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DOE OSTI2026

Extracting $B_s\to D_s^*\ellν_\ell$ form factors

Semileptonic $B_{(s)}$ decays are of great phenomenological interest because they allow to determine e.g. CKM matrix elements or test lepton flavor universality. Taking advantage of already existing lattice data, we demonstrate the analysis steps to extract the four form factors describing exclusive semileptonic $B_s\to D_s^*\ellν_\ell$ decays using the narrow width approximation. Our data are based on RBC/UKQCD's set of 2+1 flavor gauge field ensembles with Shamir domain-wall fermion and Iwasaki gauge field action featuring inverse lattice spacings of $a^{-1}=$1.785, 2.383, and 2.785 GeV as well as pion masses between 268 and 433 MeV. Light, strange and charm quarks are simulated using domain-wall fermions, whereas bottom quarks are generated with the relativistic heavy quark (RHQ) action.

Boushmelev, Anastasia [Siegen U.] (ORCID:000000033↗

NASA NTRS1996

The Determination of the Large-Scale Circulation of the Pacific Ocean from Satellite Altimetry using Model Green's Functions

A Green's function method for obtaining an estimate of the ocean circulation using both a general circulation model and altimetric data is demonstrated. The fundamental assumption is that the model is so accurate that the differences between the observations and the model-estimated fields obey a linear dynamics. In the present case, the calculations are demonstrated for model/data differences occurring on very a large scale, where the linearization hypothesis appears to be a good one. A semi-automatic linearization of the Bryan/Cox general circulation model is effected by calculating the model response to a series of isolated (in both space and time) geostrophically balanced vortices. These resulting impulse responses or 'Green's functions' then provide the kernels for a linear inverse problem. The method is first demonstrated with a set of 'twin experiments' and then with real data spanning the entire model domain and a year of TOPEX/POSEIDON observations. Our present focus is on the estimate of the time-mean and annual cycle of the model. Residuals of the inversion/assimilation are largest in the western tropical Pacific, and are believed to reflect primarily geoid error. Vertical resolution diminishes with depth with 1 year of data. The model mean is modified such that the subtropical gyre is weakened by about 1 cm/s and the center of the gyre shifted southward by about 10 deg. Corrections to the flow field at the annual cycle suggest that the dynamical response is weak except in the tropics, where the estimated seasonal cycle of the low-latitude current system is of the order of 2 cm/s. The underestimation of observed fluctuations can be related to the inversion on the coarse spatial grid, which does not permit full resolution of the tropical physics. The methodology is easily extended to higher resolution, to use of spatially correlated errors, and to other data types.

Stammer, Detlef↗

DOE OSTI2016

Inelastic dark matter with spin-dependent couplings to protons and large modulation fractions in DAMA

We discuss a scenario where the DAMA modulation effect is explained by a Weakly Interacting Massive Particle (WIMP) which upscatters inelastically to a heavier state and predominantly couples to the spin of protons. In this scenario constraints from xenon and germanium targets are evaded dynamically, due to the suppression of the WIMP coupling to neutrons, while those from fluorine targets are evaded kinematically, because the minimal WIMP incoming speed required to trigger upscatters off fluorine exceeds the maximal WIMP velocity in the Galaxy, or is very close to it. In this scenario WIMP scatterings off sodium are usually sensitive to the large-speed tail of the WIMP velocity distribution and modulated fractions of the signal close to unity arise in a natural way. On the other hand, a halo-independent analysis with more conservative assumptions about the WIMP velocity distribution allows to extend the viable parameter space to configurations where large modulated fractions are not strictly necessary. We discuss large modulated fractions in the Maxwellian case showing that they imply a departure from the usual cosine time dependence of the expected signal in DAMA. However we explicitly show that the DAMA data is not sensitive to this distortion, both in time and frequency space, even in the extreme case of a 100 % modulated fraction. Moreover the same scenario provides an explanation of the maximum in the energy spectrum of the modulation amplitude detected by DAMA in terms of WIMPs whose minimal incoming speed matches the kinematic threshold for inelastic upscatters. For the elastic case the detection of such maximum suggests an inversion of the modulation phase below the present DAMA energy threshold, while this is not expected for inelastic scattering. This may allow to discriminate between the two scenarios in a future low-threshold analysis of the DAMA data.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1986

