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NREL will collaborate with Peak to develop advanced data analytics methods to improve the situational awareness of its RC area by utilizing measurement data. The main objective is to develop accurate load forecasting methods to predict system loads at different temporal scales.
Context: Most stars from in groups which with time disperse, building the field population of their host galaxy. In the Milky Way, open clusters have been continuously forming in the disk up to the present time, providing it with stars spanning a broad range of ages and masses. Observations of the details of cluster dissolution are, however, scarce. One of the main difficulties is obtaining a detailed characterisation of the internal cluster kinematics, which requires very high-quality proper motions. For open clusters, which are typically loose groups with tens to hundreds of members, there is the additional difficulty of inferring kinematic structures from sparse and irregular distributions of stars. Aims: Here, we aim to analyse internal stellar kinematics of open clusters, and identify rotation, expansion, or contraction patterns. Methods: We use Gaia Early Data Release 3 (EDR3) astrometry and integrated nested Laplace approximations to perform vector-field inference and create spatio-kinematic maps of 1237 open clusters. The sample is composed of clusters for which individual stellar memberships were already known, thus minimising contamination from field stars in the velocity maps. Projection effects were corrected using EDR3 data complemented with radial velocities from Gaia Data Release 2 and other surveys. Results: We report the detection of rotation patterns in eight open clusters. Nine additional clusters display possible rotation signs. We also observe 14 expanding clusters, with 15 other objects showing possible expansion patterns. Contraction is evident in two clusters, with one additional cluster presenting a more uncertain detection. In total, 53 clusters are found to display kinematic structures. Within these, elongated spatial distributions suggesting tidal tails are found in five clusters. These results indicate that the approach developed here can recover kinematic patterns from noisy vector fields, as those from astrometric measurements of open clusters or other stellar or galactic populations, thus offering a powerful probe for exploring the internal kinematics and dynamics of these types of objects.
The Large Magellanic Cloud (LMC) harbors a rich and diverse system of star clusters, whose ages, chemical abundances, and positions provide information about the LMC history of star formation. We use Science Verification imaging data from the Dark Energy Survey to increase the census of known star clusters in the outer LMC and to derive physical parameters for a large sample of such objects using a spatially and photometrically homogeneous data set. Our sample contains 255 visually identified cluster candidates, of which 109 were not listed in any previous catalog. We quantify the crowding effect for the stellar sample produced by the DES Data Management pipeline and conclude that the stellar completeness is < 10% inside typical LMC cluster cores. We therefore develop a pipeline to sample and measure stellar magnitudes and positions around the cluster candidates using DAOPHOT. We also implement a maximum-likelihood method to fit individual density profiles and colour-magnitude diagrams. For 117 (from a total of 255) of the cluster candidates (28 uncatalogued clusters), we obtain reliable ages, metallicities, distance moduli and structural parameters, confirming their nature as physical systems. The distribution of cluster metallicities shows a radial dependence, with no clusters more metal-rich than [Fe/H] ~ -0.7 beyond 8 kpc from the LMC center. Furthermore, the age distribution has two peaks at ≃ 1.2 Gyr and ≃ 2.7 Gyr.
Purpose: To investigate how doses to cellular (microscopic) targets depend on cell morphology, and how cellular doses relate to doses to bulk tissues and water for 20 to 370 keV photon sources using Monte Carlo (MC) simulations. Methods: Simulation geometries involve cell clusters, single cells, and single nuclear cavities embedded in various healthy and cancerous bulk tissue phantoms. A variety of nucleus and cytoplasm elemental compositions are investigated. Cell and nucleus radii range from 5 to 10 microns and 2 to 9 microns, respectively. Doses to water and bulk tissue cavities are compared to nucleus and cytoplasm doses. Results: Variations in cell dose with simulation geometry are most pronounced for lower energy sources. Nuclear doses are sensitive to the surrounding geometry: the nuclear dose in a multicell model differs from the dose to a cavity of nuclear medium in an otherwise homogeneous bulk tissue phantom by more than 7% at 20 keV. Nuclear doses vary with cell size by up to 20% at 20 keV, with 10% differences persisting up to 90 keV. Bulk tissue and water cavity doses differ from cellular doses by up to 16%. MC results are compared to cavity theory predictions; large and small cavity theories qualitatively predict nuclear doses for energies below and above 50 keV, respectively. Burlin’s (1969) intermediate cavity theory best predicts MC results with an average discrepancy of 4%. Conclusion: Cellular doses vary as a function of source energy, subcellular compartment size, elemental composition, and tissue morphology. Neither water nor bulk tissue is an appropriate surrogate for subcellular targets in radiation dosimetry. The influence of microscopic inhomogeneities in the surrounding environment on the nuclear dose and the importance of the nucleus as a target for radiation-induced cell death emphasizes the potential importance of cellular dosimetry for understanding radiation effects. Funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Research Chairs Program (CRC), and the Ontario Ministry of Training, Colleges and Universities.
