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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Quasar classification using color and variability

We conduct a pilot investigation to determine the optimal combination of color and variability information to identify quasars in current and future multi-epoch optical surveys. We use a Bayesian quasar selection algorithm to identify 35,820 type 1 quasar candidates in a 239 deg{sup 2} field of the Sloan Digital Sky Survey (SDSS) Stripe 82, using a combination of optical photometry and variability. Color analysis is performed on 5-band single- and multi-epoch SDSS optical photometry to a depth of r∼22.4. From these data, variability parameters are calculated by fitting the structure function of each object in each band with a power-law model using 10 to >100 observations over timescales from ∼1 day to ∼8 years. Selection was based on a training sample of 13,221 spectroscopically confirmed type-1 quasars, largely from the SDSS. Using variability alone, colors alone, and combining variability and colors we achieve 91%, 93%, and 97% quasar completeness and 98%, 98%, and 97% efficiency, respectively, with particular improvement in the selection of quasars at 2.7<z<3.5 where quasars and stars have similar optical colors. The 22,867 quasar candidates that are not spectroscopically confirmed reach a depth of i∼22.0; 21,876 (95.7%) are dimmer than coadded i-band magnitude of 19.9, the cutoff for spectroscopic follow-up for SDSS on Stripe 82. Brighter than 19.9, we find 5.7% more quasar candidates without confirming spectra in sky regions otherwise considered complete. The resulting quasar sample has sufficient purity (and statistically correctable incompleteness) to produce a luminosity function comparable to those determined by spectroscopic investigations. We discuss improvements that can be made to the process in preparation for performing similar photometric selection and science on data from post-SDSS sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

UNCOVERING THE FORMATION OF ULTRACOMPACT DWARF GALAXIES BY MULTIVARIATE STATISTICAL ANALYSIS

We present a statistical analysis of the properties of a large sample of dynamically hot old stellar systems, from globular clusters (GCs) to giant ellipticals, which was performed in order to investigate the origin of ultracompact dwarf galaxies (UCDs). The data were mostly drawn from Forbes et al. We recalculated some of the effective radii, computed mean surface brightnesses and mass-to-light ratios, and estimated ages and metallicities. We completed the sample with GCs of M31. We used a multivariate statistical technique (K-Means clustering), together with a new algorithm (Gap Statistics) for finding the optimum number of homogeneous sub-groups in the sample, using a total of six parameters (absolute magnitude, effective radius, virial mass-to-light ratio, stellar mass-to-light ratio, and metallicity). We found six groups. FK1 and FK5 are composed of high- and low-mass elliptical galaxies, respectively. FK3 and FK6 are composed of high-metallicity and low-metallicity objects, respectively, and both include GCs and UCDs. Two very small groups, FK2 and FK4, are composed of Local Group dwarf spheroidals. Our groups differ in their mean masses and virial mass-to-light ratios. The relations between these two parameters are also different for the various groups. The probability density distributions of metallicity for the four groups of galaxies are similar to those of the GCs and UCDs. The brightest low-metallicity GCs and UCDs tend to follow the mass-metallicity relation like elliptical galaxies. The objects of FK3 are more metal-rich per unit effective luminosity density than high-mass ellipticals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Rotation curves and dynamical masses of MaNGA barred galaxies

