The use of cluster computer systems for NASA/JPL applications
In this paper we outline the results of using three generations of cluster machines at JPL for a select set of applications drawn from a larger development effort.
cluster↗
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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.
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FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent
Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.
Alexander Melancon↗
A Dynamic Reduction Network for Point Clouds
Classifying whole images is a classic problem in machine learning, and graph neural networks are a powerful methodology to learn highly irregular geometries. It is often the case that certain parts of a point cloud are more important than others when determining overall classification. On graph structures this started by pooling information at the end of convolutional filters, and has evolved to a variety of staged pooling techniques on static graphs. In this paper, a dynamic graph formulation of pooling is introduced that removes the need for predetermined graph structure. It achieves this by dynamically learning the most important relationships between data via an intermediate clustering. The network architecture yields interesting results considering representation size and efficiency. It also adapts easily to a large number of tasks from image classification to energy regression in high energy particle physics.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Calibration of the optical mass proxy for clusters of galaxies and an update of the WHL12 cluster catalog
Accurately determining the mass of galaxy clusters is fundamental for many studies of cosmology and galaxy evolution. We collect and rescale the cluster masses of 1191 clusters of 0.05<z<0.75 estimated by X-ray or Sunyaev–Zeldovich measurements and use them to calibrate the optical mass proxy. The total r-band luminosity (in units of L{sup ∗}) of these clusters is obtained by using spectroscopic and photometric data of the Sloan Digital Sky Survey (SDSS). We find that the correlation between the cluster mass M{sub 500} and total r-band luminosity L{sub 500} significantly evolves with redshift. After correcting for the evolution, we define a new cluster richness R{sub L∗,500}=L{sub 500} E(z){sup 1.40} as the optical mass proxy. By using this newly defined richness and the recently released SDSS DR12 spectroscopic data, we update the WHL12 cluster catalog and identify 25,419 new rich clusters at high redshift. In the SDSS spectroscopic survey region, about 89% of galaxy clusters have spectroscopic redshifts. The mass can be estimated with a scatter of 0.17 dex for the clusters in the updated catalog.
79 ASTRONOMY AND ASTROPHYSICS↗
The Second Most Distant Cluster of Galaxies in the Extended Medium Sensitivity Survey
We report on our ASCA, Keck, and ROSAT observations of MS 1137.5+6625, the second most distant cluster of galaxies in the Einstein Extended Medium Sensitivity Survey (EMSS), at redshift 0.78. We now have a full set of X-ray temperatures, optical velocity dispersions, and X-ray images for a complete, high-redshift sample of clusters of galaxies drawn from the EMSS. Our ASCA observations of MS 1137.5 +6625 yield a temperature of 5.7 (+2.1)(-1.1) keV and a metallicity of 0.43 (+40)(-3.7) solar, with 90% confidence limits. Keck II spectroscopy of 22 cluster members reveals a velocity dispersion of 884 (+185)(-124) km 24/s. This cluster is the most distant in the sample with a detected iron line. We also derive a mean abundance at z = 0.8 by simultaneously fitting X-ray data for the two z = 0.8 clusters, and obtain an abundance of Z(sub Fe) = 0.33 (+.26)(-.23). Our ROSAT observations show that MS 1137.5+6625 is regular and highly centrally concentrated. Fitting of a Beta model to the X-ray surface brightness yields a core radius of only 71/h kpc (q(sub o) = 0.1) with Beta = 0.70(+.45)(-.15) The gas mass interior to 0.5/h Mpc is thus 1.2 (+0.2)(-0.3) X 10(exp 13) h(exp - 5/2) Solar Mass (q(sub o) = 0.1). If the cluster's gas is nearly isothermal and in hydrostatic equilibrium with the cluster potential, the total mass of the cluster within this same region is 2.1(+1.5)(-0.8) X 10exp 14)/h Solar Mass, giving a gas fraction of 0.06 +/-0.04 h (exp -3/2). This cluster is the highest redshift EMSS cluster showing evidence for a possible cooling flow (about 20-400 Solar Mass/yr). The velocity dispersion, temperature, gas fraction, and iron abundance of MS 1137.5+6625 are all statistically the same as those properties in lower red- shift clusters of similar luminosity. With this cluster's temperature now in hand, we derive a high-redshift temperature function for EMSS clusters at 0.5 < z < 0.9 and compare it with temperature functions at lower redshifts, showing that the evolution of the temperature function is relatively modest. Supplementing our high-redshift sample with other data from the literature, we demonstrate that neither the cluster luminosity-temperature relation, nor cluster metallicities, nor the cluster gas evolved with redshift. The very modest degree of evolution in the luminosity-temperature relation inferred from these data is inconsistent with the absence of evolution in the X-ray luminosity functions derived from ROSAT cluster surveys if a critical density structure formation model is assumed.
