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NASA NTRS2008

Magnetic Field Observations of Partial Ring Current during Storm Recovery Phase

We present results of an extensive survey of the magnetic field observations in the inner magnetosphere using 30 years of magnetospheric magnetic field data from Polar, Cluster, ISEE, and AMPTE/CCE missions. The purpose of this study is to understand the magnetic field evolution during the recovery phase of geomagnetic storms, and its implication to the ring current recovery and loss mechanisms of ring current particles. It is now commonly believed that a strong partial ring current is formed during the storm main phase due to the enhanced earthward convection of energetic ions from nightside plasma sheet. But the presence of a strong partial ring current throughout the recovery phase remains controversial. The magnetic field generated by the ring current inflates the inner magnetosphere and causes magnetic field depressions in the equatorial magnetosphere. During the storm recovery phase, we find that the distribution of the equatorial magnetic field depression exhibits similar local time dependence as the ring current distribution obtained from the combined dataset in the earlier study. It shows that a strong partial ring current is a permanent feature throughout the recovery phase. In the early recovery phase, the partial ring current peaks near the dusk terminator as indicated by the peak of the magnetic field depression. As the recovery phase progresses, the partial ring current decays most quickly near the dusk and results in a dusk-to-midnight moving of the peak of the partial ring current. Thus the loss mechanisms work most effectively near the dusk. The magnetic field depression increases the gyroradius of ring current protons to a scale greater or comparable to the thickness of the magnetopause, which increases the chance of ion drift loss near the dusk magnetopause at larger L-shell (L greater than 5). But the drift loss mechanism alone cannot explain the loss of ring current ions especially in the smaller L-shell (L less than 5). The precipitation loss due to wave-particle interaction is most likely the dominant loss mechanism in the small L-shell as it works most effectively at the same local time.

Le, G.↗

NASA NTRS2007

Magnetic Field Observations of Partial Ring Current during Storm Recovery Phase

We present results of an extensive survey of the magnetic field observations in the inner magnetosphere using 30 years of magnetospheric magnetic field data from Polar, Cluster, ISEE, and AMPTE/CCE missions. The purpose of this study is to understand the magnetic field evolution during the recovery phase of geomagnetic storms, and its implication to the ring current recovery and loss mechanisms of ring current particles. Our previous work on global ring current distribution [Le et al., 2004] has shown that a significant partial ring current is always present at all Dst levels (regardless of storm phases) even for quiet time ring current. The total current carried by the partial ring current is much stronger than (during stormtime) or at least comparable to (during quiet time) the symmetric ring current. It is now commonly believed that a strong partial ring current is formed during the storm main phase due to the enhanced earthward convection of energetic ions from nightside plasma sheet. But the presence of a strong partial ring current throughout the recovery phase remains controversial. The magnetic field generated by the ring current inflates the inner magnetosphere and causes magnetic field depressions in the equatorial magnetosphere. During the storm recovery phase, we find that the distribution of the equatorial magnetic field depression exhibits similar local time dependence as the ring current distribution obtained from the combined dataset in the earlier study. It shows that a strong partial ring current is a permanent feature throughout the recovery phase. In the early recovery phase, the partial ring current peaks near the dusk terminator as indicated by the peak of the magnetic field depression. As the recovery phase progresses, the partial ring current decays most quickly near the dusk and results in a dusk-to-midnight moving of the peak of the partial ring current. Thus the loss mechanisms work most effectively near the dusk. The magnetic field depression increases the gyroradius of ring current protons to a scale greater or comparable to the thickness of the magnetopause, which increases the chance of ion drift loss near the dusk magnetopause at larger L-shell (L>5). But the drift loss mechanism alone cannot explain the loss of ring current ions especially in the smaller L-shell (L<5). The precipitation loss due to wave-particle interaction is most likely the dominant loss mechanism in the small L-shell as it works most effectively at the same local time.

Le, Guan↗

NASA NTRS2010

The Interannual Stability of Cumulative Frequency Distributions for Convective System Size and Intensity

The characteristics of convective system populations in West Africa and the western Pacific tropical cyclone basin were analyzed to investigate whether interannual variability in convective activity in tropical continental and oceanic environments is driven by variations in the number of events during the wet season or by favoring large and/or intense convective systems. Convective systems were defined from TRMM data as a cluster of pixels with an 85 GHz polarization-corrected brightness temperature below 255 K and with an area at least 64 km 2. The study database consisted of convective systems in West Africa from May Sep for 1998-2007 and in the western Pacific from May Nov 1998-2007. Annual cumulative frequency distributions for system minimum brightness temperature and system area were constructed for both regions. For both regions, there were no statistically significant differences among the annual curves for system minimum brightness temperature. There were two groups of system area curves, split by the TRMM altitude boost in 2001. Within each set, there was no statistically significant interannual variability. Sub-setting the database revealed some sensitivity in distribution shape to the size of the sampling area, length of sample period, and climate zone. From a regional perspective, the stability of the cumulative frequency distributions implied that the probability that a convective system would attain a particular size or intensity does not change interannually. Variability in the number of convective events appeared to be more important in determining whether a year is wetter or drier than normal.

