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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Source Analysis of Ozone Pollution in Liaoyuan City’s Atmosphere Based on Machine Learning Models and HYSPLIT Clustering Method

Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O 3 ) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O 3 concentrations. Finally, the HYSPLIT clustering method and CMAQ model are utilized to analyze inter-regional transport characteristics, identifying the causes of O 3 pollution. The results indicate that O 3 pollution in Liaoyuan exhibits a distinct seasonal pattern, with the highest concentrations found in spring and summer, peaking in the afternoon. Among the three ML models, the random forest model demonstrates the best predictive performance (R 2 = 0.9043). Feature importance identifies NO 2 as the primary driving factor, followed by meteorological conditions in the second quarter and land surface characteristics. Furthermore, regional transport significantly contributes to O 3 pollution, with approximately 80% of air mass trajectories in heavily polluted episodes originating from adjacent industrial areas and the sea. The combined effects of transboundary precursors and O 3 transport with local emissions and meteorological conditions further increase the O 3 pollution level. This study highlights the need to strengthen coordinated NO X and VOCs emission reductions and enhance regional joint prevention and control strategies in China.

HYSPLIT clustering↗
DOE OSTI2025

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

Abstract We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

Kainer, David (ORCID:0000000172714676)↗
DOE OSTI2022

Quantitative assessment of particle dispersion in polymeric composites and its effect on mechanical properties

In this work, an automated image analysis tool is developed to establish quantitative correlations between the particle/cluster size distribution and the mechanical properties of particle reinforced polymer composites (PRPC).This automated image analysis tool is developed within python programming software to process and analyze the microstructural images of the polymerbased composite materials. The spent coffee bean powder (SCBP) reinforced poly-propylene carbonate (PPC) polymer composite with differing wt.% of the filler is selected for the analysis. Detailed statistical analysis of the microstructural images reveals that ‘clustering of clusters’ is also presented in addition to the most commonly reported ‘clustering of particles’, and the distribution of particle/clusters is bimodal. Based on these findings, an effective volume fraction for the filler material is proposed to mainly capture the agglomeration effect. With this effective volume fraction, the standard rule-of-mixture model correctly captures the experimentally measured tensile strength and modulus as a function of filler wt.%. Further, the applicability of this effective volume fraction for other theoretical models is also analyzed. The detailed statistical analysis of the microstructure and the proposed effective volume fraction helps to develop a deeper quantitative understanding of the PRPC than the conventional qualitative correlation of microstructural features with the properties and failure processes.

36 MATERIALS SCIENCE↗
DOE OSTI2014

NuSTAR observations of the bullet cluster: constraints on inverse Compton emission

The search for diffuse non-thermal inverse Compton (IC) emission from galaxy clusters at hard X-ray energies has been undertaken with many instruments, with most detections being either of low significance or controversial. Because all prior telescopes sensitive at E > 10 keV do not focus light and have degree-scale fields of view, their backgrounds are both high and difficult to characterize. The associated uncertainties result in lower sensitivity to IC emission and a greater chance of false detection. In this work, we present 266 ks NuSTAR observations of the Bullet cluster, which is detected in the energy range 3-30 keV. NuSTAR's unprecedented hard X-ray focusing capability largely eliminates confusion between diffuse IC and point sources; however, at the highest energies, the background still dominates and must be well understood. To this end, we have developed a complete background model constructed of physically inspired components constrained by extragalactic survey field observations, the specific parameters of which are derived locally from data in non-source regions of target observations. Applying the background model to the Bullet cluster data, we find that the spectrum is well—but not perfectly—described as an isothermal plasma with kT = 14.2 ± 0.2 keV. To slightly improve the fit, a second temperature component is added, which appears to account for lower temperature emission from the cool core, pushing the primary component to kT ∼ 15.3 keV. We see no convincing need to invoke an IC component to describe the spectrum of the Bullet cluster, and instead argue that it is dominated at all energies by emission from purely thermal gas. The conservatively derived 90% upper limit on the IC flux of 1.1 × 10{sup –12} erg s{sup –1} cm{sup –2} (50-100 keV), implying a lower limit on B ≳ 0.2 μG, is barely consistent with detected fluxes previously reported. In addition to discussing the possible origin of this discrepancy, we remark on the potential implications of this analysis for the prospects for detecting IC in galaxy clusters in the future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

