Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data clustering”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dark energy survey year 3 results: Cosmological constraints from cluster abundances, weak lensing, and galaxy clustering

Galaxy clusters provide a unique probe of the late-time cosmic structure and serve as a powerful independent test of the Λ⁢CDM model. This work presents the first set of cosmological constraints derived with ∼16,000 optically selected redMaPPer clusters across nearly 5000 deg 2 using DES year 3 datasets. Our analysis leverages a consistent modeling framework for galaxy cluster cosmology and DES-Y3 joint analyses of galaxy clustering and weak lensing (3 × 2⁢pt), ensuring direct comparability with the DES-Y3 3 × 2⁢pt analysis. Here, we obtain constraints of 𝑆 8 = 0.864 ± 0.035 and Ω m = $0.26⁢5^{+0.019}_{−0.031}$ from the cluster-based data vector. We find that cluster constraints and 3 × 2⁢pt constraints are consistent under the Λ⁢CDM model with a posterior predictive distribution (PPD) value of 0.53. The consistency between clusters and 3 × 2⁢pt provides a stringent test of Λ⁢CDM across different mass and spatial scales. Jointly analyzing clusters with 3 × 2⁢pt further improves cosmological constraints, yielding 𝑆 8 = $0.81⁢1^{+0.022}_{−0.020}$ and Ω m = $0.29⁢4^{+0.022}_{−0.033}$, a 24% improvement in the Ω m − 𝑆 8 figure of merit over 3 × 2⁢pt alone. Moreover, we find no significant deviation from the Planck CMB constraints with a probability to exceed (PTE) value of 0.6, significantly reducing previous 𝑆 8 tension claims. Finally, combining DES 3 × 2⁢pt, DES clusters, and Planck CMB places an upper limit on the sum of neutrino masses of ∑𝑚 𝜈 < 0.26 eV at 95% confidence under the Λ⁢CDM model. These results establish optically selected clusters as a key cosmological probe and pave the way for cluster-based analyses in upcoming stage-IV surveys such as LSST, Euclid, and Roman.

Abbott, T. M. C. [NSF’s National Optical-Infrared ↗
NASA NTRSDate not supplied

Nanopore Sequencing-Based Microbial Air Profiling Method for Crewed Spacecraft

Microbial monitoring of the International Space Station (ISS) atmosphere is vital to maintaining the health of the spacecraft and crew. Key to NASA’s microbial risk assessment is the identity of contaminating microorganisms in the environment. Historically, this has been achieved through impaction-based air sampling followed by culture. Identification of the microorganisms present requires sample return to Earth and lab-based analyses. While this culture-based approach has served to provide alerts to anomalies and overall confidence in the controls in place, it is not suitable for exploration missions with no sample return. Recently, significant advancements in molecular-based microbial monitoring via nanopore sequencing have been implemented onboard the ISS. Building on this work, multiple commercially available air samplers, compatible with downstream molecular analysis, were evaluated for use in the spaceflight environment. Through this assessment, the Coriolis Compact (Bertin Technologies), which uses cyclonic technology to collect bioaerosols onto the surface of a sterile cone, was selected for a larger-scale comparison to the current culture-based monitoring method. Using the Coriolis Compact, 1000 L of air was collected from the breakroom of an office building and a fitness center. The buffer used to dissociate the microbial cells from the surface of the cone was split between the NASA Microbiology Laboratory’s standard culture and Sanger sequencing-based method and the culture-independent nanopore sequencing method. The bacteria identified through culture were present in the nanopore data, with Micrococcus, Staphylococcus, and Moraxella being the most common cultured isolates, which is expected based on the media and growth conditions. Not surprisingly, the nanopore data yielded much higher diversity and paralleled that of previous atmospheric microbiome studies of human-occupied built environments. As compared to the culture-based data where the breakroom and fitness center data sets cluster in proximity, the nanopore data depicts the contrast of these atmospheric microbiomes. Moreover, the nanopore data were sufficient to meet NASA’s risk assessment needs and noted the culturable isolates routinely observed. This nanopore-based atmospheric microbial profiling method will enable near real-time environmental monitoring of crewed spacecraft as future missions extend beyond low-Earth orbit.

Brandon Dunbar↗
NASA NTRS2002

A Dissimilarity Measure for Clustering High- and Infinite Dimensional Data that Satisfies the Triangle Inequality

The cosine or correlation measures of similarity used to cluster high dimensional data are interpreted as projections, and the orthogonal components are used to define a complementary dissimilarity measure to form a similarity-dissimilarity measure pair. Using a geometrical approach, a number of properties of this pair is established. This approach is also extended to general inner-product spaces of any dimension. These properties include the triangle inequality for the defined dissimilarity measure, error estimates for the triangle inequality and bounds on both measures that can be obtained with a few floating-point operations from previously computed values of the measures. The bounds and error estimates for the similarity and dissimilarity measures can be used to reduce the computational complexity of clustering algorithms and enhance their scalability, and the triangle inequality allows the design of clustering algorithms for high dimensional distributed data.

