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DOE OSTI2022

Probing the Gluonic Structure of the Deuteron with J / ψ Photoproduction in d + Au Ultraperipheral Collisions

Understanding gluon density distributions and how they are modified in nuclei are among the most important goals in nuclear physics. In recent years, diffractive vector meson production measured in ultraperipheral collisions (UPCs) at heavy-ion colliders has provided a new tool for probing the gluon density. In this Letter, we report the first measurement of J/psi photoproduction off the deuteron in UPCs at the center-of-mass energy root √S-NN = 200 GeV in d + Au collisions. The differential cross section as a function of momentum transfer -t is measured. In addition, data with a neutron tagged in the deuteron-going zero-degree calorimeter is investigated for the first time, which is found to be consistent with the expectation of incoherent diffractive scattering at low momentum transfer. Theoretical predictions based on the color glass condensate saturation model and the leading twist approximation nuclear shadowing model are compared with the data quantitatively. A better agreement with the saturation model has been observed. With the current measurement, the results are found to be directly sensitive to the gluon density distribution of the deuteron and the deuteron breakup process, which provides insights into the nuclear gluonic structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Baryon–Antibaryon Photoproduction off the Proton

The GlueX Experiment at Jefferson Lab uses linearly polarized photon beam to study the photoproduction of light mesons and baryons off a proton target with unprecedented precision. We have observed p¯p photoproduction and, for the first time, ?¯? photoproduction from thresholds up to E = 11.6 GeV. We reproduce the observed kinematic distributions with a phenomenological model that includes t-channel Regge exchange and double Regge exchange. The model uses few parameters to match all the kinematic spectra via an optimization algorithm developed specifically for this purpose. We report the total cross sections of the two reactions, as well as differential cross sections with respect to various momentum transfers and invariant mass spectra of p¯p, ?¯?, p¯? systems which show enhancements right above production thresholds. Furthermore, the beam asymmetries of the p¯p system are extracted from the GlueX data to further probe the nature of the t-channel Regge exchanges. We also report preliminary measurements of the spin polarizations of the individual hyperons. No prominent resonance structure was found in these reaction channels, but the angular distributions and the correlation of longitudinal momenta indicate the existence of multiple production mechanisms. Besides a dominant forward peak in the polar angle of the baryon-antibaryon pair in t-channel production, we see wide-angle antibaryon distributions different from that of the baryon. In the hyperon channel, we see a clear separation between the photoproduction of the ?¯?, p¯? systems.

Li, Hao↗

DOE OSTI2024

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

TMD Handbook

This handbook provides a comprehensive review of transverse-momentum-dependent parton distribution functions and fragmentation functions, commonly referred to as transverse momentum distributions (TMDs). TMDs describe the distribution of partons inside the proton and other hadrons with respect to both their longitudinal and transverse momenta. They provide unique insight into the internal momentum and spin structure of hadrons, and are a key ingredient in the description of many collider physics cross sections. Understanding TMDs requires a combination of theoretical techniques from quantum field theory, nonperturbative calculations using lattice QCD, and phenomenological analysis of experimental data. The handbook covers a wide range of topics, from theoretical foundations to experimental analyses, as well as recent developments and future directions. It is intended to provide an essential reference for researchers and graduate students interested in understanding the structure of hadrons and the dynamics of partons in high energy collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Geographic_Distribution_of_Populus_trichocarpa_Genotypes_by_DBSCAN_Cluster

Aninteractive mapshowingPopulus trichocarpaGWAS sub-population structure identified by DBSCAN clustering, which were derived from a UMAP projection of the top 8 PCs of LD-pruned pangenome SNP data. Geographic origins are searchable by genotype or river system using the search bar.