An examination of the precipitation behavior of proton irradiated dual phase 308L weldment filler materials

Voids, G phase particles, and Ni-Si rich clusters in proton irradiated dual phase 308L groove filler of a SA508–304 L dissimilar metal weldment are analyzed using advanced characterization techniques. These weldments are often used in light water nuclear reactors and are subject to enhanced corrosion and associated stress corrosion cracking (SCC). Radiation damage is known to accelerate SCC. Ni-Si enriched clusters were observed in proton irradiated γ austenite, while G phase M 6 Ni 16 Si 7 (where M transition metal element) precipitates were observed in proton irradiated δ ferrite. Compositional analysis of the G phase precipitates and Ni-Si clusters from STEM-EDS and APT are compared. Unlike G phase particles in proton irradiated δ ferrite, Ni-Si clusters in proton irradiated γ austenite are not rich in Mn. Both STEM-EDS and APT line-scan profiles of the Ni-Si clusters show that the Fe and Cr concentration gradient between matrix γ austenite and the Ni-Si clusters is not as sharp as those between matrix δ ferrite and G phase. Further, HR-STEM imaging indicates that the lattice parameter of the Ni-Si clusters is commensurate with γ austenite and the clusters do not represent the precipitation of a second phase. Finally, our analysis demonstrates the density and volume fraction of G phase particles and the density of voids scales with proton irradiation damage and the energy to recoils.

308L groove filler↗
DOE OSTI2023

Use of machine learning to analyze chemistry card sort tasks

Education researchers are deeply interested in understanding the way students organize their knowledge. Card sort tasks, which require students to group concepts, are one mechanism to infer a student’s organizational strategy. However, the limited resolution of card sort tasks means they necessarily miss some of the nuance in a student’s strategy. Here in this work, we propose new machine learning strategies that leverage a potentially richer source of student thinking: free-form written language justifications associated with student sorts. Using data from a university chemistry card sort task, we use vectorized representations of language and unsupervised learning techniques to generate qualitatively interpretable clusters, which can provide unique insight in how students organize their knowledge. We compared these to machine learning analysis of the students’ sorts themselves. Machine learning-generated clusters revealed different organizational strategies than those built into the task; for example, sorts by difficulty or even discipline. There were also many more categories generated by machine learning for what we would identify as more novice-like sorts and justifications than originally built into the task, suggesting students’ organizational strategies converge when they become more expert-like. Finally, we learned that categories generated by machine learning for students’ justifications did not always match the categories for their sorts, and these cases highlight the need for future research on students’ organizational strategies, both manually and aided by machine learning. In sum, the use of machine learning to analyze results from a card sort task has helped us gain a more nuanced understanding of students’ expertise, and demonstrates a promising tool to add to existing analytic methods for card sorts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Open Cluster Chemical Abundances and Mapping Survey. V. Chemical Abundances of CTIO/Hydra Clusters Using The Cannon

Open clusters are key chemical and age tracers of Milky Way evolution. While open clusters provide significant constraints on galaxy evolution, their use has been limited due to discrepancies in measuring abundances from different studies. We analyze medium-resolution ( R ~ 19,000) Cerro Tololo Inter-American Observatory/Hydra spectra of giant stars in 58 open clusters using The Cannon to determine [Fe/H], [Mg/Fe], [Si/Fe], [Al/Fe], and [O/Fe]. This work adds an additional 55 primarily southern hemisphere open clusters calibrated to the Sloan Digital Sky Survey/Apache Point Observatory Galactic Evolution Experiment DR16 metallicity system. This uniform analysis is compared to previous studies [Fe/H] measurements for 23 clusters and we present spectroscopic metallicities for the first time for 35 open clusters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Validation of the DESI-DR1 3x2-pt analysis: scale cut and shear ratio tests