Donahue, Megan↗
Evidence for Suppression of Structure Growth in the Concordance Cosmological Model
We present evidence for a suppressed growth rate of large-scale structure during the dark-energy dominated era. Modeling the growth rate of perturbations with the ``growth index'' γ, we find that current cosmological data strongly prefer a higher growth index than the value γ=0.55 predicted by general relativity in a flat ΛCDM cosmology. Both the cosmic microwave background data from Planck and the large-scale structure data from weak lensing, galaxy clustering, and cosmic velocities separately favor growth suppression. When combined, they yield γ = 0.633$^{+0.025}_{–0.024}$, excluding γ = 0.55 at a statistical significance of 3.7σ. The combination of fσ8 and Planck measurements prefers an even higher growth index of γ = 0.639$^{+0.024}_{–0.025}$, corresponding to a 4.2σ-tension with the concordance model. In Planck data, the suppressed growth rate offsets the preference for nonzero curvature and fits the data equally well as the latter model. Furthermore, a higher γ leads to a higher matter fluctuation amplitude S 8 inferred from galaxy clustering and weak lensing measurements, and a lower S 8 from Planck data, effectively resolving the S 8 tension.
79 ASTRONOMY AND ASTROPHYSICS↗
High-Throughput Nanoindentation Mapping of Additively Manufactured T91 Steel
Here, this work aims to adapt nanoindentation mapping combined with a k-means algorithm as a high-throughput technique to study the nano-scale spatial changes in mechanical properties for a heterogeneous material. This technique can also classify the individual data points based on their properties. Hundreds to thousands of indents were performed on additively manufactured T91 at room temperature, 300°C, 400°C, and 500°C across a square area with a side length of 120 μm to 400 μm. From this data, the hardness and reduced modulus at each point could be calculated and mapped. Using k-means clustering, we were able to arrange the data into three or four clusters corresponding roughly to the ferritic and martensitic phases as well as one or two intermediate clusters sampling both the phases. The hardness of these two phases appears to be quite stable as a function of temperature. Nanoindentation mapping and the k-means algorithm can therefore be used to rapidly assess the feasibility of heterogeneous materials under extreme conditions, such as nuclear reactor steels.
36 MATERIALS SCIENCE↗
Magellan/M2FS and MMT/Hectochelle Spectroscopy of Dwarf Galaxies and Faint Star Clusters within the Galactic Halo*
We present spectroscopic data for 16,369 stellar targets within and/or toward 38 dwarf spheroidal galaxies and faint star clusters within the Milky Way halo environment. All spectra come from observations with the multiobject, fiber-fed echelle spectrographs M2FS at the Magellan/Clay telescope or Hectochelle at the MMT, reaching a typical limiting magnitude G ≲ 21. Data products include processed spectra from all observations and catalogs listing estimates—derived from template model fitting—of line-of-sight velocity (median uncertainty 1.4 km s –1 ) effective temperature (255 K), (base-10 logarithm of) surface gravity (0.59 dex in cgs units), [Fe/H] (0.4 dex) and [Mg/Fe] (0.27 dex) abundance ratios. The sample contains multiepoch measurements for 3720 sources, with up to 15 epochs per source, enabling studies of intrinsic spectroscopic variability. The sample contains 6087 likely red giant stars (based on surface gravity), and 4492 likely members (based on line-of-sight velocity and Gaia-measured proper motion) of the target systems. The number of member stars per individual target system ranges from a few, for the faintest systems, to ~850 for the most luminous. For most systems, our new samples extend over wider fields than have previously been observed; of the likely members in our samples, 820 lie beyond 2 times the projected half-light radius of their host system, and 42 lie beyond 5 R half .
79 ASTRONOMY AND ASTROPHYSICS↗
Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization
Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low energy structures of Pd n (n = 1-21) clusters supported on CeO 2 (111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pdn clusters of any structure. Low energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters and large cluster isomers share similarities in both shape and energy, and elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Unsupervised Segmentation and Clustering Workflow for Efficient Processing of 4D-STEM and 5D-STEM Data
Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as data dimensionality and sampling density increase, particularly for in situ scanning diffraction experiments (5D-STEM), robust segmentation of structurally consistent behavior across sequential measurements becomes essential for efficient and physically meaningful analysis. Here, we introduce a clustering framework that identifies crystallographically distinct domains from 4D-STEM datasets. By using local diffraction-pattern similarity as a metric, the method extracts closed contours delineating spatially contiguous regions. This approach produces cluster-averaged diffraction patterns that improve signal quality while reducing data volume by orders of magnitude, enabling rapid and accurate orientation, phase, and strain mapping. We demonstrate the applicability of this approach to in situ liquid-cell 4D-STEM data of gold nanoparticle growth. Our method provides a scalable and generalizable route for spatially coherent segmentation, data compression, and quantitative structure–strain mapping across diverse 4D-STEM modalities. The full analysis code and example workflows are publicly available to support reproducibility and reuse.
