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DOE OSTI2014

On the estimation of statistical uncertainties on powder diffraction and small-angle scattering data from two-dimensional X-ray detectors

Optimal methods are explored for obtaining one-dimensional powder pattern intensities from two-dimensional planar detectors with good estimates of their standard deviations. Methods are described to estimate uncertainties when the same image is measured in multiple frames as well as from a single frame. The importance of considering the correlation of diffraction points during the integration and the resampling process of data analysis is shown. It is found that correlations between adjacent pixels in the image can lead to seriously overestimated uncertainties if such correlations are neglected in the integration process. Off-diagonal entries in the variance–covariance (VC) matrix are problematic as virtually all data processing and modeling programs cannot handle the full VC matrix. It is shown that the off-diagonal terms come mainly from the pixel-splitting algorithm used as the default integration algorithm in many popular two-dimensional integration programs, as well as from rebinning and resampling steps later in the processing. When the full VC matrix can be propagated during the data reduction, it is possible to get accurate refined parameters and their uncertainties at the cost of increasing computational complexity. However, as this is not normally possible, the best approximate methods for data processing in order to estimate uncertainties on refined parameters with the greatest accuracy from just the diagonal variance terms in the VC matrix is explored.

Yang, X.↗

DOE OSTI2021

A catalogue of white dwarfs in Gaia EDR3

ABSTRACT We present a catalogue of white dwarf candidates selected from Gaia Early Data Release 3 (EDR3). We applied several selection criteria in absolute magnitude, colour, and Gaia quality flags to remove objects with unreliable measurements while preserving most stars compatible with the white dwarf locus in the Hertzsprung–Russell diagram. We then used a sample of over 30 000 spectroscopically confirmed white dwarfs and contaminants from the Sloan Digital Sky Survey (SDSS) to map the distribution of these objects in the Gaia absolute magnitude–colour space. Finally, we adopt the same method presented in our previous work on Gaia Data Release 2 (DR2) to calculate a probability of being a white dwarf (PWD) for ≃1.3 million sources that passed our quality selection. The PWD values can be used to select a sample of ${\simeq} 359\,000$ high-confidence white dwarf candidates. We calculated stellar parameters (effective temperature, surface gravity, and mass) for all these stars by fitting Gaia astrometry and photometry with synthetic pure-H, pure-He, and mixed H–He atmospheric models. We estimate an upper limit of 93 per cent for the overall completeness of our catalogue for white dwarfs with G ≤ 20 mag and effective temperature (Teff) > 7000 K, at high Galactic latitudes (|b| > 20°). Alongside the main catalogue we include a reduced proper motion extension containing ${\simeq} 10\,200$ white dwarf candidates with unreliable parallax measurements that could, however, be identified on the basis of their proper motion. We also performed a cross-match of our catalogues with SDSS Data Release 16 (DR16) spectroscopy and provide spectral classification based on visual inspection for all resulting matches.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks, Frontiers in Earth Science: Dataset

This data package contains watershed modeling inputs and outputs as well as deep neural networks training and testing results used in "Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks" (Cromwell et al., 2021). We train various deep neural network models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models. For detailed information regarding watershed model description and DNNs setup, please refer to Cromwell et al., 2021. The deep_learning.zip file contains the inputs and outputs for the different deep neural networks. The watershed_model.zip file contains the inputs and outputs for watershed simulation using ATS. The figures.zip file contains raw figures and their corresponding scripts. For a more detailed description, please see the README.md file within each compressed file.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Alleviating prior dependencies for DESI DR1 clustering fits through reparameterization

Bayesian analyses of the full-shape clustering of Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) exhibit prior-volume projection effects, whereby weakly constrained nuisance parameters of the Effective Field Theory of Large Scale Structure (EFTofLSS) shift marginalized cosmological posteriors away from the posterior maximum. We reanalyze DESI DR1 power spectrum multipoles using two complementary mitigation strategies: (i) nonlinear orthogonalization to decorrelate nuisance and cosmological parameter priors, and (ii) a fully reparameterization-invariant Jeffreys prior over all EFTofLSS coefficients, evaluated on-the-fly via closed-form Jacobians. Including data from DESI, Big-Bang Nuclesynthesis and a constraint on $n_{\mathrm{s}}$, baseline priors lead to multi-$σ$ projection in the Hubble parameter $H_{0}$ and dark energy equation of state parameters $w_{0}$ and $w_{a}$; the Jeffreys prior successfully recenters these posteriors to enclose the maximum a posteriori estimate within the 68% credible regions, demonstrating clear mitigation of projection effects for these late-time expansion parameters. A hybrid Jeffreys+baseline-Gaussian configuration controls residual over-broad tails in the physical cold dark matter density $ω_{\mathrm{c}}$ while preserving the volume correction, and is our favoured approach. We compare the credible intervals derived using our methodology to those obtained using Halo Occupation Distribution (HOD)-informed priors and to confidence intervals derived using frequentist profile likelihood analyses, finding agreement in both central values and degeneracy directions in the $w_{0}$--$w_{a}$ plane. This demonstrates that, once projection effects are properly controlled, we can make robust inferences about the late-time cosmological expansion independent of the statistical framework adopted.

