc. For studies that wish to reliably access smaller scales, additional treatment of redshift evolution is likely needed. This study serves as a reference for GGL and clustering studies using the DESI Y1 sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

MC21 analysis of the nuclear energy agency Monte Carlo performance benchmark problem

Due to the steadily decreasing cost and wider availability of large scale computing platforms, there is growing interest in the prospects for the use of Monte Carlo for reactor design calculations that are currently performed using few-group diffusion theory or other low-order methods. To facilitate the monitoring of the progress being made toward the goal of practical full-core reactor design calculations using Monte Carlo, a performance benchmark has been developed and made available through the Nuclear Energy Agency. A first analysis of this benchmark using the MC21 Monte Carlo code was reported on in 2010, and several practical difficulties were highlighted. In this paper, a newer version of MC21 that addresses some of these difficulties has been applied to the benchmark. In particular, the confidence-interval-determination method has been improved to eliminate source correlation bias, and a fission-source-weighting method has been implemented to provide a more uniform distribution of statistical uncertainties. In addition, the Forward-Weighted, Consistent-Adjoint-Driven Importance Sampling methodology has been applied to the benchmark problem. Results of several analyses using these methods are presented, as well as results from a very large calculation with statistical uncertainties that approach what is needed for design applications. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1974

On evaluating compliance with air pollution levels 'not to be exceeded more than once a year'

Under a sampling program operated in conformity with EPA guidelines, calculational procedures are described for estimating the corresponding ambient pollution level. The statistical independence of the data, the commutativity of expectation and exponential operations, and the statistical variability of the calculated results are quantitatively evaluated. It is shown that the recommended sampling frequencies of every third to every sixth day are inadequate in determining conformity when the standards are expressed as levels not to be exceeded more than once a year.

Neustadter, H. E.↗
NASA NTRS1987

Analysis models for the estimation of oceanic fields

A general model for statistically optimal estimates is presented for dealing with scalar, vector and multivariate datasets. The method deals with anisotropic fields and treats space and time dependence equivalently. Problems addressed include the analysis, or the production of synoptic time series of regularly gridded fields from irregular and gappy datasets, and the estimate of fields by compositing observations from several different instruments and sampling schemes. Technical issues are discussed, including the convergence of statistical estimates, the choice of representation of the correlations, the influential domain of an observation, and the efficiency of numerical computations.

Carter, E. F.↗
DOE OSTI2012

Multiscale simulation on a light-harvesting molecular triad

Here, we have investigated the effect of solvation and confinement on an artificial photosynthetic material, carotenoid-porphyrin-C60 molecular triad, by a multiscale approach and an enhanced sampling technique. We have developed a combined approach of quantum chemistry, statistical physics, and all-atomistic molecular dynamics simulation to determine the partial atomic charges of the ground-state triad. To fully explore the free energy landscape of triad, the replica exchange method was applied to enhance the sampling efficiency of the simulations. The confinement effects on the triad were modeled by imposing three sizes of spherocylindrical nanocapsules. The triad is structurally flexible under ambient conditions, and its conformation distribution is manipulated by the choice of water models and confinement. Two types of water models (SPC/E and TIP3P) are used for solvation. When solvated by SPC/E water, whose HOH angle follows an ideal tetrahedron, the structural characteristics of triad is compact in the bulk systems. However, under a certain nanosized confinement that drastically disrupts hydrogen bond networks in solvent, the triad favors an extended configuration. By contrast, the triad solvated by TIP3P water shows a set of U-shaped conformations in the confinement. We have shown that a slight structural difference in the two water models with the same dipole moment can have great distinction in water density, water orientation, and the number of hydrogen bonds in the proximity of a large flexible compound such as the triad. Subsequently, it has direct impact on the position of the triad in a confinement as well as the distribution of conformations at the interface of liquid and solid in a finite-size system.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2013

A margin based approach to determining sample sizes via tolerance bounds.

