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At least 361 records · Page 20

On the determination of the coefficients of the Gaussian general theory of the Earth's magnetism for the year 1885 and about the relationship of the three Earth-magnetic elements

An attempt is made to judge the value of the Gaussian series for the Earth's magnetism. The computation employed to do this uses the method of the least and greatest coefficients. The number of unknown which had to be calculated from the individual groups was at most only four. All symbols of Gauss were retained.

Fritsche, H.↗

Theoretical study of the X 2Pi and A 2Sigma(+) states of CuO and CuS

Theoretical spectroscopic constants and dipole moments are determined for the X 2Pi and A 2Sigma(+) states of CuO and CuS, using extended Gaussian basis sets and incorporating correlation using both configuration interaction (CI) and coupled pair (CPF) methods. Relativistic corrections (Darwin plus mass velocity), included using first-order perturbation theory, are found to be relatively small. At the CPF level, significant configuration mixing with the 3d(9)4s(1) occupation occurs for both states, resulting in some bond shortening and a significant increase in the dissociation energy. The best spectroscopic parameters are in excellent agreement with experiment for both CuO and CuS. In analogy with CuO, CuS is predicted to have a yet unobserved 2Sigma(+) state near 10,000 per cm.

Langhoff, S. R.↗

Theoretical study of the low-lying electronic states of ZnO and ZnS

Theoretical spectroscopic constants and dipole moments are determined for the 1 Sigma(+), 1,3 Pi, and 3 Sigma(+) states of ZnO and ZnS, using extended Gaussian basis sets and incorporating correlation using both configuration-interaction and coupled pair (CPF) methods. Relativistic corrections (Darwin plus mass velocity), included using first-order perturbation theory, are relatively small. At the CPF level, both ZnO and ZnS have 1 Sigma(+) ground states, with the 3 Pi state lying 209 and 2075/cm higher, respectively. The 3 Sigma(+) state lies about 1.5 eV higher in ZnO and 2.1 eV higher in ZnS. The 1,3 Pi states are relatively close together since the exchange splitting is small with the sigma electron localized on Zn and the pi electron on oxygen (or sulfur).

Bauschlicher, C. W., Jr.↗

Computational Study of Electron-Molecule Collisions Related to Low-Temperature Plasmas

Computational study of electron-molecule collisions not only complements experimental measurements, but can also be used to investigate processes not readily accessible experimentally. A number of ab initio computational methods are available for these types of calculations. Here we describe a recently developed technique, the finite element Z-matrix method, Analogous to the R-matrix, method, it partitions the space into regions and employs real matrix elements. However, unlike the implementation of the R-matrix method commonly used in atomic and molecular physics, the Z-matrix method is fully variational. In the present implementation, a mixed basis of finite elements and Gaussians is used to represent the continuum electron, thus offering full flexibility without imposing fixed boundary conditions. Numerical examples include the electron-impact dissociation of N2 via the metastable A3Su+ state, a process which may be important in the lower thermosphere, and the dissociation of the CF radical, a process of interest to plasma etching. To understand the dissociation pathways, large scale quantum chemical calculations have been carried out for all target states which dissociate to the lowest five limits in the case of N2, and to the lowest two limits in the case of CF. For N2, the structural calculations clearly show the preference for predissociation if the initial state is the ground X1 Sg+ state, but direct dissociation appears to be preferable if the initial state is the A3Su+ state. Multi-configuration SCF target functions are used in the collisional calculation.

Huo, Winifred M.↗

Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments

We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.

Non-NASA Center↗

Analytical Algorithms to Quantify the Uncertainty in Remaining Useful Life Prediction

This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decisionmaking. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approximations can be made using the the first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.