Socolovsky, Eduardo A.↗
NASA NTRS2004

Chandra X-ray Observations of Young Clusters: Orion Flanking Fields Data - Volume II

We present results of Chandra observations of two flanking fields (FFs) in Orion, outside the Orion Nebula Cluster (ONC). The observations were taken with the ACIS-I camera with an exposure time of about 48 ks each field. We present a catalog of 417 sources, which includes X-ray luminosity, optical and infrared photometry, and X-ray variability information. We have found 91 variable sources, 33 of which have a flarelike light curve and 11 of which have a pattern of a steady increase or decrease over a 10 hr period. The optical and infrared photometry for the stars identified as X-ray sources are consistent with most of these objects being pre-main-sequence stars with ages younger than 10 Myr. We present evidence for an age difference among the X-ray-selected samples of NGC 2264, Orion FFs, and ONC, with NGC 2264 being the oldest and ONC being the youngest.

pre-main sequence↗
DOE OSTI2019

Atom Probe Tomography Interlaboratory Study on Clustering Analysis in Experimental Data Using the Maximum Separation Distance Approach

We summarize the findings from an interlaboratory study conducted between ten international research groups and investigate the use of the commonly used maximum separation distance and local concentration thresholding methods for solute clustering quantification. The study objectives are: to bring clarity to the range of applicability of the methods; identify existing and/or needed modifications; and interpretation of past published data. Participants collected experimental data from a proton-irradiated 304 stainless steel and analyzed Cu-rich and Ni–Si rich clusters. The datasets were also analyzed by one researcher to clarify variability originating from different operators. The Cu distribution fulfills the ideal requirements of the maximum separation method (MSM), namely a dilute matrix Cu concentration and concentrated Cu clusters. This enabled a relatively tight distribution of the cluster number density among the participants. By contrast, the group analysis of the Ni–Si rich clusters by the MSM was complicated by a high Ni matrix concentration and by the presence of Si-decorated dislocations, leading to larger variability among researchers. While local concentration filtering could, in principle, tighten the results, the cluster identification step inevitably maintained a high scatter. Recommendations regarding reporting, selection of analysis method, and expected variability when interpreting published data are discussed.

APT, Round Robin Studies, atom probe tomography, c↗
DOE OSTI2019

Mass Calibration of Optically Selected DES Clusters Using a Measurement of CMB-cluster Lensing with SPTpol Data

We use cosmic microwave background (CMB) temperature maps from the 500 deg$^{2}$ SPTpol survey to measure the stacked lensing convergence of galaxy clusters from the Dark Energy Survey (DES) Year-3 redMaPPer (RM) cluster catalog. The lensing signal is extracted through a modified quadratic estimator designed to be unbiased by the thermal Sunyaev-Zel{'}dovich (tSZ) effect. The modified estimator uses a tSZ-free map, constructed from the SPTpol 95 and 150 GHz datasets, to estimate the background CMB gradient. For lensing reconstruction, we employ two versions of the RM catalog: a flux-limited sample containing 4003 clusters and a volume-limited sample with 1741 clusters. We detect lensing at a significance of 8.7$\sigma$(6.7$\sigma$) with the flux(volume)-limited sample. By modeling the reconstructed convergence using the Navarro-Frenk-White profile, we find the average lensing masses to be $M_{200m}$ = ($1.62^{+0.32}_{-0.25}$ [stat.] $\pm$ 0.04 [sys.]) and ($1.28^{+0.14}_{-0.18}$ [stat.] $\pm$ 0.03 [sys.]) $\times\ 10^{14}\ M_{\odot}$ for the volume- and flux-limited samples respectively. The systematic error budget is much smaller than the statistical uncertainty and is dominated by the uncertainties in the RM cluster centroids. We use the volume-limited sample to calibrate the normalization of the mass-richness scaling relation, and find a result consistent with the galaxy weak-lensing measurements from DES.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Differentially Private K -Means Clustering Applied to Meter Data Analysis and Synthesis

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. Here, in this paper, we address the problem of allowing third parties to apply K-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2008

