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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Overcoming photobleaching in imaging of single barium atoms in a solid xenon matrix

Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is 136 Xe, which would double beta decay into 136 Ba. Detecting the single 136 Ba daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Dispersive and nondispersive 𝐾-matrix formalisms

The modeling of coupled-channel effects has become increasingly important due to the availability of highly precise data for a large variety of hadronic (re)scattering processes. The 𝐾-matrix is a powerful, yet comparatively simple, method to describe scattering amplitudes, including coupled-channel effects, with the aim of interpreting experimental data. Throughout the literature, a range of dispersive and nondispersive 𝐾-matrix methods are employed. Here, we compare the dispersive and nondispersive formulations in the context of the N/D method. It is shown that the methods are equivalent in the physical region under 𝐾-matrix reparametrization. Differences away from the physical region are examined. Applications to synthetic data are used to illustrate the effects of model choices concerning form factors and the application of dispersion relations, with the goal of clarifying best practices. We find no clear preference with regard to dispersive modeling. In contrast, we find that interpretational ambiguity of the bare model parameters—and even of the form of the bare model—is endemic, and recommend a thorough sampling of data and model spaces to assess conclusion robustness.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Towards excitations and dynamical quantities in correlated lattices with density matrix embedding theory

Density matrix embedding theory (DMET) provides a framework to describe ground-state expectation values in strongly correlated systems, but its extension to dynamical quantities is still an open problem. We show one route to obtaining excitations and dynamical spectral functions by using the techniques of DMET to approximate the matrix elements that arise in a single-mode inspired excitation ansatz. We demonstrate this approach in the one-dimensional Hubbard model, comparing the neutral excitations, single-particle density of states, charge, and spin dynamical structure factors to benchmarks from the Bethe ansatz and density matrix renormalization group. Finally, our work highlights the potential of these ideas in building computationally efficient approaches for dynamical quantities.

1-dimensional systems↗

DOE OSTI2021

Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System

Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.

Dahale, Shweta↗

DOE OSTI2020

Estimation Matrix Calibration of PMU Data-driven State Estimation Using Neural Network

Linear state estimation (LSE) is a phasor measurement unit (PMU) data-based power system state estimation that incorporates a linear measurement model in rectangular coordinates. Due to the high computational efficiency and high observational time-resolution, LSE can act as a supplementary state estimation in a wide-area monitoring system (WAMS). The performance of LSE is relatively sensitive to noises in measurements. Therefore, the estimation accuracy relies heavily on the accuracy of the estimation matrix, which is directly influenced by the measurement weight matrix. This paper proposes two novel calibration method of the estimation matrix using neural networks. One is based on the minimum absolute network loss (ANL), and the other is based on the minimum average squared network loss (ASNL). Both methods are tested and compared with LSE algorithms on the IEEE 14-bus system

neural network↗

DOE OSTI2022

Statistical uncertainty of fission matrix eigenvalues using perturbation theory

Eigenvalue search of high dominance ratio systems may be slow to converge. The fission matrix element is defined by its element (FM){sub ij}, which are the probability for a neutron born in cell i to create a fission in cell j for a spatial mesh of n{sub i}*n{sub j} elements. Fission matrices are used in Monte Carlo criticality simulations to enhance computing speed, but also to find higher order eigenvalues. However, few studies have been made on the link between statistical uncertainties of fission matrix elements and eigenvalues uncertainties. Thus, dominance ratio statistical uncertainties remain unknown. This paper uses a new generalized perturbation theory (GPT) method to estimate sensitivities of eigenvalues to fission matrix elements and then to calculate dominance ratio uncertainties.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Carbon Storage Technical Viability Approach (CS TVA) Matrix

The Carbon Storage Technical Viability Approach (CS TVA) Matrix is a knowledge framework developed to outline the information needed for geologic carbon storage. The CS TVA Matrix contains 5 categories, 14 sub-categories, and 47 components. This framework can be leveraged to assess the availability of data and information needed for a carbon storage project. The information categories of the matrix are tied to a list of required data using weighted mapping, published herein.

carbon storage↗

DOE OSTI2023

NISQ algorithm for the matrix elements of a generic observable

The calculation of off-diagonal matrix elements has various applications in fields such as nuclear physics and quantum chemistry. In this paper, we present a noisy intermediate scale quantum algorithm for estimating the diagonal and off-diagonal matrix elements of a generic observable in the energy eigenbasis of a given Hamiltonian without explicitly preparing its eigenstates. By means of numerical simulations we show that this approach finds many of the matrix elements for the one and two qubits cases. Specifically, while in the first case, one can initialize the ansatz parameters over a broad interval, in the latter the optimization landscape can significantly slow down the speed of convergence and one should therefore be careful to restrict the initialization to a smaller range of parameters.