The accuracy and transferability of the electronic structure approach combining dispersionless density functional theory (DFT) [K. Pernal et al., Phys. Rev. Lett. 103, 263201 (2009)] with the method of increments [H. Stoll, J. Chem. Phys. 97, 8449 (1992)], are validated for the interaction between the noble-gas Ne, Ar, Kr, and Xe atoms and coronene/graphene/graphite surfaces. This approach uses the method of increments for surface cluster models to extract intermonomer dispersion-like (2- and 3-body) correlation terms at coupled cluster singles and doubles and perturbative triples level, while periodic dispersionless density functionals calculations are performed to estimate the sum of Hartree-Fock and intramonomer correlation contributions. Dispersion energy contributions are also obtained using DFT-based symmetry-adapted perturbation theory [SAPT(DFT)]. An analysis of the structure of the X/surface (X = Ne, Ar, Kr, and Xe) interaction energies shows the excellent transferability properties of the leading intermonomer correlation contributions across the sequence of noble-gas atoms, which are also discussed using the Drude oscillator model. We further compare these results with van der Waals-(vdW)-corrected DFT-based approaches. As a test of accuracy, the energies of the low-lying nuclear bound states supported by the laterally averaged X/graphite potentials (X = {sup 3}He, {sup 4}He, Ne, Ar, Kr, and Xe) are calculated and compared with the best estimations from experimental measurements and an atom-bond potential model using the ab initio-assisted fine-tuning of semiempirical parameters. The bound-state energies determined differ by less than 6–7 meV (6%) from the atom-bond potential model. The crucial importance of including incremental 3-body dispersion-type terms is clearly demonstrated, showing that the SAPT(DFT) approach effectively account for these terms. With the deviations from the best experimental-based estimations smaller than 2.3 meV (1.9%), the accuracy of the combined DFT and post-HF incremental scheme is established for all the noble-gas atoms. With relative deviations smaller than 4% and 11%, good agreement is also achieved by applying the vdW-corrected DFT treatments PBE-D3 and vdW-DF2 for noble-gas atoms heavier than neon.
Purpose: Secondary MU checks are an important tool used during a physics review of a treatment plan. Commercial software packages offer varying degrees of theoretical dose calculation accuracy, depending on the modality involved. Dose calculations of VMAT plans are especially prone to error due to the large approximations involved. Monte Carlo (MC) methods are not commonly used due to their long run times. We investigated two methods to increase the computational efficiency of MC dose simulations with the BEAMnrc code. Distributed computing resources, along with optimized code compilation, will allow for accurate and efficient VMAT dose calculations. Methods: The BEAMnrc package was installed on a high performance computing cluster accessible to our clinic. MATLAB and PYTHON scripts were developed to convert a clinical VMAT DICOM plan into BEAMnrc input files. The BEAMnrc installation was optimized by running the VMAT simulations through profiling tools which indicated the behavior of the constituent routines in the code, e.g. the bremsstrahlung splitting routine, and the specified random number generator. This information aided in determining the most efficient compiling parallel configuration for the specific CPU’s available on our cluster, resulting in the fastest VMAT simulation times. Our method was evaluated with calculations involving 10{sup 8} – 10{sup 9} particle histories which are sufficient to verify patient dose using VMAT. Results: Parallelization allowed the calculation of patient dose on the order of 10 – 15 hours with 100 parallel jobs. Due to the compiler optimization process, further speed increases of 23% were achieved when compared with the open-source compiler BEAMnrc packages. Conclusion: Analysis of the BEAMnrc code allowed us to optimize the compiler configuration for VMAT dose calculations. In future work, the optimized MC code, in conjunction with the parallel processing capabilities of BEAMnrc, will be applied to provide accurate and efficient secondary MU checks.