Combined survey analyses of galaxy clustering and weak gravitational lensing (3x2-pt studies) will allow new and accurate tests of the standard cosmological model. However, careful validation is necessary to ensure that these cosmological constraints are not biased by uncertainties associated with the modelling of astrophysical or systematic effects. In this study we validate the combined 3x2-pt analysis of the Dark Energy Spectroscopic Instrument Data Release 1 (DESI-DR1) spectroscopic galaxy clustering and overlapping weak lensing datasets from the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES), and the Hyper-Suprime-Cam Survey (HSC). By propagating the modelling uncertainties associated with the non-linear matter power spectrum, non-linear galaxy bias and baryon feedback, we design scale cuts to ensure that measurements of the matter density and the amplitude of the matter power spectrum are biased by less than 30% of the statistical error. We also test the internal consistency of the data and weak lensing systematics by performing new measurements of the lensing shear ratio. We demonstrate that the DESI-DR1 shear ratios can be successfully fit by the same model used to describe cosmic shear correlations, and analyse the additional information that can be extracted about the source redshift distributions and intrinsic alignment parameters. This study serves as crucial preparation for the upcoming cosmological parameter analysis of these datasets.

Emas, N. [Swinburne U., Ctr. Astrophys. Supercompu↗
DOE OSTI2016

ALMA-SZ detection of a galaxy cluster merger shock at half the age of the universe

We present ALMA measurements of a merger shock using the thermal Sunyaev–Zel’dovich (SZ) effect signal, at the location of a radio relic in the famous El Gordo galaxy cluster at z≈0.9. Multi-wavelength analysis in combination with the archival Chandra data and a high-resolution radio image provides a consistent picture of the thermal and non-thermal signal variation across the shock front and helps to put robust constraints on the shock Mach number as well as the relic magnetic field. We employ a Bayesian analysis technique for modeling the SZ and X-ray data self-consistently, illustrating respective parameter degeneracies. Combined results indicate a shock with Mach number M=2.4{sub −0.6}{sup +1.3}, which in turn suggests a high value of the magnetic field (of the order of 4–10 μG) to account for the observed relic width at 2 GHz. At roughly half the current age of the universe, this is the highest-redshift direct detection of a cluster shock to date, and one of the first instances of an ALMA-SZ observation in a galaxy cluster. It shows the tremendous potential for future ALMA-SZ observations to detect merger shocks and other cluster substructures out to the highest redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Locking water molecules via ternary O–H⋯O intramolecular hydrogen bonds in perhydroxylated closo -dodecaborate

A multitude of applications related to perhydroxylated closo-dodecaborate B 12 (OH) 12 2− in the condensed phase are inseparable from the fundamental mechanisms underlying the high water orientation selectivity based on the base B 12 (OH) 12 2− . Herein, we directly compare the structural evolution of water clusters, ranging from monomer to hexamer, oriented by functional groups in the bases B 12 H 12 2− , B 12 H 11 OH 2− and B 12 (OH) 12 2− using multiple theoretical methods. A significant revelation is made regarding B 12 (OH) 12 2− : each additional water molecule is locked into the intramolecular hydrogen bond B–O–H ternary ring in an embedded form. This new pattern of water cluster growth suggests that B–(H–O)⋯H–O interactions prevail over the competition from water–hydrogen bonds (O⋯H–O), distinguishing it from the behavior observed in B 12 H 12 2− and B 12 H 11 OH 2− bases, in which competition arises from a mixed competing model involving dihydrogen bonds (B–H⋯H–O), conventional hydrogen bonds (B–(H–O)⋯H–O) and water hydrogen bonds (O⋯H–O). Through aqueous solvation and ab initio molecular dynamics analysis, we further demonstrate the largest water clusters in the first hydrated shell with exceptional thermodynamic stability around B 12 (OH) 12 2− . These findings provide a solid scientific foundation for the design of boron cluster chemistry incorporating hydroxyl-group-modified borate salts with potential implications for various applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

TU-D-207B-05: Intra-Tumor Partitioning and Texture Analysis of DCE-MRI Identifies Relevant Tumor Subregions to Predict Early Pathological Response of Breast Cancer to Neoadjuvant Chemotherapy