Magnetization modeling in the north and equatorial Atlantic Ocean using MAGSAT data

Magsat 2 x 2-deg scalar anomalous-magnetic-field data (Langel et al., 1982) for the northern and equatorial Atlantic are inverted and combined with physiographic data and laboratory results on the magnetization of oceanic rocks and the oceanic crust to construct models explaining the shorter-wavelength component of the anomalies. An annihilator is applied to the inversion results to eliminate reverse-magnetized sources and facilitate comparisons of areas inverted separately, and a latitude effect on source spacing is tentatively attributed to greater noise contamination at lower latitudes. It is found that remanent magnetization combined with considerable crustal thickening can best explain the high intensity levels observed, although viscous magnetization or contamination of the data by noncrustal sources must also be considered.

Hayling, K. L.↗

DOE OSTI2016

Data-driven probability concentration and sampling on manifold

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation method for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.

15 GEOTHERMAL ENERGY↗

NASA NTRS2012

Evaluation of Deep-Space Laser Communication under Different Mission Scenarios

A number of space agencies, including NASA, are considering free-space laser communications as a means for returning higher data-rates from future space missions. In this paper, potential deep-space missions are evaluated to show that with optical communication a 10x increase relative to state-of-the art telecommunication systems could be achieved. The maximum deep-space distance where ground transmitted laser beacons could assist acquisition and tracking; and operating points where optical communication performance degrades faster than the inverse square distance are also discussed.

mission scenarios↗

DOE OSTI2020

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

History matching aims to find a numerical reservoir model that can be used to predict the reservoir performance. An engineer and model calibration (data inversion) method are required to adjust various parameters/properties of the numerical model in order to match the reservoir production history. In this study, we develop deep neural networks within the reinforcement learning framework to achieve automated history matching that will reduce engineers’ efforts, human bias, automatically and intelligently explore the parameter space, and remove the need of large set of labeled training data. To that end, a fast-marching-based reservoir simulator is encapsulated as an environment for the proposed reinforcement learning. The deep neural-network-based learning agent interacts with the reservoir simulator within reinforcement learning framework to achieve the automated history matching. Reinforcement learning techniques, such as discrete Deep Q Network and continuous Deep Deterministic Policy Gradients, are used toth, used to train the learning agents. The continuous actions enable the Deep Deterministic Policy Gradients to explore more states at each iteration in a a learning episode; consequently, a better history matching is achieved using this algorithm as compared to Deep Q Network. For simplified dual-target composite reservoir models, the best history-matching performances of the discrete and continuous learning methods in terms of normalized root mean square errors are 0.0447 and 0.0038, respectively. Furthermore, our study shows that continuous action space achieved by the deep deterministic policy gradient drastically outperforms deep Q network.

42 ENGINEERING↗

DOE OSTI2017

Loops in AdS from conformal field theory

We propose and demonstrate a new use for conformal field theory (CFT) crossing equations in the context of AdS/CFT: the computation of loop amplitudes in AdS, dual to non-planar correlators in holographic CFTs. Loops in AdS are largely unexplored, mostly due to technical difficulties in direct calculations. We revisit this problem, and the dual 1=N expansion of CFTs, in two independent ways. The first is to show how to explicitly solve the crossing equations to the first subleading order in 1=N 2 , given a leading order solution. This is done as a systematic expansion in inverse powers of the spin, to all orders. These expansions can be resummed, leading to the CFT data for nite values of the spin. Our second approach involves Mellin space. We show how the polar part of the four-point, loop-level Mellin amplitudes can be fully reconstructed from the leading-order data. The anomalous dimensions computed with both methods agree. In the case of $\phi$ 4 theory in AdS, our crossing solution reproduces a previous computation of the one-loop bubble diagram. We can go further, deriving the four-point scalar triangle diagram in AdS, which had never been computed. In the process, we show how to analytically derive anomalous dimensions from Mellin amplitudes with an in nite series of poles, and discuss applications to more complicated cases such as the N = 4 super-Yang-Mills theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Small-scale characterization of vine plant root water uptake via 3-D electrical resistivity tomography and mise-à-la-masse method

