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At least 1,297 records · Page 72

Spin-projected generalized Hartree-Fock method as a polynomial of particle-hole excitations

The past several years have seen renewed interest in the use of symmetry-projected Hartree-Fock for the description of strong correlations. Unfortunately, these symmetry-projected mean-field methods do not adequately account for dynamic correlation. Presumably, this shortcoming could be addressed if one could combine symmetry-projected Hartree-Fock with a many-body method such as coupled-cluster theory, but this is by no means straightforward because the two techniques are formulated in very different ways. However, we have recently shown that the singlet S 2 -projected unrestricted Hartree-Fock wave function can in fact be written in a coupled-cluster-like wave function. That is, the spin-projected unrestricted Hartree-Fock wave function can be written as a polynomial of a double-excitation operator acting on some closed-shell reference determinant. Here, we extend this result and show that the spin-projected generalized Hartree-Fock wave function (which has both S 2 and S z projection) is likewise a polynomial of low-order excitation operators acting on a closed-shell determinant and provide a closed-form expression for the resulting polynomial coefficients. The spin projection of the generalized Hartree-Fock wave function introduces connected triple and quadruple excitations which are absent when spin-projecting an unrestricted Hartree-Fock determinant. We include a few preliminary applications of the combination of this spin-projected Hartree-Fock and coupled-cluster theory to the Hubbard Hamiltonian and comment on generalizations of the methodology. Results here are not for production level, but a similarity-transformed theory that combines the two offers the promise of being accurate for both weak and strong correlation, and may offer significant improvements in the intermediate correlation regime where neither projected Hartree-Fock nor coupled cluster is particularly accurate.

74 ATOMIC AND MOLECULAR PHYSICS↗

Ab Initio Computation of the Longitudinal Response Function in 40 Ca

In this work, we present a consistent ab initio computation of the longitudinal response function RL in 40 Ca using the coupled-cluster and Lorentz integral transform methods starting from chiral nucleon-nucleon and three-nucleon interactions. We validate our approach by comparing our results for R L in 4 He and the Coulomb sum rule in 40 Ca against experimental data and other calculations. For R L in 40 Ca we obtain a very good agreement with experiment in the quasielastic peak up to intermediate momentum transfers, and we find that final state interactions are essential for an accurate description of the data. This work presents a milestone towards ab initio computations of neutrino-nucleus cross sections relevant for experimental long-baseline neutrino programs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Production of ${\pi ^0}$ and $\eta $ mesons up to high transverse momentum in pp collisions at 2.76 TeV

The invariant differential cross sections for inclusive π 0 and η mesons at midrapidity were measured in pp collisions at √s=2.76 TeV for transverse momenta 0.4 < p T < 40 GeV/c and 0.6 < p T < 20 GeV/c, respectively, using the ALICE detector. This large range in p T was achieved by combining various analysis techniques and different triggers involving the electromagnetic calorimeter (EMCal). Particularly, a new single-cluster, shower-shape based method was developed for the identification of high-p T neutral pions, which exploits that the showers originating from their decay photons overlap in the EMCal. Above 4 GeV/c, the measured cross sections are found to exhibit a similar power-law behavior with an exponent of about 6.3. Next-to-leading-order perturbative QCD calculations differ from the measured cross sections by about 30% for the π 0 , and between 30–50% for the η meson, while generator-level simulations with PYTHIA 8.2 describe the data to better than 10–30%, except at p T < 1 GeV/c. The new data can therefore be used to further improve the theoretical description of π 0 and η meson production.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

