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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Two-Step Time-Series Data Clustering Method for Building-Level Load Profile: Preprint

Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation. In our two-step clustering approach, we first calculate the total load consumption for each building, clustering the load profiles based on energy consumption level. Then, we further cluster the load profiles in each energy cluster based on the load shape. The parameter selection for each clustering step is discussed. The proposed method is applied on actual building-level load profiles, and the results have proved the effectiveness of this method.

advanced metering infrastructure (AMI)↗
DOE OSTI2014

Method for discovering relationships in data by dynamic quantum clustering

Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Method for discovering relationships in data by dynamic quantum clustering

Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A Measurement of Gravitational Lensing of the Cosmic Microwave Background by Galaxy Clusters Using Data from the South Pole Telescope

Clusters of galaxies are expected to gravitationally lens the cosmic microwave background (CMB) and thereby generate a distinct signal in the CMB on arcminute scales. Measurements of this effect can be used to constrain the masses of galaxy clusters with CMB data alone. Here we present a measurement of lensing of the CMB by galaxy clusters using data from the South Pole Telescope (SPT). We also develop a maximum likelihood approach to extract the CMB cluster lensing signal and validate the method on mock data. We quantify the effects on our analysis of several potential sources of systematic error and find that they generally act to reduce the best-fit cluster mass. It is estimated that this bias to lower cluster mass is roughly 0.85σ in units of the statistical error bar, although this estimate should be viewed as an upper limit. Furthermore, we apply our maximum likelihood technique to 513 clusters selected via their Sunyaev–Zeldovich (SZ) signatures in SPT data, and rule out the null hypothesis of no lensing at 3.1σ. The lensing-derived mass estimate for the full cluster sample is consistent with that inferred from the SZ flux: M 200,lens = 0.83 +0.38 -0.37 M 200,SZ (68% C.L., statistical error only).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Constraining the dark matter contribution of $\gamma$ rays in clusters of galaxies using Fermi -LAT data

Clusters of galaxies are the largest gravitationally bound systems in the Universe. Their dynamics are dominated by dark matter (DM), which makes them among the best targets for indirect DM searches. We analyze 12 years of data collected by the Fermi Large Area Telescope (Fermi-LAT) in the direction of 49 clusters of galaxies selected for their proximity to the Earth and their high x-ray flux, which makes them the most promising targets. We first create physically motivated models for the DM density around each cluster considering different assumptions for the substructure distribution. Then we perform a combined search for a $\gamma$-ray signal in the Fermi-LAT data between 500 MeV and 1 TeV. We find a signal of $\gamma$ rays potentially associated with DM that is at a statistical significance of 2.5⁢σ-3.0⁢σ when considering a slope for the subhalo mass distribution α =1.9 and minimum mass of M min =10 -6⁢ M ⊙ . The best-fit DM mass and annihilation cross sections for a b⁢$\overline{b}$ annihilation channel are m X =40–60 GeV and $\langle$σ⁢ν$\rangle$=(2–4)×10 -25 cm 3 /s. When we consider α =2.0 and M min =10 -9⁢ M ⊙ , the best fit of the cross section reduces to $\langle$σ⁢ν$\rangle$=(4–10)×10 -26 cm 3 /s. For both DM substructure models there is a tension between the values of $\langle$σ⁢ν$\rangle$ that we find and the upper limits obtained with the nondetection of a $\gamma$-ray flux from Milky Way dwarf spheroidal galaxies. In conclusion, this signal is thus more likely associated with $\gamma$ rays produced in the intracluster region by cosmic rays colliding with gas and photon fields.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

A measurement of CMB cluster lensing with SPT and DES year 1 data

Clusters of galaxies gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint in the CMB on arcminute scales. Measurement of this effect offers a promising way to constrain the masses of galaxy clusters, particularly those at high redshift. We use CMB maps from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB lensing signal around galaxy clusters identified in optical imaging from first year observations of the Dark Energy Survey. The cluster catalog used in this analysis contains 3697 members with mean redshift of $\bar{z} = 0.45$. We detect lensing of the CMB by the galaxy clusters at $8.1\sigma$ significance. Using the measured lensing signal, we constrain the amplitude of the relation between cluster mass and optical richness to roughly $17\%$ precision, finding good agreement with recent constraints obtained with galaxy lensing. The error budget is dominated by statistical noise but includes significant contributions from systematic biases due to the thermal SZ effect and cluster miscentering.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1985

Evaluation of the Radiometric Quality of the TM Data Using Clustering, Linear Transformations and Multispectral Distance Measures

The radiometric quality of LANDSAT 4 TM data for the classification and identification of Earth surface features was evaluated. Techniques employed in the evaluation included clustering, data compression (linear transformations), multispectral distance measures, and hierarchical classification methods. TM and MSS data for the Chicago, Illinois test site were studied. In order to determine the radiometric quality of the TM thermal data for temperature mapping of surface water, a test site was selected within the area covered by the TM scene (Scene ID: 40101-16025) gathered over Illinois. This site was chosen because it includes a surface water body with a large range of temperatures, i.e., a cooling pond for the Dresden nuclear power plant and the junction of two rivers.