Sankararaman, Shankar↗

Analyses of Cometary Silicate Crystals: DDA Spectral Modeling of Forsterite

Comets are the Solar System's deep freezers of gases, ices, and particulates that were present in the outer protoplanetary disk. Where comet nuclei accreted was so cold that CO ice (approximately 50K) and other supervolatile ices like ethane (C2H2) were preserved. However, comets also accreted high temperature minerals: silicate crystals that either condensed (greater than or equal to 1400 K) or that were annealed from amorphous (glassy) silicates (greater than 850-1000 K). By their rarity in the interstellar medium, cometary crystalline silicates are thought to be grains that formed in the inner disk and were then radially transported out to the cold and ice-rich regimes near Neptune. The questions that comets can potentially address are: How fast, how far, and over what duration were crystals that formed in the inner disk transported out to the comet-forming region(s)? In comets, the mass fractions of silicates that are crystalline, f_cryst, translate to benchmarks for protoplanetary disk radial transport models. The infamous comet Hale-Bopp has crystalline fractions of over 55%. The values for cometary crystalline mass fractions, however, are derived assuming that the mineralogy assessed for the submicron to micron-sized portion of the size distribution represents the compositional makeup of all larger grains in the coma. Models for fitting cometary SEDs make this assumption because models can only fit the observed features with submicron to micron-sized discrete crystals. On the other hand, larger (0.1-100 micrometer radii) porous grains composed of amorphous silicates and amorphous carbon can be easily computed with mixed medium theory wherein vacuum mixed into a spherical particle mimics a porous aggregate. If crystalline silicates are mixed in, the models completely fail to match the observations. Moreover, models for a size distribution of discrete crystalline forsterite grains commonly employs the CDE computational method for ellipsoidal platelets (c:a:b=8.14x8.14xl in shape with geometrical factors of x:y:z=1:1:10, Fabian et al. 2001; Harker et al. 2007). Alternatively, models for forsterite employ statistical methods like the Distribution of Hollow Spheres (Min et al. 2008; Oliveira et al. 2011) or Gaussian Random Spheres (GRS) or RGF (Gielen et al. 200S). Pancakes, hollow spheres, or GRS shapes similar to wheat sheaf crystal habit (e.g., Volten et al. 2001; Veihelmann et al. 2006), however, do not have the sharp edges, flat faces, and vertices seen in images of cometary crystals in interplanetary dust particles (IDPs) or in Stardust samples. Cometary forsterite crystals often have equant or tabular crystal habit (J. Bradley). To simulate cometary crystals, we have computed absorption efficiencies of forsterite using the Discrete Dipole Approximation (DDA) DDSCAT code on NAS supercomputers. We compute thermal models that employ a size distribution of discrete irregularly shaped forsterite crystals (nonspherical shapes with faces and vertices) to explore how crystal shape affects the shape and wavelength positions of the forsterite spectral features and to explore whether cometary crystal shapes support either condensation or annealing scenarios (Lindsay et al. 2012a, b). We find forsterite crystal shapes that best-fit comet Hale-Bopp are tetrahedron, bricks or brick platelets, essentially equant or tabular (Lindsay et al. 2012a,b), commensurate with high temperature condensation experiments (Kobatake et al. 2008). We also have computed porous aggregates with crystal monomers and find that the crystal resonances are amplified. i.e., the crystalline fraction is lower in the aggregate than is derived by fitting a linear mix of spectral features from discrete subcomponents, and the crystal resonances 'appear' to be from larger crystals (Wooden et al. 2012). These results may indicate that the crystalline mass fraction in comets with comae dominated by aggregates may be lower than deduced by popular methods that only emoy ensembles of discrete crystals.

Wooden, Diane↗

A General-applications Direct Global Matrix Algorithm for Rapid Seismo-acoustic Wavefield Computations

A new matrix method for rapid wave propagation modeling in generalized stratified media, which has recently been applied to numerical simulations in diverse areas of underwater acoustics, solid earth seismology, and nondestructive ultrasonic scattering is explained and illustrated. A portion of recent efforts jointly undertaken at NATOSACLANT and NORDA Numerical Modeling groups in developing, implementing, and testing a new fast general-applications wave propagation algorithm, SAFARI, formulated at SACLANT is summarized. The present general-applications SAFARI program uses a Direct Global Matrix Approach to multilayer Green's function calculation. A rapid and unconditionally stable solution is readily obtained via simple Gaussian ellimination on the resulting sparsely banded block system, precisely analogous to that arising in the Finite Element Method. The resulting gains in accuracy and computational speed allow consideration of much larger multilayered air/ocean/Earth/engineering material media models, for many more source-receiver configurations than previously possible. The validity and versatility of the SAFARI-DGM method is demonstrated by reviewing three practical examples of engineering interest, drawn from ocean acoustics, engineering seismology and ultrasonic scattering.