Over the last decade, Heliophysics researchers have increasingly adopted a variety of machine learning methods such as artificial neural networks, decision trees, and clustering algorithms into their workflow. Adoption of these advanced data science methods had quickly outpaced institutional response, but many professional organizations such as the European Commission, the National Aeronautics and Space Administration (NASA), and the American Geophysical Union have now issued (or will soon issue) standards for artificial intelligence and machine learning that will impact scientific research. These standards add further (necessary) burdens on the individual researcher who must now prepare the public release of data and code in addition to traditional paper writing. Support for these is not reflected in the current state of institutional support, community practices, or governance systems. We examine here some of these principles and how our institutions and community can promote their successful adoption within the Heliophysics discipline.
We present a catalog of galaxy cluster candidates, selected through their Sunyaev-Zel'dovich (SZ) effect signature in the first 720 deg{sup 2} of the South Pole Telescope (SPT) survey. This area was mapped with the SPT in the 2008 and 2009 austral winters to a depth of {approx}18 {mu}K{sub CMB}-arcmin at 150 GHz; 550 deg{sup 2} of it was also mapped to {approx}44 {mu}K{sub CMB}-arcmin at 95 GHz. Based on optical imaging of all 224 candidates and near-infrared imaging of the majority of candidates, we have found optical and/or infrared counterparts for 158, which we then classify as confirmed galaxy clusters. Of these 158 clusters, 135 were first identified as clusters in SPT data, including 117 new discoveries reported in this work. This catalog triples the number of confirmed galaxy clusters discovered through the SZ effect. We report photometrically derived (and in some cases spectroscopic) redshifts for confirmed clusters and redshift lower limits for the remaining candidates. The catalog extends to high redshift with a median redshift of z = 0.55 and maximum confirmed redshift of z = 1.37. Forty-five of the clusters have counterparts in the ROSAT bright or faint source catalogs from which we estimate X-ray fluxes. Based on simulations, we expect the catalog to be nearly 100% complete above M {sub 500} Almost-Equal-To 5 Multiplication-Sign 10{sup 14} M {sub Sun} h {sup -1} {sub 70} at z {approx}> 0.6. There are 121 candidates detected at signal-to-noise ratio greater than five, at which the catalog purity is measured to be 95%. From this high-purity subsample, we exclude the z < 0.3 clusters and use the remaining 100 candidates to improve cosmological constraints following the method presented by Benson et al. Adding the cluster data to CMB + BAO + H {sub 0} data leads to a preference for non-zero neutrino masses while only slightly reducing the upper limit on the sum of neutrino masses to {Sigma}m {sub {nu}} < 0.38 eV (95% CL). For a spatially flat wCDM cosmological model, the addition of this catalog to the CMB + BAO + H {sub 0} + SNe results yields {sigma}{sub 8} = 0.807 {+-} 0.027 and w = -1.010 {+-} 0.058, improving the constraints on these parameters by a factor of 1.4 and 1.3, respectively. The larger cluster catalog presented in this work leads to slight improvements in cosmological constraints from those presented by Benson et al. These cosmological constraints are currently limited by uncertainty in the cluster mass calibration, not the size or quality of the cluster catalog. A multi-wavelength observation program to improve the cluster mass calibration will make it possible to realize the full potential of the final 2500 deg{sup 2} SPT cluster catalog to constrain cosmology.