The Dark Energy Survey and Operations: Year 6 – The Finale

The Dark Energy Survey (DES) is an optical and near-infrared imaging survey aimed at understanding the accelerating expansion of the universe using four complementary methods: weak gravitational lensing, galaxy cluster counts, baryon acoustic oscillations, and Type Ia supernovae. To perform the 6-season 5000 sq-degree wide field and the 5-season 30 sq-degree supernova surveys, the DES Collaboration built the Dark Energy Camera (DECam), a 3 square-degree, 570-Megapixel CCD camera that was installed at the prime focus of the Blanco 4-meter telescope at the Cerro Tololo Inter-American Observatory (CTIO). DES started its observations in “Year 1” (Y1) on Aug. 31, 2013 and completed its sixth and final observing season on January 9, 2019. This paper describes DES Y6, the survey strategy, an outline of the survey operations procedures, the efficiency of operations and the causes of lost observing time. It provides details about the quality of the final season's data, and a summary of the Y1-Y6 observing. It contains a brief description of the last night of observations.

79 ASTRONOMY AND ASTROPHYSICS↗

Symposium: Quantum Theory, Quantum Materials, Quantum Computing (Final Report, DE-SC0022551)

Quantum mechanics is fundamental to understanding, predicting, and controlling many physical processes in a vast range of scientific and technological areas. In molecules and materials, the quantum mechanics of Coulombic many-body systems determines far more than the ground state and bulk properties. Quantum mechanics governs magnetism, electron and spin transport, chemical reactions and catalysis, superconductivity, photosynthesis, etc. Each plays a vital role in modern society. As digital technologies push both temporal and length-scale limits, fundamental quantum principles become the current focus for a new class of technologies: quantum computing, quantum sensing, quantum encryption, etc. This oncoming quantum information sciences wave is manifest recently in frontier foci of all six Department of Energy Office of Science programs. The Sanibel Symposium offers a proven, influential platform for facilitating and advancing cross-fertilization among theoretical and computational chemists and physicists, computer scientists, and materials engineers. The Symposia have a long, successful record of driving progress on theory and computation of materials, nanostructures, and their molecular constituents. The Symposia thus are well-suited for advancing the quantum information science thrust in the Office of Basic Energy Sciences (BES). With that QIS thrust as the focus, we propose five sessions of the 61st Sanibel Symposium on the theme Quantum Theory, Quantum Materials, Quantum Computing. From a longer perspective, this thematic program for 2022 is the second year of a five year plan of program segments addressed at challenges in quantum information sciences and quantum materials. The rest of the Symposium program will be of considerable relevance, with, for example, sessions on quantum molecular and material dynamics, and new directions in coupled cluster theory. New ideas for methods and applications can be expected to emerge from the stimulus of the formal presentations and informal discussions. The proposed award is targeted at students, post-docs, and junior scientists. Conscious effort, characteristic of the Sanibel Symposia, to stimulate the participation of women and under-represented minorities will continue.

36 MATERIALS SCIENCE↗

Development of Multimodal Few-Shot Analytics for Electron Micrographs

Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Attempts at creating a multimodal approach can provide further improvements to current models and help extract more salient features from data. In this vein, raw spectrum data was taken to provide an additional modality to our current pyCHIP classifier. Modifications in segmentation also show potential in improving the accuracy of the pyCHIP classifier. Classifier output was analyzed using network graphs and unsupervised clustering algorithms such as spectral clustering to detect better segmentation methods than the current “chipping” approach. We suggest that the chip selection process can be automated in the future using a combination of these techniques to enable high-throughput analyses.

36 MATERIALS SCIENCE↗

Signature extension: An approach to operational multispectral surveys

Two data processing techniques were suggested as applicable to the large area survey problem. One approach was to use unsupervised classification (clustering) techniques. Investigation of this method showed that since the method did nothing to reduce the signal variability, the use of this method would be very time consuming and possibly inaccurate as well. The conclusion is that unsupervised classification techniques of themselves are not a solution to the large area survey problem. The other method investigated was the use of signature extension techniques. Such techniques function by normalizing the data to some reference condition. Thus signatures from an isolated area could be used to process large quantities of data. In this manner, ground information requirements and computer training are minimized. Several signature extension techniques were tested. The best of these allowed signatures to be extended between data sets collected four days and 80 miles apart with an average accuracy of better than 90%.