09 BIOMASS FUELS↗

DOE OSTI2021

Microstructure and water absorption of ancient concrete from Pompeii: An integrated synchrotron microtomography and neutron radiography characterization

There is renewed interest in using advanced techniques to characterize ancient Roman concrete due to its exceptional durability and low-carbon footprint. In the present work, samples were drilled from the “Hospitium” in Pompeii and were analyzed by synchrotron microtomography (μCT) and neutron radiography to study how the microstructure, including the presence of induced cracks, affects their water adsorption. The water distribution and absorptivity were quantified by neutron radiography. The 3D crack propagation, pore size distribution and orientation, tortuosity, and connectivity were analyzed from μCT results using advanced imaging methods. Porosity was also measured by mercury intrusion porosimetry (MIP) as a reference. Ductile fracture patterns were observed once cracks were introduced. Compared to Portland cement mortar/concrete, the Pompeii samples had relatively high porosity, low connectivity, and a similar coefficient of capillary penetration. In addition, permeability was predicted from models based on percolation theory and pore structure data to evaluate the fluid transport properties. Understanding the microstructure of ancient Pompeii concrete is important because it could inspire the development of modern concrete with high durability.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Nematic fluctuations in iron-oxychalcogenide Mott insulators

Nematic fluctuations occur in a wide range physical systems from biological molecules to cuprates and iron pnictide high- T c superconductors. It is unclear whether nematicity in pnictides arises from electronic spin or orbital degrees of freedom. We studied the iron-based Mott insulators La 2 O 2 Fe 2 OM 2 M = (S, Se), which are structurally similar to pnictides. Nuclear magnetic resonance revealed a critical slowing down of nematic fluctuations and complementary Mössbauerr spectroscopy data showed a change of electrical field gradient. The neutron pair distribution function technique detected local C 2 fluctuations while neutron diffraction indicates that global C 4 symmetry is preserved. A geometrically frustrated Heisenberg model with biquadratic and single-ion anisotropic terms provides the interpretation of the low temperature magnetic fluctuations. The nematicity is not due to spontaneous orbital order, instead it is linked to geometrically frustrated magnetism based on orbital selectivity. This study highlights the interplay between orbital order and spin fluctuations in nematicity.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Magnetic structures and excitations in sawtooth olivine chalcogenides Mn 2 SiX 4 (X = S, Se)

The Mn lattice in olivine chalcogenide Mn 2 SiX 4 (X = S, Se) compounds forms a sawtooth, which is of special interest in magnetism owing to the possibility of realizing flat bands in magnon spectra, a key component in magnonics. In this work, we investigate the Mn 2 SiX 4 olivines using magnetic susceptibility, and X-ray and neutron diffraction. We have determined the average and local crystal structures of Mn 2 SiS 4 and Mn 2 SiSe 4 using synchrotron X-ray, neutron diffraction, and X-ray total scattering data followed by Rietveld and pair distribution function analyses. It is found from the pair distribution function analysis that the Mn triangle that constitutes the sawtooth is isosceles in Mn 2 SiS 4 and Mn 2 SiSe 4 . The temperature evolution of magnetic susceptibility of Mn 2 SiS 4 and Mn 2 SiSe 4 shows anomalies below 83 K and 70 K, respectively, associated with magnetic ordering. From the neutron powder diffraction measurements the magnetic space groups of Mn 2 SiS 4 and Mn 2 SiSe 4 are found to be Pnma and Pnm'a', respectively. Here, we find that the Mn spins adopt a ferromagnetic alignment on the sawtooth in both Mn 2 SiS 4 and Mn 2 SiSe 4 but along different crystallographic directions for the S and the Se compounds. From the temperature evolution of Mn magnetic moments obtained from refining neutron diffraction data, the transition temperatures are accurately determined as T N (S) = 83(2) K and T N (Se) = 70.0(5) K. Broad diffuse magnetic peaks are observed in both the compounds, and are prominently seen close to T N , suggesting the presence of a short-range magnetic order. The magnetic excitations studied using inelastic neutron scattering reveal a magnon excitation with an energy corresponding to approximately 4.5 meV in both S and Se compounds. Spin correlations are observed to persist up to 125 K much above the ordering temperature and we suggest the possibility of short-range spin correlations responsible for this.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Watershed Workflow

