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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these diculties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measure and a sparsity enhancement rotation procedure. This sparsity-enhancing rotation method was initially proposed in our previous work [1] for Gaussian density distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such diculties, we developed a new data-driven approach to construct orthonormal polynomials for polynomials for arbitrary mutually dependent (amdP) randomness, ensuring the constructed basis maintains the orthogonality/near-orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery, with only limited training data, of a sparse representation of the target functions. The eectiveness of our method is demonstrated in challenging problems such as PDEs and realistic molecular systems within high-dimensional conformational space (O(10)) where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures.

Lei, Huan↗
DOE OSTI2012

THE RECENT STELLAR ARCHEOLOGY OF M31-THE NEAREST RED DISK GALAXY

We examine the star-forming history of the M31 disk during the past few hundred Myr. The luminosity functions (LFs) of main-sequence stars at distances R{sub GC} > 21 kpc (i.e., >4 disk scale lengths) are matched by models that assume a constant star formation rate (SFR). However, at smaller R{sub GC} the LFs suggest that during the past {approx}10 Myr the SFR was 2-3 times higher than during the preceding {approx}100 Myr. The rings of cool gas that harbor a significant fraction of the current star-forming activity are traced by stars with ages {approx}100 Myr, indicating that (1) these structures have ages of at least 100 Myr and (2) stars in these structures do not follow the same relation between age and random velocity as their counterparts throughout the disks of other spiral galaxies, probably due to the inherently narrow orbital angular momentum distribution of the giant molecular clouds in these structures. The distribution of evolved red stars is not azimuthally symmetric, in the sense that the projected density along the northeast segment of the major axis is roughly twice that on the opposite side of the galaxy. The northeast arm of the major axis thus appears to be a fossil star-forming area that dates to intermediate epochs. Such a structure may be the consequence of interactions with a companion galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Phase-space sensitivity (weight functions) of 3 MeV proton diagnostics

Abstract The weight function W ( X ) is a diagnostic sensitivity to phase-space variables X that relates the measured signal C to the distribution function F ( X ) through the equation C = ∫ W ( X ) F ( X ) d X . In the present work, an algorithm to calculate W for a diagnostic that measures 3 MeV protons produced in d(d,p)t fusion reactions between a fast ion and a thermal deuteron is developed. The emitted protons escape the tokamak on curved orbits and are detected. These curved orbits constitute effective diagnostic ‘sightlines’. The presented algorithm accounts for the complications associated with these curved sightlines. An initial calculation of time-reversed proton orbits computes effective solid angles and sightlines for the relevant range of incident proton velocity vectors. These precomputed orbits are then used within the framework of FIDASIM (2020 Plasma Phys. Cont. Fusion 62 105008) to calculate the reactivity averaged over the thermal distribution of the ‘target’ deuterons and the probability that a fast ion of specified energy and pitch has a gyroangle that is consistent with the kinematic equations along each of the sightlines. Comparisons with analytic formulas and with independent calculations for the Mega Amp Spherical Tokamak 3 MeV proton diagnostic verify the algorithm.

Physics↗
DOE OSTI2019

Discrete game-theoretic analysis of defense in correlated cyber-physical systems

A cyber-physical system (CPS) is composed of a discrete number of cyber and physical components and subject to internal failures and external disruptions. The functionality of CPS therefore is determined not only by cyber and physical components but the adversary’s attacker strategy. We characterize the effect of cyber-physical interdependency on the CPS survival probability using a product-form function with cyber and physical exponential correlation coefficients. We model simultaneous and sequential discrete games between the provider and attacker on a CPS infrastructure to analyze its survivability and reinforcement strategy at Nash equilibrium. Our results show that the cyber and physical correlation coefficients can significantly affect CPS survival probability. In general, the provider’s cyber- (or physical-) reinforcement level increases as the cyber- (or physical-) attack level increases. In each of cyber and physical domains, the reinforcement level first increases then decreases in its own correlation coefficient, probability of successful component attacks, and maximum level of available resources, but decreases in the correlation coefficient of the other domain. We apply this game-theoretic analysis to a cloud computing infrastructure, and show that its residual capacity is relatively high when the attacker has no information about the distribution of servers. Finally, a high level of survival probability does not necessarily lead to high utility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble

Abstract Projections of changes in extreme climate are sometimes predicted by multimodel ensemble methods that combine forecasts from individual simulation models using weighted averaging. One method to assign weight to each model is the Bayesian model averaging (BMA) in which posterior probability is used. For the cases of extreme climate, the generalized extreme value distribution (GEVD) is typically used. We applied the approach of GEV‐embedded BMA to a series of 35 years of the annual maximum daily precipitation data (both historical data and data gathered from simulation experiments for future periods) over the Korean peninsula as simulated by the models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Simulation data under two Representative Concentration Pathway (RCP) scenarios, namely RCP4.5 and RCP8.5, were used. Observed data and 17 CMIP5 models for 12 gird cells in Korea have been examined to predict future changes in precipitation extremes. A simple regional frequency analysis of pooling observations from three stations in each cell was employed to reduce the estimation variance and local fluctuations. A bias correction technique using the regression‐type transfer function was applied to these simulation data. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1971–2005), were estimated for two future periods, namely Period 1 (2021–2050) and Period 2 (2066–2095). From these analyses, relative increase observed in the spatially averaged 20‐year (50‐year) return level was approximately 23% (16%) and 45% (36%) in the RCP4.5 and RCP8.5 experiments, respectively, by the end of the 21st century. We concluded that extreme rainfalls will likely occur two times and four times more frequently in the RCP4.5 and RCP8.5 scenarios, respectively, as compared to in the reference years by the end of the 21st century.

Lee, Youngsaeng↗
DOE OSTI2010

Uncertainty quantification in the presence of limited climate model data with discontinuities.

Uncertainty quantification in climate models is challenged by the prohibitive cost of a large number of model evaluations for sampling. Another feature that often prevents classical uncertainty analysis from being readily applicable is the bifurcative behavior in the climate data with respect to certain parameters. A typical example is the Meridional Overturning Circulation in the Atlantic Ocean. The maximum overturning stream function exhibits a discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO2 forcing. In order to propagate uncertainties from model parameters to model output we use polynomial chaos (PC) expansions to represent the maximum overturning stream function in terms of the uncertain climate sensitivity and CO2 forcing parameters. Since the spectral methodology assumes a certain degree of smoothness, the presence of discontinuities suggests that separate PC expansions on each side of the discontinuity will lead to more accurate descriptions of the climate model output compared to global PC expansions. We propose a methodology that first finds a probabilistic description of the discontinuity given a number of data points. Assuming the discontinuity curve is a polynomial, the algorithm is based on Bayesian inference of its coefficients. Markov chain Monte Carlo sampling is used to obtain joint distributions for the polynomial coefficients, effectively parameterizing the distribution over all possible discontinuity curves. Next, we apply the Rosenblatt transformation to the irregular parameter domains on each side of the discontinuity. This transformation maps a space of uncertain parameters with specific probability distributions to a space of i.i.d standard random variables where orthogonal projections can be used to obtain PC coefficients. In particular, we use uniform random variables that are compatible with PC expansions based on Legendre polynomials. The Rosenblatt transformation and the corresponding PC expansions for the model output on either side of the discontinuity are applied successively for several realizations of the discontinuity curve. The climate model output and its associated uncertainty at specific design points is then computed by taking a quadrature-based integration average over PC expansions corresponding to possible realizations of the discontinuity curve.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