The investigation of plant roots is inherently difficult and often neglected.Being out of sight, roots are often out of mind. Nevertheless, roots play a key rolein the exchange of mass and energy between soil and the atmosphere, in addition tothe many practical applications in agriculture. In this paper, we propose amethod for roots imaging based on the joint use of two electricalnoninvasive methods: electrical resistivity tomography (ERT) andmise-à-la-masse (MALM). The approach is based on the key assumption that theplant root system acts as an electrically conductive body, so that injectingelectrical current into the plant stem will ultimately result in the injectionof current into the subsoil through the root system, and particularly throughthe root terminations via hair roots. Evidence from field data, showing thatvoltage distribution is very different whether current is injected into thetree stem or in the ground, strongly supports this hypothesis. The proposedprocedure involves a stepwise inversion of both ERT and MALM data thatultimately leads to the identification of electrical resistivity (ER)distribution and of the current injection root distribution in thethree-dimensional soil space. This, in turn, is a proxy to the active (hair)root density in the ground. We tested the proposed procedure on syntheticdata and, more importantly, on field data collected in a vineyard, where theestimated depth of the root zone proved to be in agreement with literature onsimilar crops. The proposed noninvasive approach is a step forward towards abetter quantification of root structure and functioning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Material Properties for Brady Hot Springs Nevada USA from PoroTomo Project

The PoroTomo team has completed inverse modeling of the three data sets (seismology, geodesy, and hydrology) individually, as described previously. The estimated values of the material properties are registered on a three-dimensional grid with a spacing of 25 meters between nodes. The material properties are listed an Excel file. Figures show planar slices in three sets: horizontal slices in a planes normal to the vertical Z axis (Z normal), vertical slices in planes perpendicular to the dominant strike of the fault system (X normal), and vertical slices in planes parallel to the dominant strike of the fault system (Y normal). The results agree on the following points. The material is unconsolidated and/or fractured, especially in the shallow layers. The structural trends follow the fault system in strike and dip. The geodetic measurements favor the hypothesis of thermal contraction. Temporal changes in pressure, subsidence rate, and seismic amplitude are associated with changes in pumping rates during the four stages of the deployment in 2016. The modeled hydraulic conductivity is high in fault damage zones. All the observations are consistent with the conceptual model: highly permeable conduits along faults channel fluids from shallow aquifers to the deep geothermal reservoir tapped by the production wells.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning

Abstract One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative. The sequential nature of these iterations forces a long design cycle time, ranging from several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework, namely PMI (PMI), to carry out an explicit inverse design. PMI calculates the design explicitly without costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this work the framework will be demonstrated on inverse aerodynamic design of three-dimensional turbine blades.

Engineering↗

DOE OSTI2020

Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product Report

Objective analysis (OA) is a method of mapping unstructured data to a structured grid. In the context of scanning radar data, OA is used to interpolate data in antenna coordinates (range, azimuth, and elevation) onto a regularly spaced Cartesian grid (Trapp and Doswell 2000). The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product (VAP) uses the Python ARM Radar Toolkit (Py-ART), a data model-driven interactive architecture for working with weather radar data, to map the data to a Cartesian grid (Helmus and Collis 2016). MMCG, with Py-ART built in, has the ability to take radar data in antenna coordinates and map the gates to a Cartesian grid using inverse distance weight functions such as Cressman (square) and Barnes (exponential), but also can filter the data during the interpolation. MMCG also allows arbitrary formulations for the radius of influence, which are matched to particular radar scanning strategies. This creates a complex parameter space for optimizing the retention of storm structure detail while minimizing artifacts. MMCG takes data processed with ARM’s Corrected Precipitation Radar Moments in Antenna Coordinates (CMAC) VAP and maps it to a Cartesian grid as the output product. A variety of fields that have been mapped to the Cartesian grid are then saved to plots to complement each grid file.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Regularization and Error Characterization of GRACE Mascons