Numerical results for ground-state and excited-state properties (energies, double occupancies, and Matsubara-axis self-energies) of the single-orbital Hubbard model on a two-dimensional square lattice are presented, in order to provide an assessment of our ability to compute accurate results in the thermodynamic limit. Many methods are employed, including auxiliary-field quantum Monte Carlo, bare and bold-line diagrammatic Monte Carlo, method of dual fermions, density matrix embedding theory, density matrix renormalization group, dynamical cluster approximation, diffusion Monte Carlo within a fixed-node approximation, unrestricted coupled cluster theory, and multireference projected Hartree-Fock methods. Comparison of results obtained by different methods allows for the identification of uncertainties and systematic errors. The importance of extrapolation to converged thermodynamic-limit values is emphasized. Furthermore, cases where agreement between different methods is obtained establish benchmark results that may be useful in the validation of new approaches and the improvement of existing methods.
Mining electronic health records (EHRs) to identify contextually related clinical concept clusters that tend to co-occur temporarily and consistently could improve data-driven clinical pathway (CP) construction. However, the automatic extraction of contextually related clinical concept clusters contains a vast amount of irrelevant information. Hence, this paper proposes a knowledge network-enabled literature-based discovery (LBD) approach to remove noise from clusters. The authors used published literature to filter spurious concepts from the clusters and used data from the US Department of Veterans Affairs’s major depressive disorder (MDD) cohort of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) for their experimentation. The approach was applied to 2,967 clusters extracted from the MDD OEF/OIF database. The experimental results demonstrate that the proposed approach can filter 94% of the irrelevant information. Moreover, the authors applied various network mining algorithms to analyze the clusters and demonstrated that LBD, along with network mining techniques, is a useful method for finding accurate contextually related clinical concept clusters. This could help domain researchers perform advanced analytics in CPs.
We present an optimization method for the assignment of photometric galaxies to a chosen set of redshift bins. This is achieved by combining simulated annealing, an optimization algorithm inspired by solid-state physics, with an unsupervised machine learning method, a self-organizing map (SOM) of the observed colours of galaxies. Starting with a sample of galaxies that is divided into redshift bins based on a photometric redshift point estimate, the simulated annealing algorithm repeatedly reassigns SOM-selected subsamples of galaxies, which are close in colour, to alternative redshift bins. We optimize the clustering cross-correlation signal between photometric galaxies and a reference sample of galaxies with well-calibrated redshifts. Depending on the effect on the clustering signal, the reassignment is either accepted or rejected. By dynamically increasing the resolution of the SOM, the algorithm eventually converges to a solution that minimizes the number of mismatched galaxies in each tomographic redshift bin and thus improves the compactness of their corresponding redshift distribution. This method is demonstrated on the synthetic Legacy Survey of Space and Time cosmoDC2 catalogue. We find a significant decrease in the fraction of catastrophic outliers in the redshift distribution in all tomographic bins, most notably in the highest redshift bin with a decrease in the outlier fraction from 57 percent to 16 percent.
This paper provides an overview of the new features and model updates in the upcoming release of the NASA Analogy Software Cost Tool (ASCoT). ASCoT, hosted within the Online NASA Space Estimation Tools (ONSET) on the One NASA Cost Engineering (ONCE) Database, is a web-based tool that provides a suite of estimation tools to support early lifecycle NASA flight software cost analysis. In addition to the traditional parametric flight software costing method COCOMO II, ASCoT contains a Bayesian linear regression to predict total flight software development cost as a function of total spacecraft cost, as well as four analogic methods: k-Nearest Neighbors (kNN) and Clustering models to predict Effort (in work-months) and total source lines of code (SLOC). These methods are designed to work primarily with system-level inputs such as mission type (orbiter, lander, etc.), mission destination (Earth, Inner Planetary, etc.), and the number of instruments and deployables. Nonlinear principal components analysis (NLPCA) is performed to find the principal features of the data composed of both categorical and numerical variables and is necessary prior to defining our analogic methods. Sensitivity analyses and in- and out-of-sample model performance results are presented for the Bayesian CER and the analogic models.