Recent Insights into the Nature of Turbulence in the Solar Wind

During the past several years, studies of solar wind turbulence using data from Cluster and other spacecraft, and results from new numerical simulations, have revealed new aspects of solar wind turbulence. I will try to highlight some of that research. At the shortest length scales and highest frequencies, there is renewed interest in determining how the turbulence dissipates, e.g., whether by kinetic Alfven waves or whistler turbulence. Finding observational evidence for exponential damping of solar wind fluctuations has proven challenging. New studies using a combination of flux gate and search coil magnetometer data from Cluster have extended this search (in the spacecraft frame of reference) to more than 10 Hertz. New models and simulations are also being used to study the dissipation. A detailed study of fluctuations in the magnetosheath suggests that turbulent dissipation could be occurring at very thin current sheets as had been suggested by two-dimensional MHD simulations more than 20 years ago. Data from the four Cluster spacecraft, now at their maximum separation of 10,000 km provide new opportunities to investigate the symmetry properties, scale lengths, and the relative proportion of magnetic energy in parallel and perpendicular wave numbers of solar wind turbulence. By utilizing well-calibrated electron data, it has been possible to take advantage of the tetrahedral separation of Cluster in the solar wind near apogee to measure directly the compressibility and vorticity of the solar wind plasma.

Goldstein, Melvun L.↗
NASA NTRS1988

Intrinsic integrated UBVRI colors of Galactic globular clusters

Published observational data on 50 Galactic globular clusters, including spectral classifications, homogenized colors, and color excesses, are compiled in extensive tables, graphs, and diagrams and analyzed to determine the intrinsic-color/integrated-spectral-type relationship in the UBVRI system. These relationships are found to exhibit significant slopes, although the RI colors do not contribute substantially to the intrinsic-color determination. The values of a(B-V) for the northern and southern Galactic hemispheres are found to be 0.068 + or - 0.006 and 0.039 + or - 0.003 mag, respectively.

Reed, B. Cameron↗
NASA NTRS1989

The nearby Abell clusters. III - Luminosity functions for eight rich clusters

Red photographic data on eight rich Abell clusters are combined with previous results on four other Abell clusters to study the luminosity functions of the clusters. The results produce a mean value of the characteristic galaxy magnitude (M asterisk) that is consistent with previous results. No relation is found between the magnitude of the first-ranked cluster galaxy and M asterisk, suggesting that the value of M asterisk is not changed by dynamical evolution. The faint ends of the luminosity functions for many of the clusters are quite flat, validating the nonuniversality in the parametrization of Schechter (1976) functions for rich clusters of galaxies.

Oegerle, William R.↗
NASA NTRS2008

Using PEACE Data from the four CLUSTER Spacecraft to Measure Compressibility, Vorticity, and the Taylor Microscale in the Magnetosheath and Plasma Sheet

We present determinations of compressibility and vorticity in the magnetosheath and plasma sheet using moments from the four PEACE thermal electron instruments on CLUSTER. The methodology used assumes a linear variation of the moments throughout the volume defined by the four satellites, which allows spatially independent estimates of the divergence, curl, and gradient. Once the vorticity has been computed, it is possible to estimate directly the Taylor microscale. We have shown previously that the technique works well in the solar wind. Because the background flow speed in the magnetosheath and plasma sheet is usually less than the Alfven speed, the Taylor frozen-in-flow approximation cannot be used. Consequently, this four spacecraft approach is the only viable method for obtaining the wave number properties of the ambient fluctuations. Our results using electron velocity moments will be compared with previous work using magnetometer data from the FGM experiment on Cluster.

Goldstein, Melvyn L.↗
NASA NTRS1980

Application of the SRI cloud-tracking technique to rapid-scan GOES observations

An automatic cloud tracking system was applied to multilayer clouds associated with severe storms. The method was tested using rapid scan observations of Hurricane Eloise obtained by the GOES satellite on 22 September 1975. Cloud tracking was performed using clustering based either on visible or infrared data. The clusters were tracked using two different techniques. The data of 4 km and 8 km resolution of the automatic system yielded comparable in accuracy and coverage to those obtained by NASA analysts using the Atmospheric and Oceanographic Information Processing System.

Wolf, D. E.↗
DOE OSTI2016

EVOLUTION OF STELLAR-TO-HALO MASS RATIO AT z = 0–7 IDENTIFIED BY CLUSTERING ANALYSIS WITH THE HUBBLE LEGACY IMAGING AND EARLY SUBARU/HYPER SUPRIME-CAM SURVEY DATA