We present a new global time-variable gravity mascon solution derived from Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The new product from the NASA Goddard Space Flight Center (GSFC) results from a novel approach that combines an iterative solution strategy with geographical binning of inter-satellite range-acceleration residuals in the construction of time-dependent regularization matrices applied in the inversion of mascon parameters. This estimation strategy is intentionally conservative as it seeks to maximize the role of the GRACE measurements on the final solution while minimizing the influence of the regularization design process. We fully reprocess the Level 1B data in the presence of the final mascon solution to generate true post-fit inter-satellite residuals, which are utilized to confirm solution convergence and to validate the mascon noise uncertainties. We also present the mathematical case that regularized mascon solutions are biased, and that this bias, or leakage, must be combined with the estimated noise variance to accurately assess total mascon uncertainties. The estimated leakage errors are determined from the monthly resolution operators. We present a simple approach to compute the total uncertainty for both individual mascon and regional analysis of the GSFC mascon product, and validate the results in comparison with independent mascon solutions and calibrated Stokes uncertainties. Lastly, we present the new solution and uncertainties with global analyses of the mass trends and annual amplitudes, and compute updated trends for the global ocean, and the respective contributions of the Greenland Ice Sheet, Antarctic Ice Sheet, Gulf of Alaska, and terrestrial water storage. This analysis highlights the successful closure of the global mean sea level budget, that is, the sum of global ocean mass from the GSFC mascons and the steric component from Argo floats agrees well with the total determined from sea surface altimetry.

B. D. Loomis↗

NASA NTRS2019

Regularization and Error Characterization of GRACE Mascons

Loomis, B. D.↗

DOE OSTI2020

Imaging of a fluid injection process using geophysical data — A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts.

58 GEOSCIENCES↗

DOE OSTI2018

SFDBSI_GLS v. 1.0

Matlab code for inversion of frequency domain, electrostatic geophysical data in terms of scalar scattering amplitudes in the subsurface. The data is assumed to be the difference between two measurements: electric field measurements prior to the injection of an electrically conductive proppant, and the electric field measurements after proppant injection. The proppant is injected into the subsurface via a well, and its purpose is to prop open fractures created by hydraulic fracturing. In both cases the illuminating electric field is assumed to be a vertically incident plane wave. The inversion strategy is to solve a set of linear system of equations, where each equation defines the amplitude of a candidate scattering volume. The model space is defined by M potential scattering locations and the frequency domain (of which there are k frequencies) data are recorded on N receivers. The solution thus solves a kN x M system of linear equations for M scalar amplitudes within the user-defined solution space. Practical Application: Oilfield environments where observed electrostatic geophysical data can reasonably be assumed to be scattered by subsurface proppant volumes. No field validation examples have so far been provided.

Poppeliers, Christian↗

DOE OSTI2022

Volumetric Rendering on Wavelet-Based Adaptive Grid

Numerical modeling of physical phenomena frequently involves processes across a wide range of spatial and temporal scales. In the last two decades, the advancements in wavelet-based numerical methodologies to solve partial differential equations, combined with the unique properties of wavelet analysis to resolve localized structures of the solution on dynamically adaptive computational meshes, make it feasible to perform large-scale numerical simulations of a variety of physical systems on a dynamically adaptive computational mesh that changes both in space and time. Volumetric visualization of the solution is an essential part of scientific computing, yet the existing volumetric visualization techniques do not take full advantage of multi-resolution wavelet analysis and are not fully tailored for visualization of a compressed solution on the wavelet-based adaptive computational mesh. Our objective is to explore the alternatives for the visualization of time-dependent data on space-time varying adaptive mesh using volume rendering while capitalizing on the available sparse data representation. Two alternative formulations are explored. The first one is based on volumetric ray casting of multi-scale datasets in wavelet space. Rather than working with the wavelets at the finest possible resolution, a partial inverse wavelet transform is performed as a preprocessing step to obtain scaling functions on a uniform grid at a user-prescribed resolution. As a result, a solution in physical space is represented by a superposition of scaling functions on a coarse regular grid and wavelets on an adaptive mesh. An efficient and accurate ray casting algorithm is based just on these coarse scaling functions. Additional details are added during the ray tracing by taking an appropriate number of wavelets into account based on support overlap with the interpolation point, wavelet coefficient magnitude, and other characteristics, such as opacity accumulation (front to back ordering) and deviation from frontal viewing direction. The second approach is based on complementing of wavelet-based adaptive mesh to the traditional Adaptive Mesh Refinement (AMR) mesh. Both algorithms are illustrated and compared to the existing volume visualization software for Rayleigh-Benard thermal convection and electron density data sets in terms of rendering time and visual quality for different data compression of both wavelet-based and AMR adaptive meshes.

Vezolainen, Alexei V.↗