In this paper we analyse a sample of 46 barred galaxies of MaNGA. Our goal is to investigate the stellar kinematics of these galaxies and obtain their rotation curves. Additionally, we aim to derive the total stellar and dynamical masses, as well as the maximum rotation velocity, in order to examine their distributions and scaling relations. Using the Pipe3D dataproducts publicly available we obtained the rotation curves, which were fitted considering two components of an axisymmetric Miyamoto–Nagai gravitational potential. We found a wide range of the maximum rotation velocities (117–340 ${\rm km\, s^{-1}}$), with a mean value of 200 ${\rm km\, s^{-1}}$. In addition we found that the total stellar and dynamical masses are in the range of log(M star /M ⊙ ) = 10.1−11.5, with a mean value of log(M star /M ⊙ ) = 10.8, and log(M dyn /M ⊙ ) = 10.4−12.0, with a mean value of log(M dyn /M ⊙ ) = 11.1, respectively. We found a strong correlation between dynamical mass and maximum velocity, between maximum velocity and magnitude, and between stellar mass and maximum velocity. According to these results, barred galaxies exhibit similar behaviour to that of normal spiral galaxies with respect to these relations, as well as in terms of the distribution of their dynamical mass and maximum rotation velocity. However, we found that the distribution of stellar masses of barred galaxies is statistically different from other samples including non-barred galaxies. Finally, analysing the galaxies that show nuclear activity, we find no difference with the rotation curves of normal galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1983

Cleanroom certification model

The Cleanroom software development methodology is designed to take the gamble out of product releases for both suppliers and receivers of the software. The ingredients of this procedure are a life cycle of executable product increments, representative statistical testing, and a standard estimate of the MTTF (Mean Time To Failure) of the product at the time of its release. A statistical approach to software product testing using randomly selected samples of test cases is considered. A statistical model is defined for the certification process which uses the timing data recorded during test. A reasonableness argument for this model is provided that uses previously published data on software product execution. Also included is a derivation of the certification model estimators and a comparison of the proposed least squares technique with the more commonly used maximum likelihood estimators.

Currit, P. A.↗
DOE OSTI2016

Monte Carlo Techniques for Nuclear Systems - Theory Lectures

These are lecture notes for a Monte Carlo class given at the University of New Mexico. The following topics are covered: course information; nuclear eng. review & MC; random numbers and sampling; computational geometry; collision physics; tallies and statistics; eigenvalue calculations I; eigenvalue calculations II; eigenvalue calculations III; variance reduction; parallel Monte Carlo; parameter studies; fission matrix and higher eigenmodes; doppler broadening; Monte Carlo depletion; HTGR modeling; coupled MC and T/H calculations; fission energy deposition. Solving particle transport problems with the Monte Carlo method is simple - just simulate the particle behavior. The devil is in the details, however. These lectures provide a balanced approach to the theory and practice of Monte Carlo simulation codes. The first lectures provide an overview of Monte Carlo simulation methods, covering the transport equation, random sampling, computational geometry, collision physics, and statistics. The next lectures focus on the state-of-the-art in Monte Carlo criticality simulations, covering the theory of eigenvalue calculations, convergence analysis, dominance ratio calculations, bias in Keff and tallies, bias in uncertainties, a case study of a realistic calculation, and Wielandt acceleration techniques. The remaining lectures cover advanced topics, including HTGR modeling and stochastic geometry, temperature dependence, fission energy deposition, depletion calculations, parallel calculations, and parameter studies. This portion of the class focuses on using MCNP to perform criticality calculations for reactor physics and criticality safety applications. It is an intermediate level class, intended for those with at least some familiarity with MCNP. Class examples provide hands-on experience at running the code, plotting both geometry and results, and understanding the code output. The class includes lectures & hands-on computer use for a variety of Monte Carlo calculations. Beginning MCNP users are encouraged to review LA-UR-09-00380, "Criticality Calculations with MCNP: A Primer (3nd Edition)" (available at http:// mcnp.lanl.gov under "Reference Collection") prior to the class. No Monte Carlo class can be complete without having students write their own simple Monte Carlo routines for basic random sampling, use of the random number generator, and simplified particle transport simulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Multiple Lines of Evidence