Purpose: To predict early pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multi-region analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Methods: In this institution review board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with a high-temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Results: Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast wash-out were statistically significant (p< 0.05) after correcting for multiple testing, with area under the ROC curve or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (p = 0.002) in leave-one-out cross validation. This improved upon conventional imaging predictors such as tumor volume (AUC=0.53) and texture features based on whole-tumor analysis (AUC=0.65). Conclusion: The heterogeneity of the tumor subregion associated with fast wash-out on DCE-MRI predicted early pathological response to neoadjuvant chemotherapy in breast cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Dark energy survey year 3 results: High-precision measurement and modeling of galaxy-galaxy lensing

We present and characterize the galaxy-galaxy lensing signal measured using the first three years of data from the Dark Energy Survey (DES Y3) covering 4132 deg 2 . These galaxy-galaxy measurements are used in the DES Y3 3 × 2 pt cosmological analysis, which combines weak lensing and galaxy clustering information. We use two lens samples: a magnitude-limited sample and the redmagic sample, which span the redshift range ∼ 0.2 – 1 with 10.7 and 2.6 M galaxies, respectively. For the source catalog, we use the metacalibration shape sample, consisting of ≃ 100 M galaxies separated into four tomographic bins. Our galaxy-galaxy lensing estimator is the mean tangential shear, for which we obtain a total SNR of ∼ 148 for maglim ( ∼ 120 for redmagic), and ∼ 67 ( ∼ 55 ) after applying the scale cuts of 6 Mpc / h . Thus we reach percent-level statistical precision, which requires that our modeling and systematic-error control be of comparable accuracy. The tangential shear model used in the 3 × 2 pt cosmological analysis includes lens magnification, a five-parameter intrinsic alignment model, marginalization over a point mass to remove information from small scales and a linear galaxy bias model validated with higher-order terms. We explore the impact of these choices on the tangential shear observable and study the significance of effects not included in our model, such as reduced shear, source magnification, and source clustering. We also test the robustness of our measurements to various observational and systematics effects, such as the impact of observing conditions, lens-source clustering, random-point subtraction, scale-dependent metacalibration responses, point spread function residuals, and B modes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Resurrecting Hitomi for Decaying Dark Matter and Forecasting Leading Sensitivity for XRISM

The Hitomi x-ray satellite mission carried unique high-resolution spectrometers that were set to revolutionize the search for sterile neutrino dark matter (DM) by looking for narrow x-ray lines arising from DM decays. Unfortunately, the satellite was lost shortly after launch, and to date the only analysis using Hitomi for DM decay used data taken towards the Perseus cluster. Here, in this work we present a significantly more sensitive search from an analysis of archival Hitomi data towards blank sky locations, searching for DM decaying in our own Milky Way. The recently launched XRISM satellite has nearly identical soft-x-ray spectral capabilities to Hitomi; we project the full-mission sensitivity of XRISM for analyses of their future blank-sky data, and we find that XRISM will have the leading sensitivity to decaying DM for masses between roughly 1 to 18 keV, with important implications for sterile neutrino and heavy axionlike particle DM scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

CHEMICAL ABUNDANCE ANALYSIS OF MOVING GROUP W11450 (LATHAM 1)

We present elemental abundances for all seven stars in Moving Group W11450 (Latham 1) to determine if they may be chemically related. These stars appear to be both spatially and kinematically related, but no spectroscopic abundance analysis exists in literature. Abundances for eight elements were derived via equivalent width analyses of high-resolution (R ∼ 60,000), high-signal-to-noise ratio (〈S/N〉∼100) spectra obtained with the Otto Struve 2.1 m telescope and the Sandiford Echelle Spectrograph at McDonald Observatory. The large star-to-star scatter in metallicity, −0.55 ≤ [Fe/H] ⩽ 0.06 dex (σ = 0.25), implies these stars were not produced from the same chemically homogeneous molecular cloud, and are therefore not part of a remnant or open cluster as previously proposed. Prior to this analysis, it was suggested that two stars in the group, W11449 and W11450, are possible wide binaries. The candidate wide binary pair show similar chemical abundance patterns with not only iron but with other elements analyzed in this study, suggesting the proposed connection between these two stars may be real.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Dark Energy Survey Year 3 results: $w$CDM cosmology from simulation-based inference with persistent homology on the sphere

