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At least 253 records · Page 14

Parton distribution functions from scalar light-front parton gas model

Here, we propose an application of a microcanonical ensemble with light-front kinematics to model the phase-space distribution of relativistic constituents of a bound state. These constituents denoted by partons are treated as classical spin-zero particles confined inside the bound state with inter-parton collisions as their only interaction. The microcanonical molecular dynamics ensemble is applied to obtain the phase-space distribution of such a thermodynamic system. We sample this phase-space distribution using Monte Carlo algorithms to obtain the parton distribution functions (PDFs) in scenarios with 3, 4, and 5 identical partons. In addition PDFs when a selected number of massless partons are mixed with 3 massive partons are also presented.

Microcanonical ensemble↗

Liquid Phase Modeling in Porous Media: Adsorption of Methanol and Ethanol in H-MFI in Condensed Water

Zeolites are used in the chemical and separation industries for their exceptional selectivity, adsorption capacity, regenerability, and stability in gas and liquid phase processing. Here, we developed an explicit solvation method for predicting solvent/condensed phase effects on adsorption free energies in microporous media such as zeolites based on the hybrid quantum mechanical/molecular mechanical free energy perturbation (QM/MM-FEP) technique. Our explicit solvation method for zeolite systems, called eSZS, aims to capture site-specific interactions during the adsorption process at the Brønsted acid sites of H-MFI zeolite while still considering the diverse configuration space of the solvent molecules. This strategy is ideal for chemical reactions or adsorbates that interact with the microporous medium in few distinct adsorbate/transition state configurations, i.e., the harmonic or similar approximations are acceptable for the adsorbate/transition state while such approximations break down for the solvent molecules that require extensive configuration space sampling. In this way, our approach effectively overcomes the limitations of implicit solvation models and classical force field methods for describing solvation effects on chemical reactions within porous materials such as zeolites. Specifically, in this study, we investigated various aspects of our hybrid QM/MM approach, including QM cluster size dependencies in a periodic electrostatically embedded cluster model (PEECM), rules for link atoms at the QM/MM boundary, and functional and basis set considerations for converged and reasonably accurate gas and aqueous phase methanol and ethanol adsorption free energy predictions in H-MFI. For gas phase adsorption of methanol and ethanol in H-MFI at a Brønsted acid site in T12 position, we compute adsorption free energies at 298 K of −0.61 and −0.75 eV, respectively, using a PEECM containing 50 Si and 1 Al atom with ωB97x-D/def2-TZVP level of theory. For solvent effect calculations, we sample the aqueous phase using grand canonical Monte Carlo (GCMC) simulations to (1) obtain a mean field of electrostatic interactions in the reaction system and (2) perform a rigorous free energy perturbation calculation. Similar to the experimentally and computationally observed endergonic solvation effects observed for hydrocarbon adsorption on metal surfaces, we also observe that a condensed aqueous environment destabilizes methanol and ethanol at these acid sites in H-MFI at 298 K. Specifically, the computed solvation free energies of adsorption (ΔΔG solv ) for methanol and ethanol are +0.44 and +0.54 eV, respectively. From this study, it is evident that adsorbates (methanol and ethanol) are competing with water for adsorption space inside the H-MFI zeolite, leading to an endergonic solvation effect. Here, we expect that the endergonic, aqueous solvent effect during adsorption in microporous zeolites is highly tunable by changing the pore size and hydrophobicity of the microporous material as this will affect the water density inside the pore structure.

Adsorption↗

Deep gamma ray penetration in thick shields

Appropriate importance function and sampling scheme facilitates the application of the Monte Carlo method to problems involving the deep penetration of radiation.

Armstrong, T. W.↗

LACIE performance predictor final operational capability program description, volume 2

Given the swath table files, the segment set for one country and cloud cover data, the SAGE program determines how many times and under what conditions each segment is accessed by satellites. The program writes a record for each segment on a data file which contains the pertinent acquisition data. The weather data file can also be generated from a NASA supplied tape. The Segment Acquisition Selector Program (SACS) selects data from the segment reference file based upon data input manually and from a crop window file. It writes the extracted data to a data acquisition file and prints two summary reports. The POUT program reads from associated LACIE files and produces printed reports. The major types of reports that can be produced are: (1) Substrate Reference Data Reports, (2) Population Mean, Standard Deviation and Histogram Reports, (3) Histograms of Monte Carlo Statistics Reports, and (4) Frequency of Sample Segment Acquisitions Reports.