This paper proposes a tolerance bound approach for determining sample sizes. With this new methodology we begin to think of sample size in the context of uncertainty exceeding margin. As the sample size decreases the uncertainty in the estimate of margin increases. This can be problematic when the margin is small and only a few units are available for testing. In this case there may be a true underlying positive margin to requirements but the uncertainty may be too large to conclude we have sufficient margin to those requirements with a high level of statistical confidence. Therefore, we provide a methodology for choosing a sample size large enough such that an estimated QMU uncertainty based on the tolerance bound approach will be smaller than the estimated margin (assuming there is positive margin). This ensures that the estimated tolerance bound will be within performance requirements and the tolerance ratio will be greater than one, supporting a conclusion that we have sufficient margin to the performance requirements. In addition, this paper explores the relationship between margin, uncertainty, and sample size and provides an approach and recommendations for quantifying risk when sample sizes are limited.

Newcomer, Justin T.↗
DOE OSTI2020

Yarkovsky Drift Detections for 247 Near-Earth Asteroids

The Yarkovsky effect is a thermal process acting upon the orbits of small celestial bodies, which can cause these orbits to slowly expand or contract with time. The effect is subtle (〈da/dt〉∼10{sup −4} au My{sup −1} for a 1 km diameter object) and is thus generally difficult to measure. We analyzed both optical and radar astrometry for 600 Near-Earth Asteroids (NEAs) for the purpose of detecting and quantifying the Yarkovsky effect. We present 247 NEAs with measured drift rates, which is the largest published set of Yarkovsky detections. This large sample size provides an opportunity to examine the Yarkovsky effect in a statistical manner. In particular, we describe two independent population-based tests that verify the measurement of Yarkovsky orbital drift. First, we provide observational confirmation for the Yarkovsky effect’s theoretical size dependence of 1/D, where D is diameter. Second, we find that the observed ratio of negative to positive drift rates in our sample is 2.34, which, accounting for bias and sampling uncertainty, implies an actual ratio of 2.7{sub −0.7}{sup +0.3}. This ratio has a vanishingly small probability of occurring due to chance or statistical noise. The observed ratio of retrograde to prograde rotators is two times lower than the ratio expected from numerical predictions from NEA population studies and traditional assumptions about the sense of rotation of NEAs originating from various main belt escape routes. We also examine the efficiency with which solar energy is converted into orbital energy and find a median efficiency in our sample of 12%. We interpret this efficiency in terms of NEA spin and thermal properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Simple effective conservative treatment of uncertainty from sparse samples of random functions

This paper examines the variability of predicted responses when multiple stress-strain curves (reflecting variability from replicate material tests) are propagated through a finite element model of a ductile steel can being slowly crushed. Over 140 response quantities of interest (including displacements, stresses, strains, and calculated measures of material damage) are tracked in the simulations. Each response quantity’s behavior varies according to the particular stress-strain curves used for the materials in the model. We desire to estimate response variability when only a few stress-strain curve samples are available from material testing. Propagation of just a few samples will usually result in significantly underestimated response uncertainty relative to propagation of a much larger population that adequately samples the presiding random-function source. A simple classical statistical method, Tolerance Intervals, is tested for effectively treating sparse stress-strain curve data. The method is found to perform well on the highly nonlinear input-to-output response mappings and non-standard response distributions in the can-crush problem. The results and discussion in this paper support a proposition that the method will apply similarly well for other sparsely sampled random variable or function data, whether from experiments or models. Finally, the simple Tolerance Interval method is also demonstrated to be very economical.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis

Implementing remote, real-time spectroscopic monitoring of radiochemical processing streams in hot cell environments requires efficiency and simplicity. The success of optical spectroscopy for the quantification of species in chemical systems highly depends on representative training sets and suitable validation sets. Selecting a training set (i.e., calibration standards) to build multivariate regression models is both time- and resource-consuming using standard one-factor-at-a-time approaches. This study describes the use of experimental design to generate spectral training sets and a validation set for the quantification of sodium nitrate (0–1 M) and nitric acid (0.1–10 M) using the near-infrared water band centered at 1440 nm. Partial least squares regression models were built from training sets generated by both D- and I-optimal experimental designs and a one-factor-at-a-time approach. The prediction performance of each model was evaluated by comparing the bias and standard error of prediction for statistical significance. D- and I-optimal designs reduced the number of samples required to build regression models compared with one-factor-at-a-time while also improving performance. Models must be confirmed against a validation sample set when minimizing the number of samples in the training set. The D-optimal design performed the best when considering both performance and efficiency by improving predictive capability and reducing number of samples in the training set by 64% compared with the one-factor-at-a-time approach. The experimental design approach objectively selects calibration and validation spectral data sets based on statistical criterion to optimize performance and minimize resources.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2013

Improved statistical models for limited datasets in uncertainty quantification using stochastic collocation

This paper presents a data-driven framework for performing uncertainty quantification (UQ) by choosing a stochastic model that accurately describes the sources of uncertainty in a system. This model is propagated through an appropriate response surface function that approximates the behavior of this system using stochastic collocation. Given a sample of data describing the uncertainty in the inputs, our goal is to estimate a probability density function (PDF) using the kernel moment matching (KMM) method so that this PDF can be used to accurately reproduce statistics like mean and variance of the response surface function. Instead of constraining the PDF to be optimal for a particular response function, we show that we can use the properties of stochastic collocation to make the estimated PDF optimal for a wide variety of response functions. We contrast this method with other traditional procedures that rely on the Maximum Likelihood approach, like kernel density estimation (KDE) and its adaptive modification (AKDE). We argue that this modified KMM method tries to preserve what is known from the given data and is the better approach when the available data is limited in quantity. We test the performance of these methods for both univariate and multivariate density estimation by sampling random datasets from known PDFs and then measuring the accuracy of the estimated PDFs, using the known PDF as a reference. Comparing the output mean and variance estimated with the empirical moments using the raw data sample as well as the actual moments using the known PDF, we show that the KMM method performs better than KDE and AKDE in predicting these moments with greater accuracy. This improvement in accuracy is also demonstrated for the case of UQ in electrostatic and electrothermomechanical microactuators. We show how our framework results in the accurate computation of statistics in micromechanical systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

Satellites for the study of ocean primary productivity

The use of remote sensing techniques for obtaining estimates of global marine primary productivity is examined. It is shown that remote sensing and multiplatform (ship, aircraft, and satellite) sampling strategies can be used to significantly lower the variance in estimates of phytoplankton abundance and of population growth rates from the values obtained using the C-14 method. It is noted that multiplatform sampling strategies are essential to assess the mean and variance of phytoplankton biomass on a regional or on a global basis. The relative errors associated with shipboard and satellite estimates of phytoplankton biomass and primary productivity, as well as the increased statistical accuracy possible from the utilization of contemporaneous data from both sampling platforms, are examined. It is shown to be possible to follow changes in biomass and the distribution patterns of biomass as a function of time with the use of satellite imagery.

Smith, R. C.↗
DOE OSTI2020

Detection of DoS Attacks Using ARFIMA Modeling of GOOSE Communication in IEC 61850 Substations