4D-STEM↗
A spectroscopic census in young stellar regions: the σ Orionis cluster
We present a spectroscopic survey of the stellar population of the σ Orionis cluster. We have obtained spectral types for 340 stars. Spectroscopic data for spectral typing come from several spectrographs with similar spectroscopic coverage and resolution. More than half of the stars in our sample are members confirmed by the presence of lithium in absorption, strong Hα in emission or weak gravity-sensitive features. In addition, we have obtained high-resolution (R ∼ 34,000) spectra in the Hα region for 169 stars in the region. Radial velocities were calculated from this data set. The radial velocity distribution for members of the cluster is in agreement with previous work. Analysis of the profile of the Hα line and infrared observations reveals two binary systems or fast rotators that mimic the Hα width expected in stars with accretion disks. On the other hand, there are stars with optically thick disks and narrow Hα profiles not expected in stars with accretion disks. This contribution constitutes the largest homogeneous spectroscopic data set of the σ Orionis cluster to date.
79 ASTRONOMY AND ASTROPHYSICS↗
THE ACS SURVEY OF GALACTIC GLOBULAR CLUSTERS. XI. THE THREE-DIMENSIONAL ORIENTATION OF THE SAGITTARIUS DWARF SPHEROIDAL GALAXY AND ITS GLOBULAR CLUSTERS
We use observations from the Hubble Space Telescope Advanced Camera for Surveys (HST/ACS) study of Galactic globular clusters to investigate the spatial distribution of the inner regions of the disrupting Sagittarius dwarf spheroidal galaxy (Sgr). We combine previously published analyses of four Sgr member clusters located near or in the Sgr core (M54, Arp 2, Terzan 7, and Terzan 8) with a new analysis of diffuse Sgr material identified in the background of five low-latitude Galactic bulge clusters (NGC 6624, 6637, 6652, 6681, and 6809) observed as part of the ACS survey. By comparing the bulge cluster color-magnitude diagrams to our previous analysis of the M54/Sgr core, we estimate distances to these background features. The combined data from four Sgr member clusters and five Sgr background features provide nine independent measures of the Sgr distance and, as a group, provide uniformly measured and calibrated probes of different parts of the inner regions of Sgr spanning 20 Degree-Sign over the face of the disrupting dwarf. This allows us, for the first time, to constrain the three-dimensional orientation of Sgr's disrupting core and globular cluster system and compare that orientation to the predictions of an N-body model of tidal disruption. The density and distance of Sgr debris are consistent with models that favor a relatively high Sgr core mass and a slightly greater distance (28-30 kpc, with a mean of 29.4 kpc). Our analysis also suggests that M54 is in the foreground of Sgr by {approx}2 kpc, projected on the center of the Sgr dSph. While this would imply a remarkable alignment of the cluster and the Sgr nucleus along the line of sight, we cannot identify any systematic effect in our analysis that would falsely create the measured 2 kpc separation. Finally, we find that the cluster Terzan 7 has the most discrepant distance (25 kpc) among the four Sgr core clusters, which may suggest a different dynamical history than the other Sgr core clusters.
79 ASTRONOMY AND ASTROPHYSICS↗
On the effects of cluster density and concentration on the horizontal branch morphology - The origin of the blue tails
Possible relationships between horizontal branch (HB) morphology in Galactic globular clusters and the cluster structure and dynamical evolution are investigated. New HB observables are defined and determined using a theoretical framework deduced from HB models. Data for 53 Galactic globular clusters are used to obtain correlations between the observables. It is found that the net length of the HB and the presence and extent of blue tails in particular are correlated with the cluster density and concentrations, in the sense of more concentrated or denser clusters having bluer and longer HB morphologies. This effect is especially strong for the intermediate metallicity clusters. Thus, the cluster environment can affect the stellar evolution leading to the HB and therefore the HB morphology. This result is interpreted in terms of an enhanced mass removal from the HB progenitors.