ABSTRACT Cosmological N-body simulations provide numerical predictions of the structure of the Universe against which to compare data from ongoing and future surveys, but the growing volume of the Universe mapped by surveys requires correspondingly lower statistical uncertainties in simulations, usually achieved by increasing simulation sizes at the expense of computational power. It was recently proposed to reduce simulation variance without incurring additional computational costs by adopting fixed-amplitude initial conditions. This method has been demonstrated not to introduce bias in various statistics, including the two-point statistics of galaxy samples typically used for extracting cosmological parameters from galaxy redshift survey data, but requires us to revisit current methods for estimating covariance matrices of clustering statistics for simulations. In this work, we find that it is not trivial to construct covariance matrices analytically for fixed-amplitude simulations, but we demonstrate that ezmock (Effective Zel’dovich approximation mock catalogue), the most efficient method for constructing mock catalogues with accurate two- and three-point statistics, provides reasonable covariance matrix estimates for such simulations. We further examine how the variance suppression obtained by amplitude-fixing depends on three-point clustering, small-scale clustering, and galaxy bias, and propose intuitive explanations for the effects we observe based on the ezmock bias model.
We demonstrate that it is possible to constrain the energy content of the Universe with high accuracy using observations of clusters of galaxies only. The degeneracies in the cosmological parameters are lifted by combining constraints from different observables of galaxy clusters. We show that constraints on cosmological parameters from galaxy cluster number counts as a function of redshift and accurate angular diameter distance measurements to clusters are complementary to each other and their combination can constrain the energy density content of the Universe well. The number counts can be obtained from X-ray and/or SZ (Sunyaev-Zeldovich effect) surveys, the angular diameter distances can be determined from deep observations of the intra-cluster gas using their thermal bremsstrahlung X-ray emission and the SZ effect (X-SZ method). In this letter we combine constraints from simulated cluster number counts expected from a 12 deg2 SZ cluster survey and constraints from simulated angular diameter distance measurements based on using the X-SZ method assuming an expected accuracy of 7% in the angular diameter distance determination of 70 clusters with redshifts less than 1.5. We find that R, can be determined within about 25%, A within 20%, and w within 16%. Any cluster survey can be used to select clusters for high accuracy distance measurements, but we assumed accurate angular diameter distance measurements for only 70 clusters since long observations are necessary to achieve high accuracy in distance measurements. Thus the question naturally arises: How to select clusters of galaxies for accurate diameter distance determinations? In this letter, as an example, we demonstrate that it is possible to optimize this selection changing the number of clusters observed, and the upper cut off of their redshift range. We show that constraints on cosmological parameters from combining cluster number counts and angular diameter distance measurements, as opposed to general expectations, will not improve substantially selecting clusters with redshifts higher than one. This important conclusion allow us to restrict our cluster sample to clusters closer than one, in a range where the observational time for accurate distance measurements are more manageable. Subject headings: cosmological parameters - cosmology: theory - galaxies: clusters: general - X-rays: galaxies: clusters
The development of novel energy materials and fuels is required to expand current available energy sources. Aiming to reach this goal, there is growing interest in using molecular hydrogen as an energy carrier due to its abundance and high energy density. Liquid organic hydrogen carriers (LOHCs) are a promising route to the large-scale storage and transport of hydrogen for use in the energy economy. The search for thermodynamically viable LOHC molecules for real world use has led to a set of constraints on the dehydrogenation enthalpy and the minimum gravimetric hydrogen capacity. These constraints allow one to formulate the search for an ideal LOHC candidate molecule as an optimization problem well suited to the strengths of machine learning and artificial intelligence computational approaches. A critical barrier to a large-scale, high-throughput screening of LOHC candidate molecules is the lack of reliable training data. Computational electronic structure methods including density functional theory, coupled cluster approximations, and diffusion Monte Carlo can be used to provide training data where experimental data are either unreliable or do not exist. In this work, we use these methods to calculate the dehydrogenation energies and enthalpies of candidate LOHC molecules.