Mohr, Karen I.↗

NASA NTRS2009

The Interannual Stability of Cumulative Frequency Distributions for Convective System Size and Intensity

The characteristics of convective system populations in West Africa and the western Pacific tropical cyclone basin were analyzed to investigate whether interannual variability in convective activity in tropical continental and oceanic environments is driven by variations in the number of events during the wet season or by favoring large and/or intense convective systems. Convective systems were defined from Tropical Rainfall Measuring Mission (TRMM) data as a cluster of pixels with an 85-GHz polarization-corrected brightness temperature below 255 K and with an area of at least 64 square kilometers. The study database consisted of convective systems in West Africa from May to September 1998-2007, and in the western Pacific from May to November 1998-2007. Annual cumulative frequency distributions for system minimum brightness temperature and system area were constructed for both regions. For both regions, there were no statistically significant differences between the annual curves for system minimum brightness temperature. There were two groups of system area curves, split by the TRMM altitude boost in 2001. Within each set, there was no statistically significant interannual variability. Subsetting the database revealed some sensitivity in distribution shape to the size of the sampling area, the length of the sample period, and the climate zone. From a regional perspective, the stability of the cumulative frequency distributions implied that the probability that a convective system would attain a particular size or intensity does not change interannually. Variability in the number of convective events appeared to be more important in determining whether a year is either wetter or drier than normal.

Mohr, Karen I.↗

NASA NTRS2011

Flood Inundation Mapping in the Logone Floodplain from Multi Temporal Landsat ETM+Imagery

Yearly flooding in the Logone floodplain makes an impact on agricultural, pastoral, and fishery systems in the Lake Chad Basin. Since the flooding extent and depth are highly variable, flood inundation mapping helps us make better use of water resources and prevent flood hazards in the Logone floodplain. The flood maps are generated from 33 multi temporal Landsat Enhanced Thematic Mapper Plus (ETM+) during three years 2006 to 2008. Flooded area is classified using a short-wave infrared band whereas open water is classified by Iterative Self-organizing Data Analysis (ISODATA) clustering. The maximum flooding extent in the study area increases up to approximately 5.8K km2 in late October 2008. The study also provides strong correlation of the flooding extents with water height variations in both the floodplain and the river based on a second polynomial regression model. The water heights are from ENIVSAT altimetry in the floodplain and gauge measurements in the river. Coefficients of determination between flooding extents and water height variations are greater than 0.91 with 4 to 36 days in phase lag. Floodwater drains back to the river and to the northeast during the recession period in December and January. The study supports understanding of the Logone floodplain dynamics in detail of spatial pattern and size of the flooding extent and assists the flood monitoring and prediction systems in the catchment.

Jung, Hahn Chul↗

NASA NTRS2011

NASA Instrument Cost/Schedule Model

NASA's Office of Independent Program and Cost Evaluation (IPCE) has established a number of initiatives to improve its cost and schedule estimating capabilities. 12One of these initiatives has resulted in the JPL developed NASA Instrument Cost Model. NICM is a cost and schedule estimator that contains: A system level cost estimation tool; a subsystem level cost estimation tool; a database of cost and technical parameters of over 140 previously flown remote sensing and in-situ instruments; a schedule estimator; a set of rules to estimate cost and schedule by life cycle phases (B/C/D); and a novel tool for developing joint probability distributions for cost and schedule risk (Joint Confidence Level (JCL)). This paper describes the development and use of NICM, including the data normalization processes, data mining methods (cluster analysis, principal components analysis, regression analysis and bootstrap cross validation), the estimating equations themselves and a demonstration of the NICM tool suite.