Bartolucci, L. A.↗
DOE OSTI2021

Improving the Accuracy of Clustering Electric Utility Net Load Data using Dynamic Time Warping

Identifying patterns in electric utility net load data in a time-series format is very useful in preparing the operation for next day. Machine learning algorithms have been used in other domains and those concepts are applied in this paper on real-world net load measurement data. Clustering is the practice of grouping data with similar characteristics as determined by the distance measure. The K-means clustering algorithm is utilized here with actual electric utility data. The paper uses the standard distance measure, Euclidean distance (ED), and compares its performance against the dynamic time warping (DTW) measure. An actual case study with real data is presented, and DTW distance measure-based method observed to result better accuracy compared to the ED based method for substation net load measurements predominantly with residential customers.

clustering↗
NASA NTRS1983

X-ray evidence for white dwarf binaries in globular clusters

Data from an X ray survey of globular clusters by means of instrumentation on the Einstein Observatory indicate the existence of a distinct class of low-luminosity (L) sources. L was measured in the 10 to the 32nd to 10 to the 34th ergs/sec range when viewing point-like objects. The L-value is three orders of magnitude less than that of neutron star binaries, and is consistent with projections for binaries with 0.5 solar mass white dwarfs. They were located outside the cluster cores, which was taken as an indication of low mass. More than three sources at the lowest luminosity bound were observed in two clusters, suggesting a steep rise in the luminosity function, which agreed with observational data on galactic plane cataclysmic variables.

Hertz, P.↗
DOE OSTI2022

Improving Cosmological Constraints from Galaxy Cluster Number Counts with CMB-cluster-lensing Data: Results from the SPT-SZ Survey and Forecasts for the Future

Abstract We show the improvement to cosmological constraints from galaxy cluster surveys with the addition of cosmic microwave background (CMB)-cluster lensing data. We explore the cosmological implications of adding mass information from the 3.1 σ detection of gravitational lensing of the CMB by galaxy clusters to the Sunyaev–Zel’dovich (SZ) selected galaxy cluster sample from the 2500 deg 2 SPT-SZ survey and targeted optical and X-ray follow-up data. In the ΛCDM model, the combination of the cluster sample with the Planck power spectrum measurements prefers σ 8 Ω m / 0.3 0.5 = 0.831 ± 0.020 . Adding the cluster data reduces the uncertainty on this quantity by a factor of 1.4, which is unchanged whether the 3.1 σ CMB-cluster lensing measurement is included or not. We then forecast the impact of CMB-cluster lensing measurements with future cluster catalogs. Adding CMB-cluster lensing measurements to the SZ cluster catalog of the ongoing SPT-3G survey is expected to improve the expected constraint on the dark energy equation of state w by a factor of 1.3 to σ ( w ) = 0.19. We find the largest improvements from CMB-cluster lensing measurements to be for σ 8 , where adding CMB-cluster lensing data to the cluster number counts reduces the expected uncertainty on σ 8 by respective factors of 2.4 and 3.6 for SPT-3G and CMB-S4.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Parallel k-means++

A parallelization of the k-means++ seed selection algorithm on three distinct hardware platforms: GPU, multicore CPU, and multithreaded architecture. K-means++ was developed by David Arthur and Sergei Vassilvitskii in 2007 as an extension of the k-means data clustering technique. These algorithms allow people to cluster multidimensional data, by attempting to minimize the mean distance of data points within a cluster. K-means++ improved upon traditional k-means by using a more intelligent approach to selecting the initial seeds for the clustering process. While k-means++ has become a popular alternative to traditional k-means clustering, little work has been done to parallelize this technique. We have developed original C++ code for parallelizing the algorithm on three unique hardware architectures: GPU using NVidia's CUDA/Thrust framework, multicore CPU using OpenMP, and the Cray XMT multithreaded architecture. By parallelizing the process for these platforms, we are able to perform k-means++ clustering much more quickly than it could be done before.