This paper discusses the process of identifying factors that influence the contamination level of a given decision area and then determining the likelihood that the area remains unacceptable. This process is referred to as lines of evidence. These lines of evidence then serve as inputs for the stratified compliance sampling (SCS) method, which requires a decision area to be divided into strata based upon contamination expectations. This is done in order to focus sampling efforts more within stratum where contamination is more likely and to use the domain knowledge about these likelihoods of the stratum remaining unacceptable to buy down the number of samples necessary, if possible. Two different building scenarios were considered as an example (see Table 3.1). SME expertise was elicited concerning four lines of evidence factors (see Table 3.2): 1) amount of contamination that was seen before decontamination, 2) post-decontamination air sampling information, 3) the applied decontaminant information, and 4) the surface material. Statistical experimental design and logistic regression modelling were used to help determine the likelihood that example stratum remained unacceptable for a given example scenario. The number of samples necessary for clearance was calculated by applying the SCS method to the example scenario, using the estimated likelihood of each stratum remaining unacceptable as was determined using the lines of evidence approach. The commonly used simple random sampling (SRS) method was also used to calculate the number of samples necessary for clearance for comparison purposes. The lines of evidence with SCS approach resulted in a 19% to 43% reduction in total number of samples necessary for clearance (see Table 3.6). The reduction depended upon the building scenario, as well as the level of percent clean criteria. A sensitivity analysis was also performed showing how changing the estimated likelihoods of stratum remaining unacceptable affect the number of samples necessary. In conclusion, using the lines of evidence approach with the SCS method can reduce sample sizes when SME are able to identify factors that reduce the likelihood that an area remain unacceptable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Atomic-scale determination of active facets on the MoVTeNb oxide M1 phase and their intrinsic catalytic activity for ethane oxidative dehydrogenation

Atomic resolution aberration-corrected HAADF-STEM imaging has been used to directly image the basal (001) plane of the catalytically relevant M1 phase in MoVTeNb complex oxides. Facets {010}, {120} and {210} are identified as the most stable lateral terminations of the crystals. For each of these surface facets the average concentration of active sites is determined directly from the images. The most stable {010} facet does not expose active sites and is therefore regarded to be inactive. As a consequence, large flattened M1 particles are concluded to have a lower activity than other morphologies. This hypothesis is validated by a statistical analysis of two samples with rod vs. plate morphology. The concentration of sites and the catalytic activity in ethane oxidative dehydrogenation correlate perfectly, demonstrating quantitatively that the differences in reactivity between MoVTeNbOx catalysts of equal crystallinity and chemical composition depend primarily on the terminating facets that constitute the elongated catalyst particles.

MoVTeNbO, Oxidative Dehydrogenation, M1 phase, STE↗
DOE OSTI2018

Two-Step Estimation in a Heteroscedastic Linear Regression Model

We study the problem of estimating a parameter in some heteroscedastic linear regression model in the case where the regressors consist of all order statistics based on the sample of identically distributed not necessarily independent observations with finite second moment. It is assumed that the random errors depend on the parameter and distributions of the corresponding regressors. We propose a two-step procedure for finding explicit asymptotically normal estimators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Challenges for the statistical gravitational-wave method to measure the Hubble constant

Gravitational waves (GW) can be employed as standard sirens that will soon measure the Hubble constant with sufficient precision to weigh in on the ~5σ Hubble tension. Most GW sources will have no identified electromagnetic counterpart, leading to uncertainty in the redshift of the source, and in turn a degeneracy between host galaxy distance, redshift, and H 0 . In the case where no electromagnetic counterparts are identified, it has been proposed that a statistical canvassing of candidate GW hosts, found in a large galaxy survey for example, can be used to accurately constrain the Hubble constant. We study and simulate this "galaxy voting" method to compute H 0 . Here, we find that the Hubble constant posterior is in general biased relative to the true value even when making optimistic assumptions about the statistical properties of the sample. Using the MICECAT light-cone catalog, we find that the bias in the H0 posteriors depends on the realization of the underlying galaxy sample and the precision of the GW source distance measurement.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Metabolomic signatures of chronic kidney disease of diverse etiologies in the rats and humans