Nalepka, R. F.↗

LANDSAT image interpretation aids

In the Large Area Crop Inventory Experiment, image interpretation aids were produced to assist in selecting and/or identifying representative samples of signatures in a given LANDSAT scene. The three methods employed are based on clustering techniques, information extraction, and aggregation of like spectral information on a two dimensional spectral plot.

Abotteen, R. A.↗

Landsat image interpretation aids

In the Large Area Crop Inventory Experiment, image interpretation aids were produced to assist in selecting and/or identifying representative samples of signatures in a given Landsat scene. The three methods employed are based on clustering techniques, information extraction, and aggregation of like spectral information on a two-dimensional spectral plot.

Abotteen, R. A.↗

Adaptive coding of MSS imagery

A number of adaptive data compression techniques are considered for reducing the bandwidth of multispectral data. They include adaptive transform coding, adaptive DPCM, adaptive cluster coding, and a hybrid method. The techniques are simulated and their performance in compressing the bandwidth of Landsat multispectral images is evaluated and compared using signal-to-noise ratio and classification consistency as fidelity criteria.

Habibi, A.↗

Cloud classification from satellite data using a fuzzy sets algorithm: A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗

Cloud classification from satellite data using a fuzzy sets algorithm - A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine like areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗

A comparison of the coupled cluster and internally contracted averaged coupled-pair functional levels of theory for the calculation of the MCH2(+) binding energies for M = Sc to Cu

The correlation contribution to the M-C binding energy for the MCH2(+) systems can exceed 100 kcal/mol. At the self-consistent field (SCF) level, these systems can be more than 50 kcal/mol above the fragment energies. In spite of the poor zeroth-order reference, the coupled cluster single and double excitation method with a perturbational estimate of triple excitations, CCSD(T), method is shown to provide an accurate description of these systems. The maximum difference between the CCSD(T) and internally contracted averaged coupled-pair functional binding energies is 1.5 kcal/mol for CrCH2(+), with the remaining systems agreeing to within 1.0 kcal/mol.

Bauschlicher, Charles W., Jr.↗

Application of CO2 Snow Jet Cleaning in Conjunction with Laboratory Based Total Reflection X-Ray Fluorescence

The Genesis mission was the first mission returning solar material to Earth since the Apollo program [1,2]. Unfortunately the return of the space craft on September 8, 2004 resulted in a crash landing, which shattered the samples into small fragments and exposed them to desert soil and other debris. Thus only small fragments of the original collectors are available, each having different degrees of surface contamination. Thorough surface cleaning is required to allow for subsequent analysis of solar wind material embedded within. An initial cleaning procedure was developed in coordination with Johnson Space Center which focused on removing larger sized particulates and a thin film organic contamination acquired during collection in space [3]. However, many of the samples have additional residues and more rigorous and/or innovative cleaning steps might be necessary. These cleaning steps must affect only the surface to avoid leaching and re-distribution of solar wind material from the bulk of the collectors. To aid in development and identification of the most appropriate cleaning procedures each sample has to be thoroughly inspected before and after each cleaning step. Laboratory based total reflection X-ray fluorescence (TXRF) spectrometry lends itself to this task as it is a non-destructive and surface sensitive analytical method permitting analysis of elements from aluminum onward present at and near the surface of a flat substrate [4]. The suitability of TXRF has been demonstrated for several Genesis solar wind samples before and after various cleaning methods including acid treatment, gas cluster ion beam, and CO2 snow jet [5 - 7]. The latter one is non-invasive and did show some promise on one sample [5]. To investigate the feasibility of CO2 snow jet cleaning further, several flown Genesis samples were selected to be characterized before and after CO2 snow application with sample 61052 being discussed below.