Bonici, M. [Waterloo U.; Perimeter Inst. Theor. Ph↗

DOE OSTI2016

Determination of interstellar O parameters using the first two years of data from the Interstellar Boundary Explorer

The direct measurements of interstellar matter by the Interstellar Boundary Explorer (IBEX) mission have opened a new and important chapter in our study of the interactions that control the boundaries of our heliosphere. Here we derive for the quantitative information about interstellar O flow parameters from IBEX low-energy neutral atom data for the first time. Specifically, we derive a relatively narrow four-dimensional parameter tube along which interstellar O flow parameters must lie. Along the parameter tube, we find a large uncertainty in interstellar O flow longitude, 76.°0 ± 3.°4 from χ {sup 2} analysis and 76.°5 ± 6.°2 from a maximum likelihood fit, which is statistically consistent with the flow longitude derived for interstellar He, 75.°6 ± 1.°4. The best-fit O and He temperatures are almost identical at a reference flow longitude of 76°, which provides a strong indication that the local interstellar plasma near the Sun is relatively unaffected by turbulent heating. However, key differences include an oxygen parameter tube for the interstellar speed (relation between speed and longitude) that has higher speeds than those in the corresponding parameter tube for He, and an upstream flow latitude for oxygen that is southward of the upstream flow latitude for helium. Both of these differences are likely the result of enhanced filtration of interstellar oxygen due to its charge-exchange ionization rate, which is higher than that for helium. Furthermore, we derive an interstellar O density near the termination shock of 5.8{sub −0.8}{sup +0.9}×10{sup −5} cm{sup −3} that, within uncertainties, is consistent with previous estimates. Thus, we use IBEX data to probe the interstellar properties of oxygen.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

SU-E-T-08: A Beam Source Model for Monte Carlo Simulations of a Double-Scattering Proton Beam Delivery System Using Beam Current Modulation

Purpose: To reconstruct phase-space information upstream of patient specific collimators for Monte Carlo simulations using only radiotherapy planning system data. Methods: The proton energies are calculated based on residual ranges, e.g., sum of prescribed ranges in a patient and SSD. The Kapchinskij and Vladimirskij (KV) distribution was applied to sample proton’s x-y positions and momentum direction and the beam shape was assumed to be a circle. Free parameters, e.g., the initial energy spread and the emittance of KV distribution were estimated from the benchmarking with commissioning data in a commercial treatment planning system for an operational proton therapy center. The number of histories, which defines the height of individual pristine Bragg peaks (BP) of Spread-out Bragg peak (SOBP), are weighted based on beam current modulation and a correction factor is applied to take into account the fluence reduction as the residual range decreases due to the rotation of the range modulator wheel. The timedependent behaviors, e.g., the changes of the residual range and histories per a pristine BP, are realized by utilizing TOPAS (Tool for Particle Simulation). Results: Benchmarking simulations for selected SOBPs ranging 7.5 cm to 15.5 cm matched within 2 mm in range and up to 5 mm in SOBP width against measurement data in water phantom. We found this model tends to underestimate entrance dose by about 5 % in comparison to measurement. This was attributed to the situation that the energy distribution used in the model was limited in its granularity at the limit of single energy spectrum for the narrow angle modulator steps used in the proximal pull back region of the SOBPs. Conclusion: Within these limitations the source modeling method proved itself an acceptable alternative of a full treatment head simulation when the machine geometry and materials information are not available.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Probing homogeneity with standard candles