Chronic kidney disease (CKD) has emerged as a major public health problem worldwide. It frequently progresses to end-stage renal disease, which is related to very high cost and mortality. Novel biomarkers can provide insight into the novel mechanism, facilitate early detection, and monitor progression of CKD and its response to therapeutic interventions. To identify potential biomarkers, we applied an UPLC-HDMS together with univariate and multivariate statistical analyses using plasma samples from patients with CKD of diverse etiologies (100 sera in discovery set and 120 sera in validation set) and two different rat models of CKD. Using comprehensive screening and validation workflow, we identified a panel of seven metabolites that were shared by all patients and animals regardless of the underlying cause of CKD. These included ricinoleic acid, stearic acid, cytosine, LPA(16:0), LPA(18:2), 3-methylhistidine, and argininic acid. The combination of these seven biomarkers enabled the discrimination of patients with CKD from healthy subjects with a sensitivity of 83.3% and a specificity of 96.7%. In addition, these biomarkers accurately reflected improvements in renal function in response to the therapeutic interventions. Lastly, our results indicated that the identified biomarkers may improve the diagnosis of CKD and provide a novel tool for monitoring of the progression of disease and response to treatment in CKD patients.

5/6 nephrectomized rats↗
DOE OSTI2024

Estimating Uncertainty in Simulated ENSO Statistics

Abstract Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

From Points to Planes: A Workflow for Converting Three‐Dimensional Point Cloud Data Into Discrete Fracture Network Flow and Transport Models

We present the Point cLoud Algorithm for NEtwork Extraction of Discrete Fracture Networks (PLANE-DFN), a point cloud–based algorithm for automatic fracture network extraction designed to support discrete fracture network (DFN) modeling workflows. PLANE-DFN segments three-dimensional fracture planes from raw point cloud data using RANdom SAmple Consensus coupled with statistical outlier removal and density-based clustering to isolate individual fracture features. Each candidate plane is constrained against site-specific structural constraints based on strike and dip. After segmentation, each fracture is converted into a 2-D convex polygon suitable for meshing and simulation. The PLANE-DFN algorithm is validated by comparing geometric and flow and transport data against data from dfnWorks simulations with ensembles of plane-fit networks. We find that the flow and transport in plane-fit networks are comparable to dfnWorks-generated networks when realistic network geometry is maintained. The PLANE-DFN algorithm provides an automated and streamlined workflow to transform point clouds of data into DFN network geometry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Thermodynamics of the S 2 -to-S 3 state transition of the oxygen-evolving complex of photosystem II

The room temperature pump–probe X-ray free electron laser (XFEL) measurements used for serial femtosecond crystallography provide remarkable information about the structures of the catalytic (S-state) intermediates of the oxygen-evolution reaction of photosystem II. However, mixed populations of these intermediates and moderate resolution limit the interpretation of the data from current experiments. The S 3 XFEL structures show extra density near the OEC that may correspond to a water/hydroxide molecule. However, in the latest structure, this additional oxygen is 2.08 Å from the Oε2 of D1–E189, which is closer than the sum of the van der Waals radii of the two oxygens. In this work, we use Boltzmann statistics and Monte Carlo sampling to provide a model for the S 2 -to-S 3 state transition, allowing structural changes and the insertion of an additional water/hydroxide. Based on our model, water/hydroxide addition to the oxygen-evolving complex (OEC) is not thermodynamically favorable in the S 2 g = 2 state, but it is in the S 2 g = 4.1 redox isomer. Thus, formation of the S 3 state starts by a transition from the S 2 g = 2 to the S 2 g = 4.1 structure. Then, electrostatic interactions support protonation of D1–H190 and deprotonation of the Ca 2+ -ligated water (W3) with proton loss to the lumen. The W3 hydroxide moves toward Mn4, completing the coordination shell of Mn4 and favoring its oxidation to Mn( IV ) in the S 3 state. In addition, binding an additional hydroxide to Mn1 leads to a conformational change of D1–E189 in the S 2 g = 4.1 and S 3 structures. In the S 3 state a fraction of D1–E189 release from Mn1 and bind a proton.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