JCL↗

NASA NTRS2016

Combining Passive Thermography and Acoustic Emission for Large Area Fatigue Damage Growth Assessment of a Composite Structure

Passive thermography and acoustic emission data were obtained for improved real time damage detection during fatigue loading. A strong positive correlation was demonstrated between acoustic energy event location and thermal heating, especially if the structure under load was nearing ultimate failure. An image processing routine was developed to map the acoustic emission data onto the thermal imagery. This required removing optical barrel distortion and angular rotation from the thermal data. The acoustic emission data were then mapped onto thermal data, revealing the cluster of acoustic emission event locations around the thermal signatures of interest. By combining both techniques, progression of damage growth is confirmed and areas of failure are identified. This technology provides improved real time inspections of advanced composite structures during fatigue testing.Keywords: Thermal nondestructive evaluation, fatigue damage detection, aerospace composite inspection, acoustic emission, passive thermography

Zalameda, Joseph N.↗

NASA NTRS2015

Projections of Temperature-Attributable Premature Deaths in 209 U.S. Cities Using a Cluster-Based Poisson Approach

Background: A warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships. Methods: We used Poisson regressions to model temperature-attributable premature mortality as a function of daily average temperature in 209 U.S. cities by month. We used climate data to group cities into clusters and applied an Empirical Bayes adjustment to improve model stability and calculate cluster-based month-specific temperature-mortality functions. Using data from two climate models, we calculated future daily average temperatures in each city under Representative Concentration Pathway 6.0. Holding population constant at 2010 levels, we combined the temperature data and cluster-based temperature-mortality functions to project city-specific temperature-attributable premature deaths for multiple future years which correspond to a single reporting year. Results within the reporting periods are then averaged to account for potential climate variability and reported as a change from a 1990 baseline in the future reporting years of 2030, 2050 and 2100. Results: We found temperature-mortality relationships that vary by location and time of year. In general, the largest mortality response during hotter months (April - September) was in July in cities with cooler average conditions. The largest mortality response during colder months (October-March) was at the beginning (October) and end (March) of the period. Using data from two global climate models, we projected a net increase in premature deaths, aggregated across all 209 cities, in all future periods compared to 1990. However, the magnitude and sign of the change varied by cluster and city. Conclusions: We found increasing future premature deaths across the 209 modeled U.S. cities using two climate model projections, based on constant temperature-mortality relationships from 1997 to 2006 without any future adaptation. However, results varied by location, with some locations showing net reductions in premature temperature-attributable deaths with climate change.

Schwartz, Joel D.↗

DOE OSTI2026

DESI Data Release 2 ELGs: Property-dependent subsamples, imaging systematics, and clustering

Using emission-line galaxies (ELGs) from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2, we evaluate a property-dependent correction to imaging systematics. We derive systematic weights following the same linear regression method used for other DESI tracers, but do so separately on ELG subsamples to provide a physically-informed alternative to the fiducial, neural-network-based approach. In doing so, we show that the deeper imaging in the Dark Energy Survey (DES) footprint leads to a higher overall number density but a lack of targets with extreme $g-r$ and $r-z$ colors. ELGs in the DES region also show a distinct redshift distribution when subsampled by position in the $g-r$ vs. $r-z$ plane. To address these effects, we implement a separate treatment of the DES footprint within the DESI catalog production pipeline, which is generally well-motivated and, in some cases, imperative for accurate clustering measurements. With DES treated separately, we find that property-dependent systematic weights further mitigate spurious clustering signal in $\sim$10% of subsamples, while the fiducial scheme remains optimal for the full sample.

Hagen, T. [Utah U.]↗

NASA NTRS1984

Constraints on the anisotropy of the velocity dispersion of the Coma cluster

The dynamics of the Coma cluster are probed through the study of spiral galaxies that have been stripped of gas and ellipticals that have been tidally stripped. Since such stripping is most effective in regions where intergalaxy or cluster gas density is highest, the affected galaxies probably have orbits that take them through the center of the cluster. The Bothun et al. (1982, 1983) data analyzed indicate that the spirals in the cluster center are deficient in H I relative to other spiral members. Observations by Strom and Strom (1978) may indicate the tidal stripping of elliptical galaxies near the cluster center. For both data sets, the most likely models are everywhere isotropic.

Pryor, C.↗

NASA NTRS2003

A Fast Implementation of the ISOCLUS Algorithm

Unsupervised clustering is a fundamental tool in numerous image processing and remote sensing applications. For example, unsupervised clustering is often used to obtain vegetation maps of an area of interest. This approach is useful when reliable training data are either scarce or expensive, and when relatively little a priori information about the data is available. Unsupervised clustering methods play a significant role in the pursuit of unsupervised classification. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points (or samples) in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute a set of cluster centers in d-space. Although there is no specific optimization criterion, the algorithm is similar in spirit to the well known k-means clustering method in which the objective is to minimize the average squared distance of each point to its nearest center, called the average distortion. One significant feature of ISOCLUS over k-means is that clusters may be merged or split, and so the final number of clusters may be different from the number k supplied as part of the input. This algorithm will be described in later in this paper. The ISOCLUS algorithm can run very slowly, particularly on large data sets. Given its wide use in remote sensing, its efficient computation is an important goal. We have developed a fast implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm, the filtering algorithm, by Kanungo et al.. They showed that, by storing the data in a kd-tree, it was possible to significantly reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm. For technical reasons, which are explained later, it is necessary to make a minor modification to the ISOCLUS specification. We provide empirical evidence, on both synthetic and Landsat image data sets, that our algorithm's performance is essentially the same as that of ISOCLUS, but with significantly lower running times. We show that our algorithm runs from 3 to 30 times faster than a straightforward implementation of ISOCLUS. Our adaptation of the filtering algorithm involves the efficient computation of a number of cluster statistics that are needed for ISOCLUS, but not for k-means.