How decoherence affects the probability of slow-roll eternal inflation

We present that slow-roll inflation can become eternal if the quantum variance of the inflaton field around its slowly rolling classical trajectory is converted into a distribution of classical spacetimes inflating at different rates, and if the variance is large enough compared to the rate of classical rolling that the probability of an increased rate of expansion is sufficiently high. Both of these criteria depend sensitively on whether and how perturbation modes of the inflaton interact and decohere. Decoherence is inevitable as a result of gravitationally sourced interactions whose strength are proportional to the slow-roll parameters. However, the weakness of these interactions means that decoherence is typically delayed until several Hubble times after modes grow beyond the Hubble scale. We present perturbative evidence that decoherence of long-wavelength inflaton modes indeed leads to an ensemble of classical spacetimes with differing cosmological evolutions. We introduce the notion of per-branch observables—expectation values with respect to the different decohered branches of the wave function—and show that the evolution of modes on individual branches varies from branch to branch. Thus, single-field slow-roll inflation fulfills the quantum-mechanical criteria required for the validity of the standard picture of eternal inflation. Finally, for a given potential, the delayed decoherence can lead to slight quantitative adjustments to the regime in which the inflaton undergoes eternal inflation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region

1. LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. 2. In this study, we show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. 3. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g. canopy height models based on LiDAR data). 4. By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

New Studies of Short-Range Correlations and Their Effects on Nuclei

Over the last decade, data gathered on short range correlations (SRC) and the EMC effect have stimulated tremendous progress in our understanding of the nuclear force. In particular, the tensor force was found to be the major factor in the formation of N-N short range correlations. More recently a connection between the N-N SRC probabilities and EMC effect amplitudes was first observed. However, the real origin and characteristics of this connection have not been convincingly determined experimentally or theoretically. The project consists of several approved experiments scheduled to run at Jefferson Lab in Newport News, VA. These inclusive scattering measurements will provide a detailed map of the nature of short range correlations and its role in the in-medium modification of the nucleons. The isospin dependence in 2 N- and 3 N-SRCs will be accessed through the use of two light mirror nuclei 3 He and 3 H. An extensive A-dependence study of 2 N and 3 N-SRCs will be performed as well as a measurement of the quark distribution in short range correlations, i.e. the EMC eect in SRCs. A measurement of the deuteron tensor structure function in the deep inelastic scattering regime will verify the possible contribution of hidden color components in the deuteron wave function. This project aims to help answer the fundamental question on the nature of the short range interactions and their implications at the nucleonic and partonic levels.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Stochastic Analysis of Levee Stability Subject to Variable Seepage Conditions

© ASCE. Levee stability is highly influenced by seepage. Specifically, hydraulic conductivity distribution within a levee influences pore pressure distribution and controls the drained strength of the soil. In this study the influence of hydraulic conductivity and blanket layer thickness on failure probability is evaluated within the context of reliability analyses that also include soil strength and unit weight as random variables. First-Order Reliability Method (FORM) is used to evaluate reliability, rank random variables by importance and to obtain sensitivity of the solution to each random variable and its distribution parameters. Stability is computed using Spencer's method of slices coupled to a finite element seepage code to directly evaluate pore pressure. In addition, response surface method solutions are compared to the direct reliability solution to assess response surface accuracy. The results show that blanket layer thickness is more important than most strength parameters and that the uncertainty in hydraulic conductivity is less important to the variance in safety factor for the case of a low-permeability blanket layer. Numerical challenges caused by implementing a finite element limit-state function are discussed. Response surface methods are found to give a reasonable approximation to the direct reliability solution when the design point is between response surface fitting points.

Lanzafame, Robert↗
DOE OSTI2010

Experiments and global model of inductively coupled rf Ar/N{sub 2} discharges

Experiments with a Langmuir probe and optical emission spectroscopy are carried out in inductively coupled rf (13.56 MHz) Ar/N{sub 2} discharges at three total pressures of 30, 60, and 110 mTorr, varying the Ar fractions from 50% to 95%. The electron energy probability functions (EEPFs) measured at all Ar fractions can be approximated using different exponentials in the bulk and high-energy regions, resulting in two temperature approximation of the measured EEPFs. The electron density slightly increases with increasing the Ar fraction at the Ar fractions below 70%, beyond which it relatively abruptly increases. On the other hand, the electron temperatures gradually decrease with the increase in the Ar fraction. The vibrational temperature does not strongly depend on the Ar fraction, whereas the rotational temperature gradually increases with the increase in the Ar fraction. The density of nitrogen atoms remains constant at the Ar fractions below 80%, beyond which it monotonically decreases with increasing the Ar fraction. A global model for electropositive plasma is used in order to investigate the plasma chemistry in Ar/N{sub 2} discharges assuming the Maxwellian electron energy distribution. The model results are compared with the experimental results, obtaining reasonably good agreement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

