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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Bayesian Inference in Satellite Gravity Inversion

To solve a geophysical inverse problem means applying measurements to determine the parameters of the selected model. The inverse problem is formulated as the Bayesian inference. The Gaussian probability density functions are applied in the Bayes's equation. The CHAMP satellite gravity data are determined at the altitude of 400 kilometer altitude over the South part of the Pannonian basin. The model of interpretation is the right vertical cylinder. The parameters of the model are obtained from the minimum problem solved by the Simplex method.

Kis, K. I.↗
NASA NTRSDate not supplied

Latest Results from NASA Ames’ COSmIC and Optical Constants Facility (OCF): Determining Optical Constants for Titan, Pluto and Protoplanetary Disks Applications

The NASA Ames COsmic SImulation Chamber (COSmIC) is a unique experimental facility that can be used, among many applications, to produce solid particles from gas phase molecular precursors at low temperature (150 K) using a plasma discharge to induce the chemistry in the stream of a free jet expansion. The choice of the initial gas mixture used to produce the solid sample allows the simulation of either cold planetary atmospheres like Titan or Pluto (with N2/CH4-based initial mixtures), or circumstellar environments (with Ar/CxHy-based initial mixtures). The Ames Optical Constants Facility (OCF) allows the determination of optical constants covering a broad wavelength range with high spectral resolution for solid materials, analogs of organic refractory materials formed in planetary and astrophysical environments. The core of the OCF is a Fourier transform infrared (FTIR) spectrometer that allows the continuous characterization of solid samples in the visible to far-infrared (FIR) range (0.59–200 μm, 16,950–50 cm−1). Modeling of the laboratory measurements conducted with the OCF allows the determination of accurate optical constants, n and k, over the full vis-FIR range. Here we present the latest results of two studies that combined (1) experiments performed with COSmIC to produce analogs of aerosols forming in Titan’s atmosphere and analogs of cosmic grains forming in circumstellar envelops, and (2) the characterization of these analogs with the OCF to provide the real and imaginary parts of their refractive indices, n + ik, to the community, from the visible to the FIR. These optical constants can be used as critical input parameters in radiative transfer, atmospheric and reflectance models to interpret observational data of, e.g., Titan’s atmosphere and protoplanetary disks. Providing optical constants for various materials of different compositions allows to explore a broad range of composition by simulating mixtures of materials. We also present a new project to produce analogs of Pluto’s atmospheric aerosols with COSmIC and determine their optical constants with OCF, to be used in reflectance spectra models for the interpretation of New Horizons observations of Lowell Regio, Sputnik Planitia and Cthulhu.

COsmic SImulation Chamber (COSmIC)↗
DOE OSTI2026

Role of Neutrals Versus Transport in Determining the Pedestal Density Structure: Final Technical Report

In fusion devices the plasma density plays a crucial role in determining the fusion reaction rate and has a direct impact on the fusion gain of a given device. This density is in general regulated by the particle sources and transport near the plasma edge, which give rise to an edge density pedestal. When predicting the performance of future devices, this density pedestal is often prescribed, rather than predicted, due to a lack of models which allow confident extrapolation. This project aims to advance these models through the focused validation of theoretical models related to the transport of fueling neutral particles, and through interpretive transport modeling in present day fusion plasmas, in which the penetration of neutrals is altered to better simulate future reactor-like conditions. Achievements in theory and model validation under this project have advanced our understanding of how much of the edge density profile is set by transport versus direct ionization, enabling interesting projections to future burning plasma devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

Event-driven simulation in SELMON: An overview of EDSE

EDSE (event-driven simulation engine), a model-based event-driven simulator implemented for SELMON, a tool for sensor selection and anomaly detection in real-time monitoring is described. The simulator is used in conjunction with a causal model to predict future behavior of the model from observed data. The behavior of the causal model is interpreted as equivalent to the behavior of the physical system being modeled. An overview of the functionality of the simulator and the model-based event-driven simulation paradigm on which it is based is provided. Included are high-level descriptions of the following key properties: event consumption and event creation, iterative simulation, synchronization and filtering of monitoring data from the physical system. Finally, how EDSE stands with respect to the relevant open issues of discrete-event and model-based simulation is discussed.