Fusi Pecci, F.↗
Dark energy survey year 3 results: miscentring calibration and X-ray-richness scaling relations in redMaPPer clusters
ABSTRACT We use Dark Energy Survey Year 3 (DES Y3) clusters with archival XMM–Newton and Chandra X-ray data to assess the centring performance of the redMaPPer cluster finder and to measure key richness observable scaling relations. We find that 10–20 per cent of redMaPPer clusters are miscentred, both when comparing to the X-ray peak position and to the visually identified central cluster galaxy. We find no significant difference in miscentring in bins of low versus high richness or redshift. The dominant reasons for miscentring include masked or missing data and the presence of other bright galaxies in the cluster. For half of the miscentred clusters, the correct central was one of the possible centrals identified by redMaPPer, while for ∼40 per cent of miscentred clusters, the correct central is not a redMaPPer member mostly due to masking. Additionally, we fit scaling relations of X-ray temperature and luminosity with richness. We find a TX–λ scatter of $0.21\pm 0.01$. While the scatter in TX–λ is consistent in redshift bins, we find modestly different slopes, with high-redshift clusters displaying a somewhat shallower relation. Splitting based on richness, we find a marginally larger scatter for our lowest richness bin, 20 < λ < 40. We note that the robustness of the scaling relations at lower richnesses is limited by the unknown selection function, but at λ > 75, we detect nearly all of the clusters falling within existing X-ray pointings. The X-ray properties of detected, serendipitous clusters are generally consistent with those of targeted clusters.
79 ASTRONOMY AND ASTROPHYSICS↗
The Alaska Arctic Vegetation Archive (AVA-AK)
The Alaska Arctic Vegetation Archive (AVA-AK, GIVD-ID: NA-US-014) is a free, publically available database archive of vegetation-plot data from the Arctic tundra region of northern Alaska. The archive currently contains 24 datasets with 3,026 non-overlapping plots. Of these, 74% have geolocation data with 25-m or better precision. Species cover data and header data are stored in a Turboveg database. A standardized Pan Arctic Species List provides a consistent nomenclature for vascular plants, bryophytes, and lichens in the archive. A web-based online Alaska Arctic Geoecological Atlas (AGA-AK) allows viewing and downloading the species data in a variety of formats, and provides access to a wide variety of ancillary data. We conducted a preliminary cluster analysis of the first 16 datasets (1,613 plots) to examine how the spectrum of derived clusters is related to the suite of datasets, habitat types, and environmental gradients. Here, we present the contents of the archive, assess its strengths and weaknesses, and provide three supplementary files that include the data dictionary, a list of habitat types, an overview of the datasets, and details of the cluster analysis.
54 ENVIRONMENTAL SCIENCES↗
Measurement of the splashback feature around SZ-selected Galaxy clusters with DES, SPT, and ACT
We present a detection of the splashback feature around galaxy clusters selected using the Sunyaev-Zel'dovich (SZ) signal. Recent measurements of the splashback feature around optically selected galaxy clusters have found that the splashback radius, rsp, is smaller than predicted by N-body simulations. A possible explanation for this discrepancy is that rsp inferred from the observed radial distribution of galaxies is affected by selection effects related to the optical cluster-finding algorithms. We test this possibility by measuring the splashback feature in clusters selected via the SZ effect in data from the South Pole Telescope SZ survey and the Atacama Cosmology Telescope Polarimeter survey. The measurement is accomplished by correlating these cluster samples with galaxies detected in the Dark Energy Survey Year 3 data. The SZ observable used to select clusters in this analysis is expected to have a tighter correlation with halo mass and to be more immune to projection effects and aperture-induced biases, potentially ameliorating causes of systematic error for optically selected clusters. We find that the measured r sp for SZ-selected clusters is consistent with the expectations from simulations, although the small number of SZ-selected clusters makes a precise comparison difficult. In agreement with previous work, when using optically selected redMaPPer clusters with similar mass and redshift distributions, r sp is ~2σ smaller than in the simulations. These results motivate detailed investigations of selection biases in optically selected cluster catalogues and exploration of the splashback feature around larger samples of SZ-selected clusters. Additionally, we investigate trends in the galaxy profile and splashback feature as a function of galaxy colour, finding that blue galaxies have profiles close to a power law with no discernible splashback feature, which is consistent with them being on their first infall into the cluster.
Shin, T.↗
A comparison of clustering algorithms for use in large scale gene expression analysis using synthetic microarray data
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analysis↗
Precipitation regime classification for the Mojave Desert: Implications for fire occurrence
Mojave Desert ecosystem processes are dependent upon the amount and seasonality of precipitation. Multi-decadal periods of drought or above-average rainfall affect landscape vegetation condition, biomass and susceptibility to fire. The seasonality of precipitation events can also affect the likelihood of lightning, a key ignition source for fires. To develop an understanding of precipitation regimes and fire patterns we used monthly average precipitation data and GIS data representing burned areas from 1971-2010. We applied a K-means cluster analysis to the monthly precipitation data identifying three distinct precipitation seasons; winter (October – March), spring (April-June) and summer (July-September) and four discrete precipitation regimes within the Mojave ecoregion.
classification↗