Integration of Information and Communication Technology (ICT) in modern smart grids (SGs) offers many advantages including the use of renewables and an effective way to protect, control and monitor the energy transmission and distribution. To reach an optimal operation of future energy systems, availability, integrity and confidentiality of data should be guaranteed. Research on the cyber-physical security of electrical substations based on IEC 61850 is still at an early stage. In the present work, we first model the network traffic data in electrical substations, then, we present a statistical Anomaly Detection (AD) method to detect Denial of Service (DoS) attacks against the Generic Object Oriented Substation Event (GOOSE) network communication. According to interpretations on the self-similarity and the Long-Range Dependency (LRD) of the data, an Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model was shown to describe well the GOOSE communication in the substation process network. Based on this ARFIMA-model and in view of cyber-physical security, an effective model-based AD method is developed and analyzed. Two variants of the statistical AD considering statistical hypothesis testing based on the Generalized Likelihood Ratio Test (GLRT) and the cumulative sum (CUSUM) are presented to detect flooding attacks that might affect the availability of the data. Our work presents a novel AD method, with two different variants, tailored to the specific features of the GOOSE traffic in IEC 61850 substations. The statistical AD is capable of detecting anomalies at unknown change times under the realistic assumption of unknown model parameters. The performance of both variants of the AD method is validated and assessed using data collected from a simulation case study. We perform several Monte-Carlo simulations under different noise variances. The detection delay is provided for each detector and it represents the number of discrete time samples after which an anomaly is detected. In fact, our statistical AD method with both variants (CUSUM and GLRT) has around half the false positive rate and a smaller detection delay when compared with two of the closest works found in the literature. Our AD approach based on the GLRT detector has the smallest false positive rate among all considered approaches. Whereas, our AD approach based on the CUSUM test has the lowest false negative rate thus the best detection rate. Depending on the requirements as well as the costs of false alarms or missed anomalies, both variants of our statistical detection method can be used and are further analyzed using composite detection metrics.

IEC 61850 electrical substations↗
DOE OSTI2023

A framework to measure the properties of intergalactic metal systems with two-point flux statistics

ABSTRACT The abundance, temperature, and clustering of metals in the intergalactic medium are important parameters for understanding their cosmic evolution and quantifying their impact on cosmological analysis with the Ly α forest. The properties of these systems are typically measured from individual quasar spectra redward of the quasar’s Ly α emission line, yet that approach may provide biased results due to selection effects. We present an alternative approach to measure these properties in an unbiased manner with the two-point statistics commonly employed to quantify large-scale structure. Our model treats the observed flux of a large sample of quasar spectra as a continuous field and describes the one-dimensional, two-point statistics of this field with three parameters per ion: the abundance (column density distribution), temperature (Doppler parameter), and clustering (cloud–cloud correlation function). We demonstrate this approach on multiple ions (e.g. ${\rm C\, \small {\rm IV}}$ , ${\rm Si\, \small {\rm IV}}$ , and ${\rm Mg\, \small {\rm II}}$ ) with early data from the Dark Energy Spectroscopic Instrument (DESI) and high-resolution spectra from the literature. Our initial results show some evidence that the ${\rm C\, \small {\rm IV}}$ abundance is higher than previous measurements and evidence for abundance evolution over time. The first full year of DESI observations will have over an order of magnitude more quasar spectra than this study. In a future paper, we will use those data to measure the growth of clustering and its impact on the Ly α forest, as well as test other DESI analysis infrastructure such as the pipeline noise estimates and the resolution matrix.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Lunar soils grain size catalog

This catalog compiles every available grain size distribution for Apollo surface soils, trench samples, cores, and Luna 24 soils. Original laboratory data are tabled, and cumulative weight distribution curves and histograms are plotted. Standard statistical parameters are calculated using the method of moments. Photos and location comments describe the sample environment and geological setting. This catalog can help researchers describe the geotechnical conditions and site variability of the lunar surface essential to the design of a lunar base.

Graf, John C.↗
DOE OSTI2021

A review of imputation strategies for isobaric labeling-based shotgun proteomics

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is uniquely different than unlabeled studies. In this review, we compare the efficacy of nine imputation methods on a CPTAC proteomics iTRAQ dataset. Imputation methods were evaluated with regard to accuracy, variability, statistical hypothesis test inference and run time over datasets consisting of varying number of iTRAQ plexes and percentages of missing data. In general, expectation maximization and random forest imputation methods yielded the best performances, and constant-based methods performed poorly consistently across all dataset sizes and percentages of missing values. For datasets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. Based on the findings in this review, there are core imputation methods that perform higher for isobaric-labeled proteomics data, but great care and consideration as to whether imputation should be used should be given for datasets comprised of a small number of samples, as well as to factors such as computational time and reproducibility of imputation values.