Source record↗

Experimental analysis of computer system dependability

This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance.

Iyer, Ravishankar, K.↗

Realistic Covariance Generation for the GPM Spacecraft

We present several different methods for generating realistic predictive covariance, including Monte-Carlo simulations and more direct linear methods which require the addition of process noise. The Monte-Carlo simulation starts with an epoch uncertainty sample basis and propagates each trial to a time of interest in the future. The variance-covariance of the state elements as well as other higher order sample statistics can be readily computed from the propagated sample. While this method preserves the nonlinear effects on the propagated uncertainty, it is computationally intensive as a statistically significant sample size must be considered in the propagation process. Moreover, if the epoch covariance is optimistic, this can result in an underestimation of the prediction error. Another method is to propagate a state sensitivity matrix simultaneously with the satellite state, which allows the epoch covariances to be propagated forward in a linear fashion. This method does not preserve the non-linearity of the satellite state uncertainty but is much less computationally intensive. The propagated state covariance is the scaled to represent the realistic level of GPM state uncertainty via a "e-tuning process." The tuning process generates an inflation factor based on the observed error statistics of the predictive satellite trajectories when compared to the definitive ones. Difference tuning strategies are considered and compared via Goodness-of-Fit method testing for the Gaussian properties of the scaled covariance.

prediction error↗

Active Neutron and Gamma Ray Instrumentation for In Situ Planetary Science Applications

The Pulsed Neutron Generator-Gamma Ray And Neutron Detectors (PNG-GRAND) experiment is an innovative application of the active neutron-gamma ray technology so successfully used in oil field well logging and mineral exploration on Earth. The objective of our active neutron-gamma ray technology program at NASA Goddard Space Flight Center (NASA-GSFC) is to bring the PNG-GRAND instrument to the point where it can be flown on a variety of surface lander or rover missions to the Moon, Mars, Menus, asteroids, comets and the satellites of the outer planets. Gamma-Ray Spectrometers (GRS) have been incorporated into numerous orbital planetary science missions and, especially its the case of the Mars Odyssey GRS, have contributed detailed maps of the elemental composition over the entire surface of Mars. However, orbital gamma ray measurements have low spatial sensitivity (100's of km) due to their low surface emission rates from cosmic rays and subsequent need to be averaged over large surface areas. PNG-GRAND overcomes this impediment by incorporating a powerful neutron excitation source that permits high sensitivity surface and subsurface measurements of bulk elemental compositions. PNG-GRAND combines a pulsed neutron generator (PNG) with gamma ray and neutron detectors to produce a landed instrument to determine subsurface elemental composition without needing to drill into a planet's surface a great advantage in mission design. We are currently testing PNG-GRAND prototypes at a unique outdoor neutron instrumentation test facility recently constructed at NASA/GSFC that consists of a 2 m x 2 in x 1 m granite structure placed outdoors in an empty field. Because an independent trace elemental analysis has been performed on the material, this granite sample is a known standard with which to compare both Monte Carlo simulations and our experimentally measured elemental composition data. We will present data from operating PNG-GRAND in various experimental configurations on a known sample in a geometry that is identical to that on a planetary surface. We will also illustrate the use of gamma ray timing techniques to improve sensitivity and will compare the material composition results from our experiments to both an independent laboratory elemental composition analysis and MCNPX computer modeling results.