The effect of cathode geometry on barium transport in hollow cathode plasmas

The effect of barium transport on the operation of dispenser hollow cathodes was investigated in numerical modeling of a cathode with two different orifice sizes. Despite large differences in cathode emitter temperature, emitted electron current density, internal xenon neutral and plasma densities, and size of the plasma-surface interaction region, the barium transport in the two geometries is qualitatively very similar. Barium is produced in the insert and flows to the surface through the porous structure. A buildup of neutral Ba pressure in the plasma over the emitter surface can suppress the reactions supplying the Ba, restricting the net production rate. Neutral Ba flows into the dense Xe plasma and has a high probability of being ionized at the periphery of this zone. The steady state neutral Ba density distribution is determined by a balance between pressure gradient forces and the drag force associated with collisions between neutral Ba and neutral Xe atoms. A small fraction of the neutral Ba is lost upstream. The majority of the neutral Ba is ionized in the high temperature Xe plasma and is pushed back to the emitter surface by the electric field. The steady state Ba{sup +} ion density distribution results from a balance between electrostatic and pressure forces, neutral Xe drag and Xe{sup +} ion drag with the dominant forces dependent on location in the discharge. These results indicate that hollow cathodes are very effective at recycling Ba within the discharge and therefore maintain a high coverage of Ba on the emitter surface, which reduces the work function and sustains high electron emission current densities at moderate temperatures. Barium recycling is more effective in the cathode with the smaller orifice because the Ba is ionized in the dense Xe plasma concentrated just upstream of the orifice and pushed back into the hollow cathode. Despite a lower emitter temperature, the large orifice cathode has a higher Ba loss rate through the orifice because the Xe plasma density peaks further upstream.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Kinetic theory of particle-in-cell simulation plasma and the ensemble averaging technique

Abstract We derive the kinetic theory of fluctuations in physically and numerically stable particle-in-cell (PIC) simulations of electrostatic plasmas. The starting point is the single-time correlation at the start of the simulation between the statistical fluctuations of the weighted densities of macroparticle centers in the plasma particle phase-space. The fluctuations are associated with different initial conditions, typically due to the random initial conditions (in velocity space) of the macroparticles/simulation plasma, assigned according to their initial distribution of probability. The single-time correlations at all time steps and in each spatial grid cell are then determined from the Laplace–Fourier transforms of the discretized Klimontovich-like equation for the macroparticles and Maxwell’s equations for the fields, as computed by modern PIC codes. We recover the expressions for the electrostatic field and the plasma particle density fluctuation autocorrelation spectra as well as the kinetic equations describing the average evolution of PIC-simulated plasma particles, first derived by Langdon (1970b Proc. 4th Conf. Numerical Simulation of Plasmas ) using a test macroparticle approach perturbing a discretized Vlasovian plasma and then averaging the obtained physical quantity over the initial macroparticle velocity distribution. We generalize and extend these results to the modern algorithms in PIC codes using arbitrary macroparticle weights. Analytical estimates of statistical fluctuation amplitudes are derived as a function of the plasma simulation parameters, using the central limit theorem in the limit of a large number of macroparticles per cell. The theory is then used to analyze the ensemble averaging technique of PIC simulations where statistical averages are performed over ensembles of PIC simulations, modeling the same plasma physics problem but using different statistical realizations of the initial distribution functions of the macroparticles. This method is illustrated by linear Landau damping uncovering (from noise, which is usually considered numerical) the physical fluctuations driven by a single small amplitude electrostatic wave perturbing a PIC simulation plasma in equilibrium.