We have performed new wide-field photometry of the young open cluster NGC 6231 to study the shape of the initial mass function (IMF) and mass segregation. We also investigated the reddening law toward NGC 6231 from optical to mid-infrared color excess ratios, and found that the total-to-selective extinction ratio is R{sub V} = 3.2, which is very close to the normal value. But many early-type stars in the cluster center show large color excess ratios. We derived the surface density profiles of four member groups, and found that they reach the surface density of field stars at about 10', regardless of stellar mass. The IMF of NGC 6231 is derived for the mass range 0.8-45 M{sub Sun }. The slope of the IMF of NGC 6231 ({Gamma} = -1.1 {+-} 0.1) is slightly shallower than the canonical value, but the difference is marginal. In addition, the mass function varies systematically, and is a strong function of radius-it is very shallow at the center, and very steep at the outer ring suggesting the cluster is mass segregated. We confirm the mass segregation for the massive stars (m {approx}> 8 M{sub Sun }) by a minimum spanning tree analysis. Using a Monte Carlo method, we estimate the total mass of NGC 6231 to be about 2.6 ({+-} 0.6) Multiplication-Sign 10{sup 3} M{sub Sun }. We constrain the age of NGC 6231 by comparison with evolutionary isochrones. The age of the low-mass stars ranges from 1 to 7 Myr with a slight peak at 3 Myr. However, the age of the high-mass stars depends on the adopted models and is 3.5 {+-} 0.5 Myr from the non-rotating or moderately rotating models of Brott et al. as well as the non-rotating models of Ekstroem et al. But the age is 4.0-7.0 Myr if the rotating models of Ekstroem et al. are adopted. This latter age is in excellent agreement with the timescale of ejection of the high-mass runaway star HD 153919 from NGC 6231, albeit the younger age cannot be entirely excluded.
A method of testing the performance of a cluster of wind turbine units an data analysis equations are presented which together form a simple and direct procedure for determining the reduction in energy output caused by the wake of an upwind turbine. This method appears to solve the problems presented by data scatter and wind variability. Test data from the three-unit Mod-2 wind turbine cluster at Goldendale, Washington, are analyzed to illustrate the application of the proposed method. In this sample case the reduction in energy was found to be about 10 percent when the Mod-2 units were separated a distance equal to seven diameters and winds were below rated.
Orbital-optimized MP2.5 [or simply “optimized MP2.5,” OMP2.5, for short] and its analytic energy gradients are presented. The cost of the presented method is as much as that of coupled-cluster singles and doubles (CCSD) [O(N{sup 6}) scaling] for energy computations. However, for analytic gradient computations the OMP2.5 method is only half as expensive as CCSD because there is no need to solve λ{sub 2}-amplitude equations for OMP2.5. The performance of the OMP2.5 method is compared with that of the standard second-order Møller–Plesset perturbation theory (MP2), MP2.5, CCSD, and coupled-cluster singles and doubles with perturbative triples (CCSD(T)) methods for equilibrium geometries, hydrogen transfer reactions between radicals, and noncovalent interactions. For bond lengths of both closed and open-shell molecules, the OMP2.5 method improves upon MP2.5 and CCSD by 38%–43% and 31%–28%, respectively, with Dunning's cc-pCVQZ basis set. For complete basis set (CBS) predictions of hydrogen transfer reaction energies, the OMP2.5 method exhibits a substantially better performance than MP2.5, providing a mean absolute error of 1.1 kcal mol{sup −1}, which is more than 10 times lower than that of MP2.5 (11.8 kcal mol{sup −1}), and comparing to MP2 (14.6 kcal mol{sup −1}) there is a more than 12-fold reduction in errors. For noncovalent interaction energies (at CBS limits), the OMP2.5 method maintains the very good performance of MP2.5 for closed-shell systems, and for open-shell systems it significantly outperforms MP2.5 and CCSD, and approaches CCSD(T) quality. The MP2.5 errors decrease by a factor of 5 when the optimized orbitals are used for open-shell noncovalent interactions, and comparing to CCSD there is a more than 3-fold reduction in errors. Overall, the present application results indicate that the OMP2.5 method is very promising for open-shell noncovalent interactions and other chemical systems with difficult electronic structures.