Parsons, A.↗

The VATMOS-SR Mission Concept: DSMC Studies of the Gas Sampling

VATMOS-SR (Venus ATMOSpheric - Sample Return) is a small spacecraft mission concept that would return a gas sample from the upper atmosphere of Venus to Earth for scientific analysis. This could be the first sample return mission for an extra-terrestrial atmosphere and potentially the first sample return from an Earth-sized planet. The VATMOS-SR mission concept consists of a SmallSat atmospheric sampling probe (45 deg. sphere/cone geometry, <1 m diameter) that is designed to skim through the Venus upper atmosphere and acquire gas samples below the homopause altitude (around ~110 km altitude), where the different atmospheric gases are mixed. The velocity of the spacecraft where sampling would occur is expected to be between ~10.5 km/s and ~13.1 km/s, depending on the trajectory chosen. This presentation will discuss hypervelocity sampling in the upper atmosphere of Venus with respect to the VATMOS-SR mission concept. VATMOS-SR would enable critical atmospheric measurements to form a complete picture of how, why, and when Venus evolved to be so different from Earth and Mars. The abundances and isotopic compositions of volatile elements (such as N, C, S, O, and the noble gases) in planetary atmospheres record volatile delivery during accretion, outgassing from planetary interiors, and atmospheric loss to space. Precise and accurate determinations of volatile atmospheric signatures are the key to understanding the origins and geodynamical evolution of Venus compared to the other terrestrial planets. Hypersonic sampling poses unique technical and scientific challenges. To ensure it is possible to relate the composition of the sampled gases to the free stream atmospheric composition, large-scale numerical simulations are employed to model the flow through the VATMOS-SR sampling system. In particular, an emphasis is placed on quantifying noble gas isotopic fractionation that occurs during the sample acquisition and transfer process to determine how measured isotopic ratios of noble gases in the sample compared to actual isotopic ratios in the Venusian atmosphere. The Direct Simulation Monte Carlo (DSMC) code SPARTA, an open-source software package developed by Sandia National Laboratories, is used in this work. SPARTA, based on Bird’s DSMC method, is a molecular-level gas-kinetic technique. As SPARTA is able to model hypervelocity reacting flows in strong chemical and thermal non-equilibrium, this software package is well suited to determine relevant flow properties for the VATMOS-SR mission concept and to numerically quantify the expected level of elemental and/or isotopic fractionation in the sample acquired by VATMOS-SR. This presentation will show results from 3D simulations correlating the noble gas isotopic fractionation in the gas acquired at hypervelocity speeds to its ambient atmosphere value. In particular, emphasis will be placed on Xenon isotopes of masses 128 and 130, as precise measurements of that ratio would yield comparison to Earth’s atmosphere. Additionally, sensitivity studies that quantify the uncertainties due to the freestream parameters, as well as the modeling parameters, will be performed.

direct simulation Monte Carlo↗

Kullback-Leibler information function and the sequential selection of experiments to discriminate among several linear models

The error variance of the process prior multivariate normal distributions of the parameters of the models are assumed to be specified, prior probabilities of the models being correct. A rule for termination of sampling is proposed. Upon termination, the model with the largest posterior probability is chosen as correct. If sampling is not terminated, posterior probabilities of the models and posterior distributions of the parameters are computed. An experiment was chosen to maximize the expected Kullback-Leibler information function. Monte Carlo simulation experiments were performed to investigate large and small sample behavior of the sequential adaptive procedure.

Sidik, S. M.↗

The Suzaku View of Highly Ionized Outflows in AGN: Statistical Detection and Global Absorber Properties - 1

We present the results of a new spectroscopic study of Fe K-band absorption in active galactic nuclei (AGN). Using data obtained from the Suzaku public archive we have performed a statistically driven blind search for Fe XXV Healpha and/or Fe XXVI Lyalpha absorption lines in a large sample of 51 Type 1.0−1.9 AGN. Through extensive Monte Carlo simulations we find that statistically significant absorption is detected at E greater than or approximately equal to 6.7 keV in 20/51 sources at the P(sub MC) greater than or equal tov 95 per cent level, which corresponds to approximately 40 per cent of the total sample. In all cases, individual absorption lines are detected independently and simultaneously amongst the two (or three) available X-ray imaging spectrometer detectors, which confirms the robustness of the line detections. The most frequently observed outflow phenomenology consists of two discrete absorption troughs corresponding to Fe XXV Healpha and Fe XXVI Lyalpha at a common velocity shift. From xstar fitting the mean column density and ionization parameter for the Fe K absorption components are log (N(sub H) per square centimeter)) is approximately equal to 23 and log (Xi/erg centimeter per second) is approximately equal to 4.5, respectively. Measured outflow velocities span a continuous range from less than1500 kilometers per second up to approximately100 000 kilometers per second, with mean and median values of approximately 0.1 c and approximately 0.056 c, respectively. The results of this work are consistent with those recently obtained using XMM-Newton and independently provides strong evidence for the existence of very highly ionized circumnuclear material in a significant fraction of both radio-quiet and radio-loud AGN in the local universe.