Source record↗
DOE OSTI2017

The Clustering of Luminous Red Galaxies at z ∼ 0.7 from EBOSS and BOSS Data

We present the first scientific results from the luminous red galaxy (LRG) sample of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) combined with the high-redshift galaxies of the previous BOSS sample. We measure the small- and intermediate-scale clustering from a sample of more than 97,000 galaxies in the redshift range 0.6<z<0.9. We interpret these measurements in the framework of the Halo Occupation Distribution. The bias of this sample of LRGs is 2.30 ± 0.03, with a satellite fraction of 13% ± 3% and a mean halo mass of 2.5×10{sup 13} h{sup −1} M{sub ⊙}. These results are consistent with expectations, demonstrating that these LRGs will be reliable tracers of large-scale structure at z∼0.7. The galaxy bias implies a scatter of luminosity at fixed halo mass, σ{sub logL}, of 0.19 dex. Using the clustering of massive galaxies from BOSS CMASS, BOSS LOWZ, and SDSS, we find that σ{sub logL}=0.19 is consistent with observations over the full redshift range that these samples cover. The addition of eBOSS to previous surveys allows the investigation of the evolution of massive galaxies over the past ∼7 Gyr.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

The Redmapper galaxy cluster catalog from DES science verification data

We describe updates to the redMaPPer algorithm, a photometric red-sequence cluster finder specifically designed for large photometric surveys. The updated algorithm is applied to 150 deg{sup 2} of Science Verification (SV) data from the Dark Energy Survey (DES), and to the Sloan Digital Sky Survey (SDSS) DR8 photometric data set. The DES SV catalog is locally volume limited and contains 786 clusters with richness λ>20 (roughly equivalent to M{sub 500c}≳10{sup 14} h{sub 70}{sup −1} M{sub ⊙}) and 0.2<z<0.9. The DR8 catalog consists of 26,311 clusters with 0.08<z<0.6, with a sharply increasing richness threshold as a function of redshift for z≳0.35. The photometric redshift performance of both catalogs is shown to be excellent, with photometric redshift uncertainties controlled at the σ{sub z}/(1+z)∼0.01 level for z≲0.7, rising to ∼0.02 at z∼0.9 in DES SV. We make use of Chandra and XMM X-ray and South Pole Telescope Sunyaev–Zeldovich data to show that the centering performance and mass–richness scatter are consistent with expectations based on prior runs of redMaPPer on SDSS data. We also show how the redMaPPer photo-z and richness estimates are relatively insensitive to imperfect star/galaxy separation and small-scale star masks.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1982

Classification of surface units in the equatorial region of Mars based on Viking Orbiter color, albedo, and thermal data

Clusters corresponding to mappable surface units are sought in Viking Orbiter color, albedo, and thermal inertia data for the equatorial region of Mars. A principal components analysis indicated that 84% of the variance within the data for this region can be carried along two vector directions which typify the dominant trend of Martian surface materials. These dominant trends were deemphasized by stretching the data from a five-dimensional elliptical swarm into a hypersphere, through the use of principal component techniques. The decorrelated data were then plotted in a triangle diagram with red/violet, albedo and thermal inertia apices to facilitate inherent cluster discrimination. As many as eight clusters can be identified, with important mixing between them. The three major clusters consist of red and grey material extremes, along with intermediate value materials.

Arvidson, R. E.↗
DOE OSTI2025

Data-Driven Clustering and Classification of Outage Patterns with Insights into their Links to Extreme Events

At a global level extreme events have increased in both scale and impact. These events have the potential to affect the electrical grid infrastructure and cause a wide range of outages, which can lead to a disruption in daily patterns, cost millions of dollars and also the loss of life. Currently, to track these outage events there have been various approaches developed ranging from regional to national level quantifications for what defines an outage. However, this variation in methods can potentially lead to subjective decision-making and a lack of proper management in relation to the event. While previous work has made strides in determining spatio-temporal patterns, minimal attention has been given to the type and number of outages an area may be exposed to. The differences in incurred cost and the overall severity of an event between a transformer box malfunction and a hurricane are drastic, and by finding historical signals, we can allow for more efficient management, potentially saving lives and millions of dollars. Here, we leverage unsupervised machine learning techniques to delineate outage patterns among 22 counties within the United States and find that there are clear, segregated clusters (0.93 silhouette) of data which are related by event behavior and underlying cause. This finding will allow for energy stakeholders, policy makers, and researchers to gain a deeper understanding of the extent and severity of historic events and to better prepare for electrical grid infrastructure planning and management.

Koob, Benjamin [ORNL]↗