Watershed Workflow aims to lower the bar on leveraging open data products in hyperresolution watershed hydrologic models. Integrated, distributed hydrologic models of watersheds require large amounts and complexity of data including meteorological forcing datasets; land cover datasets; watershed geometry, hydrogography, and elevation datasets; and subsurface soil and rock structural datasets. Increasingly open data products meeting this need are available through U.S. governmental and other agencies via APIs and other electronic means, but are not available in the same format, through the same API, or in the same coordinate system as one another, and present other challenges in manipulations across scales. This product automates the discovery, download, curation, and integration of these types of datasets into a common format for use with hydrologic models.

Coon, EthanT [Oak Ridge National Laboratory] (0000↗

DOE OSTI2023

Association between soil organic carbon and calcium in acidic grassland soils from Point Reyes National Seashore, CA

All data is collected from across 3 soil cores spanning a pH gradient at Point Reyes National Seashore, as part of a recent publication. The bulk data csv includes hygroscopic moisture content, soil pH, total C and N values, particle size distributions, cation exchange capacity and extractable cations. The STXM data package includes raw spectra data from scanning transmission X-ray microscopy C near-edge X-ray absorption fine structure spectroscopy analysis, extracted from STXM Image Reader analysis and then subsequently normalised in Athena (see details in paper). The data file also contains a bulk chemistry and mineralogy dataset, including total and trace element contents from 3 depths and mineral compositions as measured at BL 11-3 SSRL and semi-quantified in HighScore. Finally the final data file is calcium K-edge X-ray absorption near-edge structure spectroscopy (BL 4-3) and micro-XANES and micro-X-ray fluorescence (BL 14-3b) data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

PETSc Users Manual (Revision 3.15)

This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) for the numerical solution of partial differential equations and related problems on high-performance computers. PETSc is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

PETSc/TAO Users Manual: Revision 3.18

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

PETSc/TAO Users Manual (Rev. 3.19)

This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) and the Toolkit for Advanced Optimization (TAO) for the numerical solution of partial differential equations and related problems on high-performance computers. PETSc/TAO is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication. PETSc/TAO includes a large suite of parallel linear solvers, nonlinear solvers, time integrators, and opti mization that may be used in application codes written in Fortran, C, C++, and Python (via petsc4py; see Getting Started). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than packages such as MATLAB or a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++, python, or Fortran and experience using a debugger such as gdb or lldb, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself. For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. There are several packages, built on PETSc, that may satisfy your needs without requiring directly using PETSc. We recommend reviewing these packages functionality before starting to code directly with PETSc. PETSc can be used to provide a “MPI parallel linear solver” in an otherwise sequential, or OpenMP parallel code. This approach cannot provide extremely large improvements in the application time by utilizing large numbers of MPI processes but can still improve the performance. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect true scalability to large numbers of MPI processes. See PCMPI for details on how to utilize the PETSc MPI linear solver server. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc has been supported for twenty-five years; see mailing list information on our website for information on contacting support.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

PETSc/TAO Users Manual (Rev. 3.20)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Trends in the market structure of US residential solar PV installation, 2000 to 2016: An evolving industry

The US residential solar photovoltaic (PV) installation grew from fewer than 100 firms in 2000 to about 3000 firms by 2016. The rapid expansion of this emerging industry prompts questions about the installation industry's market structure, such as how market shares are distributed among installers and how interinstaller competition affects PV deployment. This article leverages a rich dataset to describe trends in the evolution of the US residential PV installation industry. The data show that most residential PV installers are relatively small companies, with about half of installers installing fewer than 5 systems. At the same time, a subset of high-volume installers have accumulated market share, especially from 2010 to 2016. Furthermore causes of this recent market concentration are explored, with an emphasis on the role of the emergence of alternative customer financing options in the United States.

14 SOLAR ENERGY↗

DOE OSTI2021

Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery

Not provided.

Engineering↗

DOE OSTI2022

Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. Here, in this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