Schmeling, M.↗

The Dark Energy Survey and Operations: Years 1 to 3

The Dark Energy Survey (DES) is an operating optical survey aimed at understanding the accelerating expansion of the universe using four complementary methods: weak gravitational lensing, galaxy cluster counts, baryon acoustic oscillations, and Type Ia supernovae. To perform the 5000 sq-degree wide field and 30 sq-degree supernova surveys, the DES Collaboration built the Dark Energy Camera (DECam), a 3 square-degree, 570-Megapixel CCD camera that was installed at the prime focus of the Blanco 4-meter telescope at the Cerro Tololo Inter-American Observatory (CTIO). DES has completed its third observing season out of a nominal five. This paper describes DES “Year 1” (Y1) to “Year 3” (Y3), the strategy, an outline of the survey operations procedures, the efficiency of operations and the causes of lost observing time. It provides details about the quality of the first three season's data, and describes how we are adjusting the survey strategy in the face of the El Niño Southern Oscillation

Diehl, H. T.↗

The Dark Energy Survey and Operations: Years 4 and 5

The Dark Energy Survey (DES) is an operating optical survey aimed at understanding the accelerating expansion of the universe using four complementary methods: weak gravitational lensing, galaxy cluster counts, baryon acoustic oscillations, and Type Ia supernovae. To perform the 5000 sq-degree wide field and 30 sq-degree supernova surveys, the DES Collaboration built the Dark Energy Camera (DECam), a 3 square-degree, 570-Megapixel CCD camera that was installed at the prime focus of the Blanco 4-meter telescope at the Cerro Tololo Inter-American Observatory (CTIO). DES has completed its third observing season out of a nominal five. This paper describes DES “Year 4” (Y4) and “Year 5” (Y5), the survey strategy, an outline of the survey operations procedures, the efficiency of operations and the causes of lost observing time. It provides details about the quality of these two-season's data, a summary of the overall status, and plans for the final survey season.

Diehl, H. T.↗

Evaluation of Drilling Performance at The Geysers with Machine Learning Methods Using Geologic Data

A recent well, GDC-36, was drilled in The Geysers Geothermal Field served in a Department of Energy-industry to demonstrate improved drilling performance with polycrystalline diamond compact (PDC) bits. Both PDC and roller cone drill bits were used to drill this well. Key challenges encountered during drilling included lost circulation in the mud-drilled section, and bit damage interfacial severity in the deeper, air-drilled section. The objective of this study is to evaluate the drilling performance in relation to the local geological characteristics using machine learning methods. By applying K-clustering to the sonic log data, we were able to identify areas correlated with measured lost circulation. Also, the boundaries defined by clustering of the mineralogical and lithological data from the mud logs correlate well with interfacial severity during drilling. A random forest model was employed to build correlation between drilling data and rock strength. The confined compressive strength (CCS) of the rock in the training of the machine learning model was inferred from the dipole sonic log. The R-squared of the testing data is 0.78, and the RMSE (Root Mean Squared Error) is 0.06. The trained model was used to forecast rock strength for the section where sonic log data are not available. CCS could also be inferred from mud logs provided the relationship between mineralogy and rock strength is established through core testing data.

15 GEOTHERMAL ENERGY↗

Lattice Heat Capacity of Nanostructured Materials Based on Titanium/Zirconium and Aluminum

The dynamic and thermal properties of nanostructured materials based on aluminum with the periodic inclusions of Ti or Zr clusters have been investigated by the method of molecular dynamics. The elastic moduli, spectral vibrational densities of states, and temperature dependences of heat capacity for different Ti–Al and Zr–Al systems have been obtained. The effect of specific features of the phonon spectrum on the heat capacity of the nanocomposite lattice has been examined. It is shown that the ordering type and size of Ti/Zr clusters in the aluminum matrix significantly affect the elastic properties and heat capacity. The results obtained can be used for fabricating new aluminum-, titanium-, and zirconium-based composites with the desired properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