Rouquette, Nicolas F.↗
DOE OSTI2022

Understanding the Drivers of Atlantic Multidecadal Variability using a Stochastic Model Hierarchy

The relative importance of ocean and atmospheric dynamics in generating Atlantic Multidecadal Variability (AMV) remains an open question. Comparisons between climate models with SLAB and fully-dynamic (FULL) ocean components are often used to explore this question, but cannot reveal how individual ocean processes generate these differences. We build a hierarchy of physically interpretable stochastic models to investigate the contribution of two upper-ocean processes to AMV: the role of seasonal variation and mixed-layer entrainment. This interpretability arises from the stochastic model’s simplified representation of sea surface temperature (SST), considering only the local upper ocean response to white-noise atmospheric forcing and its impact on surface heat exchange. We focus on understanding differences between SLAB and FULL non-eddy resolving pre-industrial control simulations of the Community Earth System Model 1 (CESM), and estimate the stochastic model parameters from each respective simulation. Despite its simplicity, the stochastic model reproduces temporal characteristics of SST variability in the SPG, including reemergence, seasonal-to-interannual persistence and power spectra. Furthermore, unrealistically persistent SST of the CESM-SLAB ocean simulation is reproduced in the equivalent stochastic model configuration where the mixed-layer depth (MLD) is constant. The stochastic model also reveals that vertical entrainment primarily damps SST variability, thus explaining why SLAB exhibits larger SST variance than FULL. Here, the stochastic model driven by temporally stochastic, spatially coherent forcing patterns reproduces the canonical AMV pattern. However, the amplitude of low-frequency variability remains underestimated, suggesting a role for ocean dynamics beyond entrainment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Crystallization↗
NASA NTRS1984

Finite-difference fluid dynamics computer mathematical models for the design and interpretation of experiments for space flight

Numerical methods are used to design a spherical baroclinic flow model experiment of the large scale atmosphere flow for Spacelab. The dielectric simulation of radial gravity is only dominant in a low gravity environment. Computer codes are developed to study the processes at work in crystal growing systems which are also candidates for space flight. Crystalline materials rarely achieve their potential properties because of imperfections and component concentration variations. Thermosolutal convection in the liquid melt can be the cause of these imperfections. Such convection is suppressed in a low gravity environment. Two and three dimensional finite difference codes are being used for this work. Nonuniform meshes and implicit iterative methods are used. The iterative method for steady solutions is based on time stepping but has the options of different time steps for velocity and temperature and of a time step varying smoothly with position according to specified powers of the mesh spacings. This allows for more rapid convergence. The code being developed for the crystal growth studies allows for growth of the crystal as the solid-liquid interface. The moving interface is followed using finite differences; shape variations are permitted. For convenience in applying finite differences in the solid and liquid, a time dependent coordinate transformation is used to make this interface a coordinate surface.

Roberts, G. O.↗
DOE OSTI2025

Explainable Machine Learning for Functional Data

Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).

Elastic Shape Analysis↗
DOE OSTI2023

Searches for new phenomena in events with two leptons, jets, and missing transverse momentum in 139 fb –1 of √s = 13 TeV $pp$ collisions with the ATLAS detector

Searches for new phenomena inspired by supersymmetry in final states containing an e + e – or μ + μ – pair, jets, and missing transverse momentum are presented. These searches make use of proton–proton collision data with an integrated luminosity of 139 fb –1 , collected during 2015–2018 at a centre-of-mass energy √s = 13 TeV by the ATLAS detector at the Large Hadron Collider. Two searches target the pair production of charginos and neutralinos. One uses the recursive-jigsaw reconstruction technique to follow up on excesses observed in 36.1 fb –1 of data, and the other uses conventional event variables. The third search targets pair production of coloured supersymmetric particles (squarks or gluinos) decaying through the next-to-lightest neutralino ($\tilde{χ}$$^{0}_{2}$) via a slepton ($\tilde{ℓ}$) or Z boson into ℓ + ℓ – $\tilde{χ}$$^{0}_{1}$ , resulting in a kinematic endpoint or peak in the dilepton invariant mass spectrum. The data are found to be consistent with the Standard Model expectations. Results are interpreted using simplified models and exclude masses up to 900 GeV for electroweakinos, 1550 GeV for squarks, and 2250 GeV for gluinos.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Programmatic Advantages of Linear Equivalent Seismic Models

Underground explosions nonlinearly deform the surrounding earth material and can interact with the free surface to produce spall. However, at typical seismological observation distances the seismic wavefield can be accurately modeled using linear approximations. Although nonlinear algorithms can accurately simulate very near field ground motions, they are computationally expensive and potentially unnecessary for far field wave simulations. Conversely, linearized seismic wave propagation codes are orders of magnitude faster computationally and can accurately simulate the wavefield out to typical observational distances. Thus, devising a means of approximating a nonlinear source in terms of a linear equivalent source would be advantageous both for scenario modeling and for interpretation of seismic source models that are based on linear, far-field approximations. This allows fast linear seismic modeling that still incorporates many features of the nonlinear source mechanics built into the simulation results so that one can have many of the advantages of both types of simulations without the computational cost of the nonlinear computation. In this report we first show the computational advantage of using linear equivalent models, and then discuss how the near-source (within the nonlinear wavefield regime) environment affects linear source equivalents and how well we can fit seismic wavefields derived from nonlinear sources.