Schmidt, H.↗

A Bayesian Learning Approach to Wireless Outdoor Heatmap Construction using Deep Gaussian Process

We present a novel Bayesian learning approach to outdoor radio heatmap construction utilizing deep Gaussian process (GP). The proposed approach employs a two-layer hierarchy which consists of two cascaded Gaussian processes that are capable of modeling more complex input-output relations than standard single-layer Gaussian processes. Since deriving the exact model likelihood is challenging, a lower bound is optimized instead so that gradient descent-based methods can be performed to find out the optimal model parameters. Typically, inducing points are used in GPs to facilitate low-rank approximation of covariance (kernel) matrices for computation speedup. However, the inaccuracy induced by inducing points can accumulate when stacking multiple layers of GP which may hinder the performance of deep GP. Moreover, since inducing points need to be learned, having them at all layers of deep GP also incurs computational burden. To overcome the above challenges, in contrast to the canonical deep GP model, we use a modified architecture where a full standard GP resides in the first layer and inducing points are only introduced for the second layer. This modified architecture strikes a balance between model accuracy and training complexity. In the proposed model, the noise parameter of the first GP layer is also eliminated to improve the training efficiency as the noise parameter at the output of the second layer suffices to model the uncertainty in the output. The proposed approach is evaluated on real-world datasets, in the form of location-Received Signal Strength (RSS) pairs, collected from the Platform for Open Wireless Data-driven Experimental Research (POWDER) located at the campus of the University of Utah. Experiment results show that the proposed approach can achieve smaller prediction errors on various training and testing data configurations than DNN-based and GP-based methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Signal-preserving CMB component separation with machine learning

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation using multifrequency methods, whereby different signals are isolated according to their different frequency behaviors. Many so-called blind methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC produces a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for the case of Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that , and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds and show that our ML model can exploit non-Gaussian features, such as point sources and spatially varying spectral indices, to produce lower-variance maps than ILC—e.g., reducing the variance of the B-mode residual by factors of up to 5—while preserving the signal of interest in an unbiased manner. Moreover, we often find improved performance even when applying our ML technique to foreground models on which it was not trained. Published by the American Physical Society 2025

McCarthy, Fiona (ORCID:0000000253893565)↗

Searching for Quasi-periodic Oscillations in Astrophysical Transients Using Gaussian Processes

Analyses of quasi-periodic oscillations(QPOs)are important to understanding the dynamic behavior in manyastrophysical objects during transient events like gamma-ray bursts, solarflares, magnetarflares, and fast radiobursts. Astrophysicists often search for QPOs with frequency-domain methods such as(Lomb–Scargle)periodograms, which generally assume power-law models plus some excess around the QPO frequency. Time-series data can alternatively be investigated directly in the time domain using Gaussian process(GP)regression.While GP regression is computationally expensive in the general case, the properties of astrophysical data andmodels allow fast likelihood strategies. Heteroscedasticity and nonstationarity in data have been shown to causebias in periodogram-based analyses. GPs can take account of these properties. Using GPs, we model QPOs as astochastic process on top of a deterministicflare shape. Using Bayesian inference, we demonstrate how to infer GPhyperparameters and assign them physical meaning, such as the QPO frequency. We also perform model selectionbetween QPOs and alternative models such as red noise and show that this can be used to reliablyfind QPOs. Thismethod is easily applicable to a variety of different astrophysical data sets. We demonstrate the use of this methodon a range of short transients: a gamma-ray burst, a magnetarflare, a magnetar giantflare, and simulated solarflare data.

Moritz Hubner↗

Attitude determination and parameter estimation using vector observations - Application

This paper presents tests of a new method for the simultaneous estimation of spacecraft attitude and sensor biases, based on a quaternion estimation algorithm minimizing Wahba's loss function. The new method is compared with a conventional batch least-squares differential correction algorithm. The estimates are based on data from strapdown gyros and star trackers, simulated with varying levels of Gaussian noise for both inertially-fixed and earth-pointing attitudes. Both algorithms solve for the spacecraft attitude and the gyro drift rate biases. In the majority of tests performed, the two methods converge to the same estimates in the same number of iterations, but the new algorithm requires about 60 percent more computational effort. Some cases were found in which the new method converges in fewer iterations than the differential correction, and some for which the differential correction requires fewer iterations.