fluctuations correlations↗
DOE OSTI2015

TU-CD-BRA-05: Atlas Selection for Multi-Atlas-Based Image Segmentation Using Surrogate Modeling

Purpose: The growing size and heterogeneity in training atlas necessitates sophisticated schemes to identify only the most relevant atlases for the specific multi-atlas-based image segmentation problem. This study aims to develop a model to infer the inaccessible oracle geometric relevance metric from surrogate image similarity metrics, and based on such model, provide guidance to atlas selection in multi-atlas-based image segmentation. Methods: We relate the oracle geometric relevance metric in label space to the surrogate metric in image space, by a monotonically non-decreasing function with additive random perturbations. Subsequently, a surrogate’s ability to prognosticate the oracle order for atlas subset selection is quantified probabilistically. Finally, important insights and guidance are provided for the design of fusion set size, balancing the competing demands to include the most relevant atlases and to exclude the most irrelevant ones. A systematic solution is derived based on an optimization framework. Model verification and performance assessment is performed based on clinical prostate MR images. Results: The proposed surrogate model was exemplified by a linear map with normally distributed perturbation, and verified with several commonly-used surrogates, including MSD, NCC and (N)MI. The derived behaviors of different surrogates in atlas selection and their corresponding performance in ultimate label estimate were validated. The performance of NCC and (N)MI was similarly superior to MSD, with a 10% higher atlas selection probability and a segmentation performance increase in DSC by 0.10 with the first and third quartiles of (0.83, 0.89), compared to (0.81, 0.89). The derived optimal fusion set size, valued at 7/8/8/7 for MSD/NCC/MI/NMI, agreed well with the appropriate range [4, 9] from empirical observation. Conclusion: This work has developed an efficacious probabilistic model to characterize the image-based surrogate metric on atlas selection. Analytical insights lead to valid guiding principles on fusion set size design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2010

X-RAY EMISSION FROM THE SOMBRERO GALAXY: DISCRETE SOURCES

We present a study of discrete X-ray sources in and around the bulge-dominated, massive Sa galaxy, Sombrero (M104), based on new and archival Chandra observations with a total exposure of {approx}200 ks. With a detection limit of L{sub X} {approx} 10{sup 37} erg s{sup -1} and a field of view covering a galactocentric radius of {approx}30 kpc (11.'5), 383 sources are detected. Cross-correlation with Spitler et al.'s catalog of Sombrero globular clusters (GCs) identified from HST/ACS observations reveals 41 X-ray sources in GCs, presumably low-mass X-ray binaries (LMXBs). Metal-rich GCs are found to have a higher probability of hosting these LMXBs, a trend similar to that found in elliptical galaxies. On the other hand, the four most luminous GC LMXBs, with apparently super-Eddington luminosities for an accreting neutron star, are found in metal-poor GCs. We quantify the differential luminosity functions (LFs) for both the detected GC and field LMXBs, whose power-law indices ({approx}1.1 for the GC-LF and {approx}1.6 for field-LF) are consistent with previous studies for elliptical galaxies. With precise sky positions of the GCs without a detected X-ray source, we further quantify, through a fluctuation analysis, the GC-LF at fainter luminosities down to 10{sup 35} erg s{sup -1}. The derived index rules out a faint-end slope flatter than 1.1 at a 2{sigma} significance, contrary to recent findings in several elliptical galaxies and the bulge of M31. On the other hand, the 2-6 keV unresolved emission places a tight constraint on the field LF, implying a flattened index of {approx}1.0 below 10{sup 37} erg s{sup -1}. We also detect 101 sources in the halo of Sombrero. The presence of these sources cannot be interpreted as galactic LMXBs whose spatial distribution empirically follows the starlight. Their number is also higher than the expected number of cosmic active galactic nuclei (52 {+-} 11 [1{sigma}]) whose surface density is constrained by deep X-ray surveys. We suggest that either the cosmic X-ray background is unusually high in the direction of Sombrero, or a distinct population of X-ray sources is present in the halo of Sombrero.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Excitation energy dependence of fragment-mass distributions from fission of 180,190 Hg formed in fusion reactions of 36 Ar + 144,154 Sm