Gofford, Jason↗

Using Convex Optimization to Efficiently Apportion Tracer and Pollutant Sources From Point Concentration Observations

Abstract Rivers transport elements, minerals, chemicals, and pollutants produced in their upstream basins. A sample from a river is a mixture of all of its upstream sources, making it challenging to pinpoint the contribution from each individual source. Here, we show how a nested sample design and convex optimization can be used to efficiently unmix downstream samples of a well‐mixed, conservative tracer in a steady state system into the contributions of their upstream sources. Our approach is significantly faster than previous methods. We represent the river's sub‐catchments, defined by sampling sites, using a directed acyclic graph. This graph is used to build a convex optimization problem which, thanks to its convexity, can be quickly solved to global optimality—in under a second on desktop hardware for data sets of ∼100 samples or fewer. Uncertainties in the upstream predictions can be generated using Monte Carlo resampling. We provide an open‐source implementation of this approach in Python. The inputs required are straightforward: a table containing sample locations and observed tracer concentrations, along with a D8 flow‐direction raster map. As a case study, we use this method to map the elemental geochemistry of sediment sources for rivers draining the Cairngorms mountains, UK. This method could be extended to non‐conservative and non‐steady state tracers. We also show, theoretically, how multiple tracers could be simultaneously inverted to recover upstream run‐off or erosion rates as well as source concentrations. Overall, this approach can provide valuable insights to researchers in various fields, including water quality, geochemical exploration, geochemistry, hydrology, and wastewater epidemiology.

Barnes, Richard↗

Constraining Cross Section and Beam Systematics for Future NOvA Sterile Neutrino Search

he NOvA (NuMI Off-axis $\nu_e$ Appearance) experiment measures neutrino oscillations in a nearly pure muon (anti)neutrino beam over a 810 km baseline. We search for sterile neutrino driven oscillations in both neutral current (NC) and charged current (CC) muon neutrino samples. A deficit of NC interactions at the Far Detector could occur since sterile neutrinos do not couple to the Z boson. A modulation of the muon neutrino disappearance probability is possible due to a new mass splitting. The NuMI beam line is capable of running in forward or reverse horn current modes, which creates a neutrino or antineutrino beam, respectively. Samples collected in forward horn current (FHC) or reverse horn current (RHC) modes are subject to beam optics uncertainties, in addition to cross section and hadron production uncertainties. We explore the ategories. To tackle reduction of cross section uncertainties, we study how splitting the neutral current (NC) samples can impact the systematic reduction and its implications on oscillation parameters, leveraging results from Monte Carlo simulations. Additionally, we look at the possibility of using a $\nu$-on-e scattering sample. The $\nu$-on-e scattering process has minimal cross section uncertainties compared to that of neutrino-nucleus interactions, which can help constrain the overall flux normalizations. Using both the ZHC sample, NC split sample, and $\nu$-on-e scattering sample jointly with FHC samples would reduce the focusing uncertainties and the hadron production uncertainties in the future sterile analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Exploring the Connection Between Sampling Problems in Bayesian Inference and Statistical Mechanics

The Bayesian and statistical mechanical communities often share the same objective in their work - estimating and integrating probability distribution functions (pdfs) describing stochastic systems, models or processes. Frequently, these pdfs are complex functions of random variables exhibiting multiple, well separated local minima. Conventional strategies for sampling such pdfs are inefficient, sometimes leading to an apparent non-ergodic behavior. Several recently developed techniques for handling this problem have been successfully applied in statistical mechanics. In the multicanonical and Wang-Landau Monte Carlo (MC) methods, the correct pdfs are recovered from uniform sampling of the parameter space by iteratively establishing proper weighting factors connecting these distributions. Trivial generalizations allow for sampling from any chosen pdf. The closely related transition matrix method relies on estimating transition probabilities between different states. All these methods proved to generate estimates of pdfs with high statistical accuracy. In another MC technique, parallel tempering, several random walks, each corresponding to a different value of a parameter (e.g. "temperature"), are generated and occasionally exchanged using the Metropolis criterion. This method can be considered as a statistically correct version of simulated annealing. An alternative approach is to represent the set of independent variables as a Hamiltonian system. Considerab!e progress has been made in understanding how to ensure that the system obeys the equipartition theorem or, equivalently, that coupling between the variables is correctly described. Then a host of techniques developed for dynamical systems can be used. Among them, probably the most powerful is the Adaptive Biasing Force method, in which thermodynamic integration and biased sampling are combined to yield very efficient estimates of pdfs. The third class of methods deals with transitions between states described by rate constants. These problems are isomorphic with chemical kinetics problems. Recently, several efficient techniques for this purpose have been developed based on the approach originally proposed by Gillespie. Although the utility of the techniques mentioned above for Bayesian problems has not been determined, further research along these lines is warranted

Pohorille, Andrew↗

A 3-D Coupled CFD-DSMC Solution Method With Application to the Mars Sample Return Orbiter

A method to obtain coupled Computational Fluid Dynamics-Direct Simulation Monte Carlo (CFD-DSMC), 3-D flow field solutions for highly blunt bodies at low incidence is presented and applied to one concept of the Mars Sample Return Orbiter vehicle as a demonstration of the technique. CFD is used to solve the high-density blunt forebody flow defining an inflow boundary condition for a DSMC solution of the afterbody wake flow. By combining the two techniques in flow regions where most applicable, the entire mixed flow field is modeled in an appropriate manner.