58 GEOSCIENCES↗
DOE OSTI2026

Overview of IMPACT Data Acquisition System and Data Reduction Process

This report documents the development of the data acquisition system (DAS) and data reduction methodologies for the Irradiated Material Property Accelerated Characterization Test (IMPACT) experiment at the Advanced Test Reactor (ATR). The IMPACT experiment is designed to enable in-pile measurement of thermal conductivity in metallic nuclear fuels, specifically U-10Zr, using an instrumented thermal conductivity probe. The DAS supports both passive temperature monitoring and active thermal interrogation of the probe through controlled AC and DC excitation. Significant modifications to laboratory-scale systems were required to accommodate the higher resistance paths associated with the in-pile application. Custom electronics and relay-controlled measurement sequencing were developed to enable the measurement and sufficient power delivery to the sensing region. A reduced-order, axisymmetric thermal model based on the thermal quadrupoles method is presented to support data interpretation. This model enables efficient evaluation of transient heat transfer behavior and facilitates solution of the inverse problem required to extract thermal properties from measured signals. Multiple boundary condition formulations are discussed to address varying experimental time scales and geometries. Additionally, machine learning techniques are introduced to support data reduction and improve confidence in inverse solutions. Convolutional neural networks are applied to identify the presence of gas gaps and other evolving geometric features that significantly impact thermal response during irradiation. These efforts contribute to the broader integration of digital twin frameworks and real-time modeling capabilities within the Advanced Fuels Campaign.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
NASA NTRS1980

Wheat forecast economics effect study

A model to assess the value of improved information regarding the inventories, productions, exports, and imports of crop on a worldwide basis is discussed. A previously proposed model is interpreted in a stochastic control setting and the underlying assumptions of the model are revealed. In solving the stochastic optimization problem, the Markov programming approach is much more powerful and exact as compared to the dynamic programming-simulation approach of the original model. The convergence of a dual variable Markov programming algorithm is shown to be fast and efficient. A computer program for the general model of multicountry-multiperiod is developed. As an example, the case of one country-two periods is treated and the results are presented in detail. A comparison with the original model results reveals certain interesting aspects of the algorithms and the dependence of the value of information on the incremental cost function.

Mehra, R. K.↗
NASA NTRS1999

Remote Thermal IR Spectroscopy of our Solar System

Indirect methods to detect extrasolar planets have been successful in identifying a number of stars with companion planets. No direct detection of an extrasolar planet has yet been reported. Spectroscopy in the thermal infrared region provides a potentially powerful approach to detection and characterization of planets and planetary systems. We can use knowledge of our own solar system, its planets and their atmospheres to model spectral characteristics of planets around other stars. Spectra derived from modeling our own solar system seen from an extrasolar perspective can be used to constrain detection strategies, identification of planetary class (terrestrial vs. gaseous) and retrieval of chemical, thermal and dynamical information. Emission from planets in our solar system peaks in the thermal infrared region, approximately 10 - 30 microns, substantially displaced from the maximum of the much brighter solar emission in the visible near 0.5 microns. This fact provides a relatively good contrast ratio to discriminate between stellar (solar) and planetary emission and optimize the delectability of planetary spectra. Important molecular constituents in planetary atmospheres have rotational-vibrational spectra in the thermal infrared region. Spectra from these molecules have been well characterized in the laboratory and studied in the atmospheres of solar system planets from ground-based and space platforms. The best example of such measurements are the studies with Fourier transform spectrometers, the Infrared Interferometer Spectrometers (IRIS), from spacecraft: Earth observed from NIMBUS 8, Mars observed from Mariner 9, and the outer planets observed from Voyager spacecraft. An Earth-like planet is characterized by atmospheric spectra of ozone, carbon dioxide, and water. Terrestrial planets have oxidizing atmospheres which are easily distinguished from reducing atmospheres of gaseous giant planets which lack oxygen-bearing species and are characterized by spectra containing hydrocarbons such as methane and ethane. Spectroscopic information on extrasolar planets thus can permit their classification. Spectra and spectral lines contain information on the temperature structure of the atmosphere. Line and band spectra can be used to identify the molecular constituents and retrieve species abundances, thereby classifying and characterizing the planet. At high enough spectral resolution characteristic planetary atmospheric dynamics and unique phenomena such as failure of local thermodynamic equilibrium can be identified. Dynamically induced effects such as planetary rotation and orbital velocity shift and change the shape of spectral features and must be modeled in detailed spectral studies. We will use our knowledge of the compositional, thermal and dynamical characteristics of planetary atmospheres in our own solar system to model spectra observed remotely on similar planets in extrasolar planetary systems. We will use a detailed radiative transfer and beam integration program developed for the modeling and interpretation of thermal infrared spectra measured from nearby planet planets to generate models of an extra-solar "Earth" and "Jupiter". From these models we will show how key spectral features distinguish between terrestrial and gaseous planets, what information can be obtained with different spectral resolution, what spectral features can be used to search for conditions for biogenic activity, and how dynamics and distance modify the observed spectra. We also will look at unique planetary phenomena such as atmospheric lasing and discuss their utility as probes for detection and identification of planets. Results of such studies will provide information to constrain design for instrumentation needed to directly detect extrasolar planets.