Mass distributions of fission fragments from the compound nuclei 180 Hg and 190 Hg formed in fusion reactions 36 Ar + 144 Smand 36 Ar + 154 Sm, respectively, were measured at initial excitation energies of E* ( 180 Hg) = 33-66 MeV and E* ( 190 Hg) = 48-71 MeV. In the fission of 180 Hg, the mass spectra were well reproduced by assuming only an asymmetric-mass division, with most probable light and heavy fragment masses $\overline{A}_L$ / $\overline{A}_H$ = 79/101. The mass asymmetry for 180 Hg agrees well with that obtained in the low-energy β + /EC-delayed fission of 180 Tl, from our earlier ISOLDE(CERN) experiment. Fission of 190 Hg is found to proceed in a similar way, delivering the mass asymmetry of $\overline{A}_L$ / $\overline{A}_H$ = 83/107, throughout the measured excitation energy range. The persistence as a function of excitation energy of the mass-asymmetric fission for both proton-rich Hg isotopes gives strong evidence for the survival of microscopic effects up to effective excitation energies of compound nuclei as high as 40 MeV. In conclusion, this behavior is different from fission of actinide nuclei and heavier mercury isotope 198 Hg.

(180↗
DOE OSTI2020

Convergence acceleration of Monte Carlo many-body perturbation methods by direct sampling

In the Monte Carlo many-body perturbation (MC-MP) method, the conventional correlation-correction formula, which is a long sum of products of low-dimensional integrals, is first recast into a short sum of high-dimensional integrals over electron-pair and imaginary-time coordinates. These high-dimensional integrals are then evaluated by the Monte Carlo method with random coordinates generated by the Metropolis–Hasting algorithm according to a suitable distribution. The latter algorithm, while advantageous in its ability to sample nearly any distribution, introduces autocorrelation in sampled coordinates, which in turn increases the statistical uncertainty of the integrals and thus the computational cost. It also involves wasteful rejected moves and an initial “burn-in” step as well as displays hysteresis. Here, an algorithm is proposed that directly produces a random sequence of electron-pair coordinates for the same distribution used in the MC-MP method, which is free from autocorrelation, rejected moves, a burn-in step, or hysteresis. Furthermore, this direct-sampling algorithm is shown to accelerate second- (MC-MP2) and third-order Monte Carlo many-body perturbation (MC-MP3) calculations by up to 222% and 38%, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

The spatial structure of young stellar clusters. II. Total young stellar populations

We investigate the intrinsic stellar populations (estimated total numbers of OB and pre-main-sequence stars down to 0.1 M{sub ⊙}) that are present in 17 massive star-forming regions (MSFRs) surveyed by the MYStIX project. The study is based on the catalog of >31,000 MYStIX Probable Complex Members with both disk-bearing and disk-free populations, compensating for extinction, nebulosity, and crowding effects. Correction for observational sensitivities is made using the X-ray luminosity function and the near-infrared initial mass function—a correction that is often not made by infrared surveys of young stars. The resulting maps of the projected structure of the young stellar populations, in units of intrinsic stellar surface density, allow direct comparison between different regions. Several regions have multiple dense clumps, similar in size and density to the Orion Nebula Cluster. The highest projected density of ∼34,000 stars pc{sup −2} is found in the core of the RCW 38 cluster. Histograms of surface density show different ranges of values in different regions, supporting the conclusion of Bressert et al. that no universal surface-density threshold can distinguish between clustered and distributed star formation. However, a large component of the young stellar population of MSFRs resides in dense environments of 200–10,000 stars pc{sup −2} (including within the nearby Orion molecular clouds), and we find that there is no evidence for the B10 conclusion that such dense regions form an extreme “tail” of the distribution. Tables of intrinsic populations for these regions are used in our companion study of young cluster properties and evolution.

79 ASTRONOMY AND ASTROPHYSICS↗