Memarsadeghi, Nargess↗

NASA NTRS1972

Computer program documentation: ISOCLS iterative self-organizing clustering program, program C094

The author has identified the following significant results. This program implements an algorithm which, ideally, sorts a given set of multivariate data points into similar groups or clusters. The program is intended for use in the evaluation of multispectral scanner data; however, the algorithm could be used for other data types as well. The user may specify a set of initial estimated cluster means to begin the procedure, or he may begin with the assumption that all the data belongs to one cluster. The procedure is initiatized by assigning each data point to the nearest (in absolute distance) cluster mean. If no initial cluster means were input, all of the data is assigned to cluster 1. The means and standard deviations are calculated for each cluster.

Minter, R. T.↗

DOE OSTI2022

Cosmology with the Roman Space Telescope – Synergies with CMB lensing

ABSTRACT We explore synergies between the Nancy Grace Roman Space Telescope and CMB lensing data to constrain dark energy and modified gravity scenarios. A simulated likelihood analysis of the galaxy clustering and weak lensing data from the Roman Space Telescope High Latitude Survey combined with CMB lensing data from the Simons Observatory is undertaken, marginalizing over important astrophysical effects and calibration uncertainties. Included in the modelling are the effects of baryons on small-scale clustering, scale-dependent growth suppression by neutrinos, as well as uncertainties in the galaxy clustering biases, in the intrinsic alignment contributions to the lensing signal, in the redshift distributions, and in the galaxy shape calibration. The addition of CMB lensing roughly doubles the dark energy figure-of-merit from Roman photometric survey data alone, varying from a factor of 1.7 to 2.4 improvement depending on the particular Roman survey configuration. Alternatively, the inclusion of CMB lensing information can compensate for uncertainties in the Roman galaxy shape calibration if it falls below the design goals. Furthermore, we report the first forecast of Roman constraints on a model-independent structure growth, parametrized by σ8(z), and on the Hu-Sawicki f(R) gravity as well as an improved forecast of the phenomenological (Σ0, μ0) model. We find that CMB lensing plays a crucial role in constraining σ8(z) at z > 2, with percent-level constraints forecasted out to z = 4. CMB lensing information does not improve constraints on the f(R) model substantially. It does, however, increase the (Σ0, μ0) figure-of-merit by a factor of about 1.5.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Rapid detection of rare events from in situ X-ray diffraction data using machine learning

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics ( e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Zheng, Weijian↗

NASA NTRS2006

Star Formation Rates in Cooling Flow Clusters: A UV Pilot Study with Archival XMM-Newton Optical Monitor Data

We have analyzed XMM-Newton Optical Monitor (OM) UV (180-400 nm) data for a sample of 33 galaxies. 30 are cluster member galaxies, and nine of these are central cluster galaxies (CCGs) in cooling flow clusters having mass deposition rates which span a range of 8 - 525 Solar Mass/yr. By comparing the ratio of UV to 2MASS J band fluxes, we find a significant UV excess in many, but not all, cooling flow CCGs, a finding consistent with the outcome of previous studies based on optical imaging data (McNamara & O'Connell 1989; Cardiel, Gorgas, & Aragon-Salamanca 1998; Crawford et al. 1999). This UV excess is a direct indication of the presence of young massive stars, and therefore recent star formation, in these galaxies. Using the Starburst99 spectral energy distribution (SED) model of continuous star formation over a 900 Myr period, we derive star formation rates of 0.2 - 219 solar Mass/yr for the cooling flow sample. For 2/3 of this sample it is possible to equate Chandra/XMM cooling flow mass deposition rates with UV inferred star formation rates, for a combination of starburst lifetime and IMF slope. This is a pilot study of the well populated XMM UV cluster archive and a more extensive follow up study is currently underway.