We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how features like clusters and voids evolve across density thresholds. For the first time, we apply spherical persistent homology to galaxy survey data through the algorithm TopoS2, which is optimized for curved-sky analyses and HEALPix compatibility. Employing a simulation-based inference framework with the Gower Street simulation suite, specifically designed to mimic DES Y3 data properties, we extract topological summary statistics from convergence maps across multiple smoothing scales and redshift bins. After neural network compression of these statistics, we estimate the likelihood function and validate our analysis against baryonic feedback effects, finding minimal biases (under $0.3σ$) in the $Ω_\mathrm{m}-S_8$ plane. Assuming the $w$CDM model, our combined Betti numbers and second moments analysis yields $S_8 = 0.821 \pm 0.018$ and $Ω_\mathrm{m} = 0.304\pm0.037$-constraints 70% tighter than those from cosmic shear two-point statistics in the same parameter plane. Our results demonstrate that topological methods provide a powerful and robust framework for extracting cosmological information, with our spherical methodology readily applicable to upcoming Stage IV wide-field galaxy surveys.

Prat, J. [Nordita; Royal Inst. Tech., Sodertalje; ↗
DOE OSTI2024

The SRG/eROSITA All-Sky Survey: Dark Energy Survey year 3 weak gravitational lensing by eRASS1 selected galaxy clusters

Context. Number counts of galaxy clusters across redshift are a powerful cosmological probe if a precise and accurate reconstruction of the underlying mass distribution is performed – a challenge called mass calibration. With the advent of wide and deep photometric surveys, weak gravitational lensing (WL) by clusters has become the method of choice for this measurement. Aims. We measured and validated the WL signature in the shape of galaxies observed in the first three years of the Dark Energy Survey (DES Y3) caused by galaxy clusters and groups selected in the first all-sky survey performed by SRG (Spectrum Roentgen Gamma)/eROSITA (eRASS1). These data were then used to determine the scaling between the X-ray photon count rate of the clusters and their halo mass and redshift. Methods. We empirically determined the degree of cluster member contamination in our background source sample. The individual cluster shear profiles were then analyzed with a Bayesian population model that self-consistently accounts for the lens sample selection and contamination and includes marginalization over a host of instrumental and astrophysical systematics. To quantify the accuracy of the mass extraction of that model, we performed mass measurements on mock cluster catalogs with realistic synthetic shear profiles. This allowed us to establish that hydrodynamical modeling uncertainties at low lens redshifts (z < 0.6) are the dominant systematic limitation. At high lens redshift, the uncertainties of the sources’ photometric redshift calibration dominate. Results. With regard to the X-ray count rate to halo mass relation, we determined its amplitude, its mass trend, the redshift evolution of the mass trend, the deviation from self-similar redshift evolution, and the intrinsic scatter around this relation. Conclusions. The mass calibration analysis performed here sets the stage for a joint analysis with the number counts of eRASS1 clusters to constrain a host of cosmological parameters. We demonstrate that WL mass calibration of galaxy clusters can be performed successfully with source galaxies whose calibration was performed primarily for cosmic shear experiments, opening the way for the cluster cosmological exploitation of future optical and NIR surveys like Euclid and LSST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Exploring charge transport dynamics in a cryogenic P-type germanium detector

Abstract This study explores the dynamics of charge transport within a cryogenic P-type Ge particle detector, fabricated from a crystal cultivated at the University of South Dakota. By subjecting the detector to cryogenic temperatures and an Am-241 source, we observe evolving charge dynamics and the emergence of cluster dipole states, leading to the impact ionization process at 40 mK. Our analysis focuses on crucial parameters: the zero-field cross-section of cluster dipole states and the binding energy of these states. For the Ge detector in our investigation, the zero-field cross-section of cluster dipole states is determined to be 8.45 × 10 −11 ± 4.22 × 10 −12 cm 2 . Examination of the binding energy associated with cluster dipole states, formed by charge trapping onto dipole states during the freeze-out process, reveals a value of 0.034 ± 0.0017 meV. These findings shed light on the intricate charge states influenced by the interplay of temperature and electric field, with potential implications for the sensitivity in detecting low-mass dark matter.

Physics↗