Bramer, Lisa M.↗
DOE OSTI2022

The Sagittarius stream in Gaia Early Data Release 3 and the origin of the bifurcations

The Sagittarius dwarf spheroidal (Sgr) is a dissolving galaxy being tidally disrupted by the Milky Way (MW). Its stellar stream still poses serious modelling challenges, which hinders our ability to use it effectively as a prospective probe of the MW gravitational potential at large radii. Our goal is to construct the largest and most stringent sample of stars in the stream with which we can advance our understanding of the Sgr-MW interaction, focusing on the characterization of the bifurcations. We improved on previous methods based on the use of the wavelet transform to systematically search for the kinematic signature of the Sgr stream throughout the whole sky in the Gaia data. We then refined our selection via the use of a clustering algorithm on the statistical properties of the colour-magnitude diagrams. Our final sample contains more than 700 000 candidate stars and is three times larger than previous Gaia samples. With it, we have been able to detect the bifurcation of the stream in both the northern and southern hemispheres, which requires four branches (two bright and two faint) to fully describe the system. We present the detailed proper motion distribution of the trailing arm as a function of the angular coordinate along the stream, showing, for the first time, the presence of a sharp edge (on the side of the small proper motions) beyond which there are no Sgr stars. We also characterize the correlation between kinematics and distance. Finally, the chemical analysis of our sample shows that the faint branch of the bifurcation is more metal poor than the bright. We provide analytical descriptions for the proper motion trends as well as for the sky distribution of the four branches of the stream. Based on our analysis, we interpret the bifurcations as a misaligned overlap of the material stripped at the antepenultimate pericentre (faint branches) with the stars ejected at the penultimate pericentre (bright branch), given that Sgr just went through its perigalacticon. The source of this misalignment is still unknown, but we argue that models with some internal rotation in the progenitor – at least during the time of stripping of the stars that are now in the faint branches – are worth exploring.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Statistical Association between the Candidate Repeating FRB 20200320A and a Galaxy Group

Abstract We present results from angular cross correlations between select samples of CHIME/FRB repeaters and galaxies in three photometric galaxy surveys, which have shown correlations with the first CHIME/FRB catalog containing repeating and nonrepeating sources: Wide-field Infrared Survey Explorer (WISE) × SCOS, DESI-BGS, and DESI-LRG. We find a statistically significant correlation (p-value <0.001, after accounting for look-elsewhere factors) between a sample of repeaters with an extragalactic dispersion measure (DM) > 395 pc cm −3 and WISE × SCOS galaxies with redshiftz> 0.275. We demonstrate that the correlation arises surprisingly because of a statistical association between FRB 20200320A (extragalactic DM ≈ 550 pc cm −3 ) and a galaxy group in the same dark matter halo at redshiftz≈ 0.32. We estimate that the host halo, along with an intervening halo at redshiftz≈ 0.12, accounts for at least ∼30% of the extragalactic DM. Our results strongly motivate incorporating galaxy group and cluster catalogs into direct host association pipelines for FRBs with ≲ 1 ′ localization precision, effectively utilizing the two-point information to constrain FRB properties such as their redshift and DM distributions. In addition, we find marginal evidence for a negative correlation at 99.4% confidence limit between a sample of repeating FRBs with baseband data (median extragalactic DM = 354 pc cm −3 ) and DESI-LRG galaxies with redshift 0.3 ≤z< 0.45, suggesting that the repeaters might be more prone than apparent nonrepeaters to propagation effects in FRB–galaxy correlations due to intervening free electrons over angular scales ∼0.°5.

Astronomy & Astrophysics↗