Markley, F. Landis↗

Determination of Cross-Sectional Area of Focused Picosecond Gaussian Laser Beam

Measurement of the waist diameter of a focused Gaussian-beam at the 1/e(sup 2) intensity, also referred to as spot size, is key to determining the fluence in laser processing experiments. Spot size measurements are also helpful to calculate the threshold energy and threshold fluence of a given material. This work reports an application of a conventional method, by analyzing single laser ablated spots for different laser pulse energies, to determine the cross-sectional area of a focused Gaussian-beam, which has a nominal pulse width of approx. 10 ps. Polished tungsten was used as the target material, due to its low surface roughness and low ablation threshold, to measure the beam waist diameter. From the ablative spot measurements, the ablation threshold fluence of the tungsten substrate was also calculated.

Ledesma, Rodolfo↗

Robust Control of Uncertain Systems via Dissipative LQG-Type Controllers

Optimal controller design is addressed for a class of linear, time-invariant systems which are dissipative with respect to a quadratic power function. The system matrices are assumed to be affine functions of uncertain parameters confined to a convex polytopic region in the parameter space. For such systems, a method is developed for designing a controller which is dissipative with respect to a given power function, and is simultaneously optimal in the linear-quadratic-Gaussian (LQG) sense. The resulting controller provides robust stability as well as optimal performance. Three important special cases, namely, passive, norm-bounded, and sector-bounded controllers, which are also LQG-optimal, are presented. The results give new methods for robust controller design in the presence of parametric uncertainties.

Joshi, Suresh M.↗

On the performance of large Gaussian basis sets for the computation of total atomization energies

The total atomization energies of a number of molecules have been computed using an augmented coupled-cluster method and (5s4p3d2f1g) and 4s3p2d1f) atomic natural orbital (ANO) basis sets, as well as the correlation consistent valence triple zeta plus polarization (cc-pVTZ) correlation consistent valence quadrupole zeta plus polarization (cc-pVQZ) basis sets. The performance of ANO and correlation consistent basis sets is comparable throughout, although the latter can result in significant CPU time savings. Whereas the inclusion of g functions has significant effects on the computed Sigma D(e) values, chemical accuracy is still not reached for molecules involving multiple bonds. A Gaussian-1 (G) type correction lowers the error, but not much beyond the accuracy of the G1 model itself. Using separate corrections for sigma bonds, pi bonds, and valence pairs brings down the mean absolute error to less than 1 kcal/mol for the spdf basis sets, and about 0.5 kcal/mol for the spdfg basis sets. Some conclusions on the success of the Gaussian-1 and Gaussian-2 models are drawn.

Martin, J. M. L.↗

Robust Dark Energy Constraints with the Dark Energy Spectroscopic Survey (Final Technical Report)

This project developed and applied advanced theoretical, computational, and data-analysis methodologies to extract robust and precise cosmological constraints from the Dark Energy Spectroscopic Instrument (DESI). The work focused on maximizing the scientific return of DESI through optimized survey strategy, novel higher-order clustering statistics, improved modeling of small-scale structure, and rigorous mitigation of observational systematics. Over the award period, the project made substantial contributions to DESI science planning, produced new methods for bispectrum and three-point correlation function analyses, advanced constraints on primordial non-Gaussianity, and delivered widely used software tools. The project also played a major role in training graduate students and a postdoctoral researcher who contributed directly to DESI key projects. The results have significantly enhanced the cosmological reach of DESI and provide a strong foundation for future surveys such as DESI-II and Stage-V experiments.

79 ASTRONOMY AND ASTROPHYSICS↗

Simulation of time series by distorted Gaussian processes

Distorted stationary Gaussian process can be used to provide computer-generated imitations of experimental time series. A method of analyzing a source time series and synthesizing an imitation is shown, and an example using X-band radiometer data is given.

Greenhall, C. A.↗

Bit-wise arithmetic coding for data compression

This article examines the problem of compressing a uniformly quantized independent and identically distributed (IID) source. We present a new compression technique, bit-wise arithmetic coding, that assigns fixed-length codewords to the quantizer output and uses arithmetic coding to compress the codewords, treating the codeword bits as independent. We examine the performance of this method and evaluate the overhead required when used block-adaptively. Simulation results are presented for Gaussian and Laplacian sources. This new technique could be used as the entropy coder in a transform or subband coding system.

Kiely, A. B.↗