We show that standard candles can provide some valuable information about the density contrast, which could be particularly important at redshifts where other observations are not available. We use an inversion method to reconstruct the local radial density profile from luminosity distance observations assuming background cosmological parameters obtained from large scale observations. Using type Ia Supernovae, Cepheids and the cosmological parameters from the Planck mission we reconstruct the radial density profiles along two different directions of the sky. We compare these profiles to other density maps obtained from luminosity density, in particular Keenan et al. 2013 and the 2M++ galaxy catalogue. The method independently confirms the existence of inhomogeneities, could be particularly useful to correctly normalize density maps from galaxy surveys with respect to the average density of the Universe, and could clarify the apparent discrepancy between local and large scale estimations of the Hubble constant. When better observational supernovae data will be available, the accuracy of the reconstructed density profiles will improve and will allow to further investigate the existence of structures whose size is beyond the reach of galaxy surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Deep SOAR follow-up photometry of two Milky Way outer-halo companions discovered with Dark Energy Survey

We report the discovery of a new star cluster, DES 3, in the constellation of Indus, and deeper observations of the previously identified satellite DES J0222.7−5217 (Eridanus III). DES 3 was detected as a stellar overdensity in first-year Dark Energy Survey data, and confirmed with deeper photometry from the 4.1-m Southern Astrophysical Research (SOAR) telescope. The new system was detected with a relatively high significance and appears in the DES images as a compact concentration of faint blue point sources. We determine that DES 3 is located at a heliocentric distance of ≃76.2 kpc and it is dominated by an old (≃9.8 Gyr) and metal-poor ([Fe/H] ≃ −1.84) population. While the age and metallicity values of DES 3 are comparable to typical globular clusters (objects with a high stellar density, stellar mass of ∼10^5 M_⊙ and luminosity ∼ −7.3), its half-light radius (r_h ∼ 6.87 pc) and luminosity (M_V ∼ −1.7) are more indicative of faint star cluster. Based on the angular size, DES 3, with a value of r_h ∼ 0$${^{\prime}_{.}}$$31, is among the smallest faint star clusters known to date. Furthermore, using deeper imaging of DES J0222.7−5217 taken with the SOAR telescope, we update structural parameters and perform the first isochrone modelling. Our analysis yields the first age (≃12.6 Gyr) and metallicity ([Fe/H] ≃ −2.01) estimates for this object. The half-light radius (r_h ≃ 11.24 pc) and luminosity (M_V ≃ −2.4) of DES J0222.7−5217 suggest that it is likely a faint star cluster. The discovery of DES 3 indicates that the census of stellar systems in the Milky Way is still far from complete, and demonstrates the power of modern wide-field imaging surveys to improve our knowledge of the Galaxy’s satellite population.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Estimating active layer thickness and volumetric water content from ground penetrating radar measurements in Barrow, Alaska

Ground penetrating radar (GPR) has emerged as an effective tool for estimating active layer thickness (ALT) and volumetric water content (VWC) within the active layer. In August 2013, we conducted a series of GPR and probing surveys using a 500 MHz antenna and metallic probe around Barrow, Alaska. Here, we collected about 15 km of GPR data and 1.5 km of probing data. We describe the GPR data processing workflow from raw GPR data to the estimated ALT and VWC. We then include the corresponding uncertainties for each measured and estimated parameter. The estimated average GPR-derived ALT was 41 cm, with a standard deviation of 9 cm. The average probed ALT was 40 cm, with a standard deviation of 12 cm. The average GPR-derived VWC was 0.65, with a standard deviation of 0.14.

58 GEOSCIENCES↗

DOE OSTI2018

Scale Mixtures of Frechet Distributions as Asymptotic Approximations of Extreme Precipitation

This paper is a further development of the results of [20] where, based on the negative binomial model for the duration of wet periods measured in days [16], an asymptotic approximation was proposed for the distribution of the maximum daily precipitation volume within a wet period. This approximation has the form of a scale mixture of the Fr´echet distribution with the gamma mixing distribution and coincides with the distribution of a positive power of a random variable having the Snedecor–Fisher distribution. Here we extend this result to the mth extremes, m ∈ ℕ, and sample quantiles. The proof of this result is based on the representation of the negative binomial distribution as a mixed geometric (and hence, mixed Poisson) distribution [17] and limit theorems for extreme order statistics in samples with random sizes having mixed Poisson distributions [10]. Some analytic properties of the obtained limit distribution are described. In particular, it is demonstrated that under certain conditions the limit distribution of the maximum precipitation is mixed exponential and hence, is infinitely divisible. It is shown that under the same conditions this limit distribution can be represented as a scale mixture of stable or Weibull or Pareto or folded normal laws. The corresponding product representations for the limit random variable can be used for its computer simulation. The results of fitting this distribution to real data are presented illustrating high adequacy of the proposed model. It is also shown that the limit distribution of sample quantiles is the well-known Student distribution. Several methods are proposed for the estimation of the parameters of the asymptotic distributions. The obtained mixture representations for the limit laws and the corresponding asymptotic approximations provide better insight into the nature of mixed probability (“Bayesian”) models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Refining instrument response functions with 3-D Monte Carlo simulations of differential hard x-ray spectrometers