High-throughput Single-Cell Proteomics and Transcriptomics from the Same Cells with a Nanoliter-Scale Spin-Transfer Approach

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

multi 'omics↗
DOE OSTI2020

Strong dependence of Type Ia supernova standardization on the local specific star formation rate

As part of an on-going effort to identify, understand and correct for astrophysics biases in the standardization of Type Ia supernovae (SN Ia) for cosmology, we have statistically classified a large sample of nearby SNe Ia into those that are located in predominantly younger or older environments. This classification is based on the specific star formation rate measured within a projected distance of 1 kpc from each SN location (LsSFR). This is an important refinement compared to using the local star formation rate directly, as it provides a normalization for relative numbers of available SN progenitors and is more robust against extinction by dust. We find that the SNe Ia in predominantly younger environments are Δ Y = 0.163 ± 0.029 mag (5.7 σ ) fainter than those in predominantly older environments after conventional light-curve standardization. This is the strongest standardized SN Ia brightness systematic connected to the host-galaxy environment measured to date. The well-established step in standardized brightnesses between SNe Ia in hosts with lower or higher total stellar masses is smaller, at Δ M = 0.119 ± 0.032 mag (4.5 σ ), for the same set of SNe Ia. When fit simultaneously, the environment-age offset remains very significant, with Δ Y = 0.129 ± 0.032 mag (4.0 σ ), while the global stellar mass step is reduced to Δ M = 0.064 ± 0.029 mag (2.2 σ ). Thus, approximately 70% of the variance from the stellar mass step is due to an underlying dependence on environment-based progenitor age. Also, we verify that using the local star formation rate alone is not as powerful as LsSFR at sorting SNe Ia into brighter and fainter subsets. Standardization that only uses the SNe Ia in younger environments reduces the total dispersion from 0.142 ± 0.008 mag to 0.120 ± 0.010 mag. Overall, we show that as environment-ages evolve with redshift, a strong bias, especially on the measurement of the derivative of the dark energy equation of state, can develop. Fortunately, data that measure and correct for this effect using our local specific star formation rate indicator, are likely to be available for many next-generation SN Ia cosmology experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

BEYONDPLANCK II. CMB mapmaking through Gibbs sampling

We present a Gibbs sampling solution to the mapmaking problem for cosmic microwave background (CMB) measurements that builds on existing destriping methodology. Gibbs sampling breaks the computationally heavy destriping problem into two separate steps: noise filtering and map binning. Considered as two separate steps, both are computationally much cheaper than solving the combined problem. This provides a huge performance benefit as compared to traditional methods and it allows us, for the first time, to bring the destriping baseline length to a single sample. Here, we applied the Gibbs procedure to simulated Planck 30 GHz data. We find that gaps in the time-ordered data are handled efficiently by filling them in with simulated noise as part of the Gibbs process. The Gibbs procedure yields a chain of map samples, from which we are able to compute the posterior mean as a best-estimate map. The variation in the chain provides information on the correlated residual noise, without the need to construct a full noise covariance matrix. However, if only a single maximum-likelihood frequency map estimate is required, we find that traditional conjugate gradient solvers converge much faster than a Gibbs sampler in terms of the total number of iterations. The conceptual advantages of the Gibbs sampling approach lies in statistically well-defined error propagation and systematic error correction. This methodology thus forms the conceptual basis for the mapmaking algorithm employed in the BEYONDPLANCK framework, which implements the first end-to-end Bayesian analysis pipeline for CMB observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Optimum value of original events on the PEPT technique

Do Positron emission particle tracking (PEPT) has been used to track the motion of a single radioactively labeled tracer particle within a bed of similar particles. In this paper, the effect of the original event fraction on the results precise in two experiments has been reviewed. Results showed that the algorithm can no longer distinguish some corrupt trajectories, in addition to; further iteration reduces the statistical significance of the sample without improving its quality. Results show that the optimum value of trajectories depends on the type of experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