Glass, Christopher E.↗

Developing Atmospheric Retrieval Methods for Direct Imaging Spectroscopy of Gas Giants in Reflected Light I: Methane Abundances and Basic Cloud Properties

Reflected light spectroscopy and photometry of cool, directly imaged extrasolar giant planets are expected to be performed in the next decade by space-based telescopes equipped with optical wavelength coronagraphs and integral field spectrographs, such as the Wide-Field Infrared Survey Telescope (WFIRST). We are developing a new atmospheric retrieval methodology to help assess the science return and inform the instrument design for such future missions, and ultimately interpret the resulting observations. Our retrieval technique employs an albedo model coupled with both a Markov chain Monte Carlo Ensemble Sampler (emcee) and a multimodal nested sampling algorithm (MultiNest) to map the posterior distribution. This combination makes the global evidence calculation more robust for any given model, and highlights possible discrepancies in the likelihood maps. Here we apply this methodology to simulated spectra of cool giant planets. As a proof-of-concept, our current atmospheric model contains 1 or 2 cloud layers, methane as a major absorber, and a H2-He background gas. This 6-to-9 parameter model is appropriate for Jupiter-like planets and can be easily expanded in the future. In addition to deriving the marginal likelihood distribution and confidence intervals for the model parameters, we perform model selection to determine the significance of methane and cloud detection as a function of expected signal-to-noise, in the presence of spectral noise correlations. After internal validation, the method is applied to realistic reflected-light spectra of Jupiter, Saturn, and HD 99492 c, a likely observing target. We find that the presence or absence of clouds and methane can be determined with high accuracy, while parameters uncertainties are model-dependent.

spectroscopy↗

Sputter deposition of ultrathick Bi coatings onto rotating substrates for inertial confinement fusion

Sputter-deposited Bi is a potential candidate material for the production of inertial confinement fusion (ICF) hohlraums, which are sphero-cylindrical canisters that serve as the housing for hydrogen-fuel-filled capsules in ICF implosions. The fabrication of hohlraums involves deposition onto rotating sphero-cylindrical substrates and, hence, requires an understanding of oblique angle deposition phenomena. Here, we systematically study the effect of dynamic substrate tilt on properties of Bi films deposited onto rotating planar substrates. Results show that film morphology, porosity, and electrical resistivity depend on substrate tilt. In comparison with films deposited onto substrates adhered to a stationary sample holder, films deposited on rotating substrates have more uniform microstructure with fewer void defects. Based on Monte Carlo simulations, these changes in film morphology and microstructure are attributed to averaging and geometric effects related to sample position and rotation. In conclusion, the fabrication of an ∼ 50-µm-thick Bi film on a rotating sphero-cylindrical mandrel demonstrates a significant step toward the production of Bi hohlraums.

Bismuth↗

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗

Solid-propellant rocket motor ballistic performance variation analyses

Results are presented of research aimed at improving the assessment of off-nominal internal ballistic performance including tailoff and thrust imbalance of two large solid-rocket motors (SRMs) firing in parallel. Previous analyses using the Monte Carlo technique were refined to permit evaluation of the effects of radial and circumferential propellant temperature gradients. Sample evaluations of the effect of the temperature gradients are presented. A separate theoretical investigation of the effect of strain rate on the burning rate of propellant indicates that the thermoelastic coupling may cause substantial variations in burning rate during highly transient operating conditions. The Monte Carlo approach was also modified to permit the effects on performance of variation in the characteristics between lots of propellants and other materials to be evaluated. This permits the variabilities for the total SRM population to be determined. A sample case shows, however, that the effect of these between-lot variations on thrust imbalances within pairs of SRMs is minor in compariosn to the effect of the within-lot variations. The revised Monte Carlo and design analysis computer programs along with instructions including format requirements for preparation of input data and illustrative examples are presented.

Sforzini, R. H.↗