Physics↗

DOE OSTI2020

Smoothers for Matrix-Free Algebraic Multigrid Preconditioning of High-Order Finite Elements

We investigate smoothers for use in matrix-free algebraic multigrid (AMG) preconditioning of high-order finite element problems. These AMG preconditioners are matrix-free in the sense that they are built from a related low-order refined finite element problem whose system matrix can be much more rapidly assembled than the high-order problem. Our proposed smoother, which we call distributive relaxation, is more robust to the anisotropy present in many low-order refined meshes which feature a clustering of nodes near the boundaries between high-order finite elements. For solving the low-order refined problem, we show that this new distributive relaxation smoother possesses significantly improved performance compared to more traditional smoothers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Nuclear Matrix Elements for Neutrinoless Double-Beta Decay from Lattice QCD

Neutrinoless double-beta decay (0vbb) is a hypothetical nuclear decay that is only possible if the neutrino is a Majorana fermion. Experimental searches for this process with ever-increasing sensitivity have placed strong constraints on the 0vbb half-lives of relevant isotopes. Relating these experimental half-lives to the underlying particle physics -- the effective Majorana mass of the neutrino -- requires understanding of the nuclear matrix elements for the transition. These matrix elements can be computed within a nuclear effective field theory framework, but input from lattice QCD is necessary to constrain low-energy constants relevant for the decay. This talk will discuss calculations of these matrix elements using lattice QCD and the implications for determination of nuclear EFT parameters.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

HYBRID Modeling Validation and Verification Status Matrix

The HYBRID modeling repository is a premier resource for integrated energy systems modeling. HYBRID models have been developed since 2015 to describe the physical operation of tightly coupled thermal systems including power generators, thermal transport systems, thermal storage, thermal-to-electric conversion systems, and other thermal applications. Due to the increased size of the repository, a concise summary matrix of available models is desired. This matrix will consolidate not only the list of available models but also indicate original information sources, publications that have model examples, and level of validation and verification that exists for the models. Validation and verification (V&V) levels begin from simplified algebraic relationship and advance to dynamic data validation. Moving forward, as new models are contributed to HYBRID, their V&V level will be included, updating this matrix.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Reconstruction of the 4D beam matrix

The widely used transverse parameters characterizing particle beams are the Twiss parameters. These parameters can be measured experimentally but they do not fully characterize the beam since they do not account for possible correlations in particle distribution between two transverse coordinates. These correlations may occur due to uncompensated magnetic field at the cathode or misalignment of focusing quadrupoles in the transport beamline. We test a novel diagnostic for diagnosing full 4D beam matrix which may be used to identify such imperfections. The diagnostic is based on transporting the beam through the beamline which includes a quadrupole and a skew quadrupole magnets and measuring the resulting 2D beam distribution at the screen downstream. Such a measurement can be viewed as measuring a 2D projection of the 4D distribution. Different settings of the quads provide measurements of different slices of the phase space. The reconstruction of the original beam matrix from a number of measurements is done using machine learning algorithm, which provides a fast and reliable way of reconstruction for an arbitrary configuration of the scanning beamline. In August 2024, we set up the diagnostic beamline to perform a quadrupole scan of the beam. The setup includes a skew quadrupole, a regular quadrupole, and a screen. The images on the screen were post-processed to remove experimental artifacts and enhance contrast by eliminating background noise outside the core of the distribution=. The rms parameters of the distribution were then calculated and used as inputs for the reconstruction algorithm. This algorithm attempts to determine the initial beam matrix that produces expected images on the screen closely matching the observed images across all quadrupole settings. The algorithm found a solution in which the expected rms parameters closely align with the observations. Validation of the results is planned for FY25.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Spin-Density Matrix Elements in K∗(892) Photoproduction with Recoiling Λ at GlueX