We present clustering analysis results from 10,381 Lyman break galaxies (LBGs) at z ∼ 4–7, identified in the Hubble legacy deep imaging and new complimentary large-area Subaru/Hyper Suprime-Cam data. We measure the angular correlation functions of these LBGs at z ∼ 4, 5, 6, and 7 and fit these measurements using halo occupation distribution (HOD) models that provide an estimate of halo masses, M{sub h}∼(1−20)×10{sup 11} M{sub ⊙}. Our M{sub h} estimates agree with those obtained by previous clustering studies in a UV-magnitude versus M{sub h} plane and allow us to calculate stellar-to-halo mass ratios (SHMRs) of LBGs. By comparison with the z∼0 SHMR, we identify evolution of the SHMR from z∼0 to z∼4 and from z∼4 to z∼7 at the >98% confidence levels. The SHMR decreases by a factor of ∼2 from z∼0 to 4 and increases by a factor of ∼4 from z∼4 to 7 at the dark matter halo mass of M{sub h}∼10{sup 11} M{sub ⊙}. We compare our SHMRs with results of a hydrodynamic simulation and a semianalytic model and find that these theoretical studies do not predict the SHMR increase from z∼4 to 7. We obtain the baryon conversion efficiency (BCE) of LBGs at z∼4 and find that the BCE increases with increasing dark matter halo mass. Finally, we compare our clustering+HOD estimates with results from abundance matching techniques and conclude that the M{sub h} estimates of the clustering+HOD analyses agree with those of the simple abundance matching within a factor of 3, and that the agreement improves when using more sophisticated abundance matching techniques that include subhalos, incompleteness, and/or evolution in the star formation and stellar mass functions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization

© 2019 Association for Computing Machinery. The goal of this work is to investigate the impact of missing values in clustering joint categorical social sequences. Identifying patterns in sociodemographic longitudinal data is important in a number of social science settings. However, performing analytical operations, such as clustering on life course trajectories, is challenging due to the categorical and multidimensional nature of the data, their mixed data types, and corruption by missing and inconsistent values. Data quality issues were investigated previously on single variable sequences. To understand their effects on multivariate sequence analysis, we employ a dataset of mixed data types and missing values, a dissimilarity measure designed for joint categorical sequence data, together with dimensionality reduction methodologies in a systematic design of sequence clustering experiments. Given the categorical nature of our data, we employ an “edit” distance using optimal matching. Because each data record has multiple variables of different types, we investigate the impact of mixing these variables in a single dissimilarity measure. Between variables with binary values and those with multiple nominal values, we find that the ability to overcome missing data problems is more difficult in the nominal domain than in the binary domain. Additionally, alignment of leading missing values can result in systematic biases in dissimilarity matrices and subsequently introduce both artificial clusters and unrealistic interpretations of associated data domains. We demonstrate the usage of t-distributed stochastic neighborhood embedding to visually guide mitigation of such biases by tuning the missing value substitution cost parameter or determining an optimal sequence span.

Lazar, Alina↗
DOE OSTI2014

Neutrinos and dark energy after Planck and BICEP2: data consistency tests and cosmological parameter constraints

The detection of the B-mode polarization of the cosmic microwave background (CMB) by the BICEP2 experiment implies that the tensor-to-scalar ratio r should be involved in the base standard cosmology. In this paper, we extend the ΛCDM r+neutrino/dark radiation models by replacing the cosmological constant with the dynamical dark energy with constant w. Four neutrino plus dark energy models are considered, i.e., the wCDM r ∑ m{sub ν}, wCDM r N{sub eff}, wCDM r ∑ m{sub ν} N{sub eff}, and wCDM r N{sub eff} m{sub ν,sterile}{sup eff} models. The current observational data considered in this paper include the Planck temperature data, the WMAP 9-year polarization data, the baryon acoustic oscillation data, the Hubble constant direct measurement data, the Planck Sunyaev-Zeldovich cluster counts data, the Planck CMB lensing data, the cosmic shear data, and the BICEP2 polarization data. We test the data consistency in the four cosmological models, and then combine the consistent data sets to perform joint constraints on the models. We focus on the constraints on the parameters w, ∑ m{sub ν}, N{sub eff}, and m{sub ν,sterile}{sup eff}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

DLSIA: Deep Learning for Scientific Image Analysis

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Changes of multispectral soil patterns with increasing crop canopy

Multispectral data and automatic data processing were used to map surface soil patterns and to follow the changes in multispectral radiation from a field of maize (Zea mays L.) during a period from seeding to maturity. Panchromatic aerial photography was obtained in early May 1970 and multispectral scanner missions were flown on May 6, June 30, August 11 and September 5, 1970 to obtain energy measurements in 13 wavelength bands. The orange portion of the visible spectrum was used in analyzing the May and June data to cluster relative radiance of the soils into eight different radiance levels. The reflective infrared spectral band was used in analyzing the August and September data to cluster maize into different spectral categories. The computer-produced soil patterns had a striking similarity to the soil pattern of the aerial photograph. These patterns became less distinct as the maize canopy increased.

Kristof, S. J.↗