Hicks, A. K.↗

NASA NTRS2005

Star Formation Rates in Cooling Flow Clusters: A UV Pilot Study with Archival XMM-Newton Optical Monitor Data

We have analyzed XMM-Newton Optical Monitor (OM) UV (180-400 nm) data for a sample of 33 galaxies. 30 are cluster member galaxies, and nine of these are central cluster galaxies (CCGs) in cooling flow clusters having mass deposition rates which span a range of 8 - 525 solar mass per year. By comparing the ratio of UV to 2MASS J band fluxes, we find a significant UV excess in many, but not all, cooling flow CCGs, a finding consistent with the outcome of previous studies based on optical imaging data (McNamara & O Connell 1989; Cardiel, Gorgas, & Aragon-Salamanca 1998; Crawford et al. 1999). This UV excess is a direct indication of the presence of young massive stars, and therefore recent star formation, in these galaxies. Using the Starburst99 spectral energy distribution (SED) model of continuous star formation over a 900 Myr period, we derive star formation rates of 0.2 - 219 solar mass per year for the cooling flow sample. For 2/3 of this sample it is possible to equate Chandra/XMM cooling flow mass deposition rates with UV inferred star formation rates, for a combination of starburst lifetime and IMF slope. This is a pilot study of the well populated XMM UV cluster archive and a more extensive follow up study is currently underway.

Hicks, A. K.↗

DOE OSTI2024

Masses of Sunyaev-Zel’dovich galaxy clusters detected by the Atacama Cosmology Telescope: Stacked lensing measurements with Subaru HSC year 3 data

We present a stacked lensing analysis of 96 galaxy clusters selected by the thermal Sunyaev-Zel’dovich (SZ) effect in maps of the cosmic microwave background (CMB). We select foreground galaxy clusters with a 5 σ -level SZ threshold in CMB observations from the Atacama Cosmology Telescope, while we define background source galaxies for the lensing analysis with secure photometric redshift cuts in Year 3 data of the Subaru Hyper Suprime Cam survey. We detect the stacked lensing signal in the range of 0.1 < R [ h - 1 Mpc ] < 100 in each of three cluster redshift bins, 0.092 < z ≤ 0.445 , 0.445 < z ≤ 0.695 , and 0.695 < z ≤ 1.180 , with 32 galaxy clusters in each bin. The cumulative signal-to-noise ratios of the lensing signal are 14.6, 12.0, and 6.6, respectively. Using a halo-based forward model, we then constrain statistical relationships between the mass inferred from the SZ observation (i.e. SZ mass) and the total mass derived from our stacked lensing measurements. At the average SZ mass in the cluster sample ( 2.1 - 2.4 × 10 14 h - 1 M ⊙ ), our likelihood analysis shows that the average total mass differs from the SZ counterpart by a factor of 1.3 ± 0.2 , 1.6 ± 0.2 , and 1.6 ± 0.3 (68%) in the aforementioned redshift ranges, respectively. Our limits are consistent with previous lensing measurements, and we find that the cluster modeling choices can introduce a 1 σ -level difference in our parameter inferences.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2024

Masses of Sunyaev-Zel’dovich Galaxy Clusters Detected by the Atacama Cosmology Telescope: Stacked Lensing Measurements with Subaru HSC Year 3 Data

We present a stacked lensing analysis of 96 galaxy clusters selected by the thermal Sunyaev-Zel’dovich (SZ) effect in maps of the cosmic microwave background (CMB). We select foreground galaxy clusters with a 5σ-level SZ threshold in CMB observations from the Atacama Cosmology Telescope, while we define background source galaxies for the lensing analysis with secure photometric redshift cuts in Year 3 data of the Subaru Hyper Suprime Cam survey. We detect the stacked lensing signal in the range of 0.1 < R[h −1 Mpc] < 100 in each of three cluster redshift bins, 0.092 < z ≤ 0.445, 0.445 < z ≤ 0.695, and 0.695 < z ≤ 1.180, with 32 galaxy clusters in each bin. The cumulative signal-to-noise ratios of the lensing signal are 14.6, 12.0, and 6.6, respectively. Using a halo-based forward model, we then constrain statistical relationships between the mass inferred from the SZ observation (i.e. SZ mass) and the total mass derived from our stacked lensing measurements. At the average SZ mass in the cluster sample (2.1 − 2.4 × 10 14 h −1 M⊙), our likelihood analysis shows that the average total mass differs from the SZ counterpart by a factor of 1.3 ± 0.2, 1.6 ± 0.2, and 1.6 ± 0.3 (68%) in the aforementioned redshift ranges, respectively. Our limits are consistent with previous lensing measurements, and we find that the cluster modeling choices can introduce a 1σ-level difference in our parameter inferences.

Masato Shirasaki↗