We report on the measurement of spin-density matrix elements for the K∗(892) → K+π0 photoproduction process with a recoiling Λ hyperon. The measurement used high-statistics GlueX data from photoproduction off a proton target at photon energies between 8.2 and 8.8 GeV, in a kinematic regime dominated by t-channel exchange processes. GlueX is a fixed-target experiment with a linearly polarized photon beam and a nearly 4π hermetic detector, allowing extraction of the full spin observ- ables from both the K+π0 and Λ → π−p systems. The GlueX data show clear evidence of the K∗(892) resonance in the K+π0 mass spectrum, along with additional structures at higher masses that may correspond to K∗0 or K∗2 resonances. The precise measurement of the spin-density matrix elements of the K∗(892) could serve as a standard candle for con- tinuing studies of higher-mass excited K∗ states. In addition, t-channel K∗ photoproduction differs from previous measurements of non-strange vector meson photoproduction, as it is free from Pomeron exchange and is virtually unexplored in this energy regime. Furthermore, the unprece- dented data collected by GlueX allow for investigations of correlations between the Λ polarization and the spin-density matrix elements of K∗ production, paving the way for future partial-wave analyses with full spin information involving the recoil hyperon.

Li, Hao [College of William and Mary, Williamsburg↗

DOE OSTI2020

Nanocellulosic metal matrix composite

A composite material comprising a metal matrix and nanocellulose supplement. The metal matrix is formed of a metal base material and may be monolithic throughout the composite material. The nanocellulose supplement improves a material property of the metal matrix and is formed of a nanocellulose supplement material dispersed in the metal base material. Importantly, the nanocellulose supplement material does not become damaged when the composite material is formed.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Compositions including nano-particles and a nano-structured support matrix and methods of preparation as reversible high capacity anodes in energy storage systems

The present invention relates to compositions including nano-particles and a nano-structured support matrix, methods of their preparation and applications thereof. The compositions of the present invention are particularly suitable for use as anode material for lithium-ion rechargeable batteries. The nano-structured support matrix can include nanotubes, nanowires, nanorods, and mixtures thereof. The composition can further include a substrate on which the nano-structured support matrix is formed. The substrate can include a current collector material.

Kumta, Prashant Nagesh↗

DOE OSTI2021

Nanocellulosic metal matrix composite

Everhart, Wesley↗

DOE OSTI2022

Binder for electrolyte matrix for molten carbonate fuel cells

A binder solution for an electrolyte matrix for use with molten carbonate fuel cells is provided. The binder solution includes a first polymer with a molecular weight of less than about 150,000 and a second binder with a molecular weight of greater than about 200,000. The binder solution produces an electrolyte matrix with improved flexibility, matrix particle packing density, strength, and pore structure.

Surendranath, Arun↗

DOE OSTI2022

Matrix Multiply Performance of GPUs on Exascale-class HPE/Cray Systems

The computation of dense matrix-matrix products (GEMMs) is central to many modeling and simulation workloads as well as AI/ML deep learning campaigns. In fact, millions of dollars are spent annually on computing GEMMs, and large model training demands are increasing exponentially. Specialized processors such as GPUs are designed to perform well for these operations. However, the performance of GEMMs on GPUs can exhibit complex behaviors depending on many factors, making it challenging to optimize the performance of GEMMs on these processors. In this study we undertake an examination of GEMM performance on several leading GPU models taken from product lines of GPUs to be deployed in forthcoming exascale computing systems. We show results to illustrate the many factors that can affect performance of GEMMs on GPUs. We then present data collected from a large number of test runs for an example GEMM operation to show the dependence behaviors of GEMM rate on matrix dimensions. Finally, we show results from machine learning-based performance models using novel feature engineering methods to fit the measured performance, providing a potential basis for GEMM performance tuning and autotuning methods for GPUs. Recommendations are also given for how to achieve high GEMM performance on modern GPUs.

Melesse Vergara, Veronica↗