Three x-ray spectrometers – the hard x-ray detector (HXRD), the bremsstrahlung MeV x-ray spectrometer (BMXS), and the hard x-ray image plate (HXIP) – areused here to estimate hot-electron bremsstrahlung spectrum parameters for inertial confinement fusion experiments on OMEGA. Using 1-D deterministically calculated response functions, they show significantly different estimates of spectrum slope temperature. For all three detectors, 3-D Geant4 Monte Carlo simulations are developed and used to compute instrument response functions. Simulations explain features in raw image-plate data, but only significantly improve systematic errors in predicted detector channel signals for BMXS and do not entirely eliminate the discrepancy between estimates of slope temperature.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2016

DESAlert: Enabling Real-Time Transient Follow-Up with Dark Energy Survey Data

Here, the Dark Energy Survey is undertaking an observational programme imaging 1/4 of the southern hemisphere sky with unprecedented photometric accuracy. In the process of observing millions of faint stars and galaxies to constrain the parameters of the dark energy equation of state, the Dark Energy Survey will obtain pre-discovery images of the regions surrounding an estimated 100 gamma-ray bursts over 5 yr. Once gamma-ray bursts are detected by, e.g., the Swift satellite, the DES data will be extremely useful for follow-up observations by the transient astronomy community. We describe a recently-commissioned suite of software that listens continuously for automated notices of gamma-ray burst activity, collates information from archival DES data, and disseminates relevant data products back to the community in near-real-time. Of particular importance are the opportunities that non-public DES data provide for relative photometry of the optical counterparts of gamma-ray bursts, as well as for identifying key characteristics (e.g., photometric redshifts) of potential gamma-ray burst host galaxies. We provide the functional details of the DESAlert software, and its data products, and we show sample results from the application of DESAlert to numerous previously detected gamma-ray bursts, including the possible identification of several heretofore unknown gamma-ray burst hosts.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Probabilistic Assessment of Above Zone Pressure Predictions at a Geologic Carbon Storage Site

Carbon dioxide (CO2) storage into geological formations is regarded as an important mitigation strategy for anthropogenic CO2 emissions to the atmosphere. This study first simulates the leakage of CO2 and brine from a storage reservoir through the caprock. Then, we estimate the resulting pressure changes at the zone overlying the caprock also known as Above Zone Monitoring Interval (AZMI). A data-driven approach of arbitrary Polynomial Chaos (aPC) Expansion is then used to quantify the uncertainty in the above zone pressure prediction based on the uncertainties in different geologic parameters. Finally, a global sensitivity analysis is performed with Sobol indices based on the aPC technique to determine the relative importance of different parameters on pressure prediction. The results indicate that there can be uncertainty in pressure prediction locally around the leakage zones. The degree of such uncertainty in prediction depends on the quality of site specific information available for analysis. The scientific results from this study provide substantial insight that there is a need for site-specific data for efficient predictions of risks associated with storage activities. The presented approach can provide a basis of optimized pressure based monitoring network design at carbon storage sites.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Sequential data assimilation for single-molecule FRET photon-counting data

Data assimilation is a statistical method designed to improve the quality of numerical simulations in combination with real observations. Here, we develop a sequential data assimilation method that incorporates one-dimensional time-series data of smFRET (single-molecule Förster resonance energy transfer) photon-counting into conformational ensembles of biomolecules derived from “replicated” molecular dynamics (MD) simulations. A particle filter using a large number of “replicated” MD simulations with a likelihood function for smFRET photon-counting data is employed to screen the conformational ensembles that match the experimental data. We examine the performance of the method using emulated smFRET data and coarse-grained (CG) MD simulations of a dye-labeled polyproline-20. The method estimates the dynamics of the end-to-end distance from smFRET data as well as revealing that of latent conformational variables. The particle filter is also able to correct model parameter dependence in CG MD simulations. We discuss the applicability of the method to real experimental data for conformational dynamics of biomolecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations

Ice Ih, the common form of ice in the biosphere, contains proton disorder. Its proton-ordered counterpart, ice XI, is thermodynamically stable below 72 K. However, the formation of ice XI is kinetically hindered, and experimentally it is obtained by doping with KOH. Doping creates ionic defects that promote the migration of protons and the associated change in proton configuration. In this article, we mimic the effect of doping with a bias potential that enhances the formation of ionic defects in molecular dynamics simulations. The recombination of the ions thus formed proceeds through fast migration of the hydroxide along hydrogen bond loops, providing a physical and expedite way to change the proton configuration. Here, a key ingredient of this approach is a machine learning potential trained with density functional theory data and capable of modelling molecular dissociation. We exemplify the usefulness of this idea by studying the order-disorder transition using an appropriate order parameter that distinguishes the proton environments in ice Ih and XI. We calculate the changes in free energy, enthalpy, and entropy associated with the transition. Our estimated entropy agrees with experiment within the error bars of the calculation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Year six photometric measurements of known Trans-Neptunian Objects and Centaurs by the Dark Energy Survey

ABSTRACT We identified known Trans-Neptunian Objects (TNOs) and Centaurs in the complete Dark Energy Survey (DES) year six catalogue (DES Y6) through the Sky Body Tracker (SkyBoT) tool. We classified our data set of 144 objects into a widely used 4-class taxonomic system of TNOs. No such previous classification was available in the literature for most of these objects. From absolute magnitudes and average albedos, an estimation of the diameters of all these objects is obtained. Correlations involving colours, orbital parameters, dynamical classes, and sizes are also discussed. In particular, our largest reddest object has a diameter of $390^{+68}_{-53}$ km and our largest cold classical, $255^{+19}_{-17}$ km. Also, a weak correlation between colour and inclination is found within the population of resonant TNOs in addition to weak correlations between colour and phase slope in different bands.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Quantum spin ice response to a magnetic field in the dipole-octupole pyrochlore Ce 2 Zr 2 O 7

The pyrochlore magnet Ce 2 Zr 2 O 7 has attracted much attention as a quantum spin ice candidate whose novelty derives in part from the dipolar-octupolar nature of the Ce 3+ pseudospin-1/2 degrees of freedom it possesses. We report heat capacity measurements on single crystal samples of Ce 2 Zr 2 O 7 down to T~0.1K in a magnetic field along the [$1,\bar{1},0$] direction. These measurements show that the broad hump in the zero-field heat capacity moves higher in temperature with increasing field strength and is split into two separate humps by the [$1,\bar{1},0$] magnetic field at ~2T. These separate features are due to the decomposition of the pyrochlore lattice into effectively decoupled chains for fields in this direction: One set of chains (α chains) is polarized by the field while the other (β chains) remains free. This situation is similar to that observed in the classical spin ices Ho 2 Ti 2 O 7 and Dy 2 Ti 2 O 7 , but with the twist that here the strong transverse exchange interactions produce substantial quantum effects. Our theoretical modeling suggests that the β chains are close to a critical state, with nearly-gapless excitations. We also report elastic and inelastic neutron scattering measurements on single crystal Ce 2 Zr 2 O 7 in [$1,\bar{1},0$] and [0,0,1] magnetic fields at temperatures down to T=0.03K. The elastic scattering behaves consistently with the formation of independent chains for a [$1,\bar{1},0$] field, while the [0,0,1] field produces a single field-induced elastic magnetic Bragg peak at (0,2,0) and equivalent wavevectors, indicating a polarized spin ice state for fields above ~3T. For both [$1,\bar{1},0$] and [0,0,1] magnetic fields, our inelastic neutron scattering results show an approximately dispersionless continuum of scattering that increases in both energy and intensity with increasing field strength. By modeling the complete set of experimental data using numerical linked cluster and semiclassical molecular dynamics calculations, we demonstrate the dominantly multipolar nature of the exchange interactions in Ce 2 Zr 2 O 7 and the smallness of the parameter θ, which controls the mixing between dipolar and octupolar degrees of freedom. Finally, these results support previous estimates of the microscopic exchange parameters and place strong constraints on the theoretical description of this prominent spin ice candidate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertainty and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.

17 WIND ENERGY↗