Kostiuk, Theodor↗
DOE OSTI2022

Search for supersymmetry in final states with two or three soft leptons and missing transverse momentum in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A search for supersymmetry in events with two or three low-momentum leptons and missing transverse momentum is performed. The search uses proton-proton collisions at $\sqrt{s}$ = 13 TeV collected in the three-year period 2016–2018 by the CMS experiment at the LHC and corresponding to an integrated luminosity of up to 137 fb -1 . The data are found to be in agreement with expectations from standard model processes. The results are interpreted in terms of electroweakino and top squark pair production with a small mass difference between the produced supersymmetric particles and the lightest neutralino. For the electroweakino interpretation, two simplified models are used, a wino-bino model and a higgsino model. Exclusion limits at 95% confidence level are set on $^{\sim0}_{χ2}/^{\sim±}_{χ1}$ masses up to 275 GeV for a mass difference of 10 GeV in the wino-bino case, and up to 205(150) GeV for a mass difference of 7.5 (3) GeV in the higgsino case. The results for the higgsino are further interpreted using a phenomenological minimal supersymmetric standard model, excluding the higgsino mass parameter μ up to 180 GeV with the bino mass parameter M 1 at 800 GeV. In the top squark interpretation, exclusion limits are set at top squark masses up to 540 GeV for four-body top squark decays and up to 480 GeV for chargino-mediated decays with a mass difference of 30 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

pyTCR: A tropical cyclone rainfall model for python

pyTCR is a climatology software package developed in the Python programming language. It integrates the capabilities of several legacy physical models and increases computational efficiency to allow rapid estimation of tropical cyclone (TC) rainfall consistent with the large-scale environment. Specifically, pyTCR implements a horizontally distributed and vertically integrated model [Zhu et al., 2013] for simulating rainfall driven by TCs. Along storm tracks, rainfall is estimated by computing the cross-boundary-layer, upward water vapor transport caused by different mechanisms including frictional convergence, vortex stretching, large-scale baroclinic effect (i.e., wind shear), topographic forcing, and radiative cooling [Lu et al., 2018]. The package provides essential functionalities for modeling and interpreting spatio-temporal TC rainfall data. pyTCR requires a limited number of model input parameters, making it a convenient and useful tool for analyzing rainfall mechanisms driven by TCs. To sample rare (most intense) rainfall events that are often of great societal interest, pyTCR adapts and leverages outputs from a statistical-dynamical TC downscaling model [Lin et al., 2023] capable of rapidly generating a large number of synthetic TCs given a certain climate. As a result, pyTCR significantly reduces computational effort and improves the efficiency in capturing extreme TC rainfall events at the tail of the distributions from limited datasets. Furthermore, the TC downscaling model is forced entirely by large-scale environmental conditions from reanalysis data or coupled General Circulation Models (GCMs), simplifying the projection of TC-induced rainfall and wind speed under future climate using pyTCR. Finally, pyTCR can be coupled with hydrological and wind models to assess risks associated with independent and compound events (e.g., storm surges and freshwater flooding).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

A model for the proton spectrum and cosmic ray anisotropy

The problem of the origin of the cosmic rays is still uncertain. As a theory, it should explain the support of particles and energy, the mechanism of acceleration and propagation as well as some important features obtained directly from cosmic ray experiments, such as the power spectrum and the knee. There are two kinds of models for interpreting the knee of the cosmic ray spectrum. One is the leaky box model. Another model suggests that the cut-off rigidity of the main sources causes the knee. The present paper studies the spectrum and the anisotropy of cosmic rays in an isotropic diffuse model with explosive discrete sources in an infinite galaxy.

Xu, C.↗
NASA NTRS2012

GRB 120422A: a Low-Luminosity Gamma-Ray Burst Driven by a Central Engine

GRB 120422A is a low-luminosity gamma-ray burst (GRB) associated with a bright supernova, which distinguishesitself by its relatively short T(sub 90) (approximately 5 s) and an energetic and steep-decaying X-ray tail. We analyze the Swift BurstAlert Telescope and X-ray Telescope data and discuss the physical implications. We show that the steep declineearly in the X-ray light curve can be interpreted as the curvature tail of a late emission episode around 58-86 s,with a curved instantaneous spectrum at the end of the emission episode. Together with the main activity in thefirst 20 s and the weak emission from 40 s to 60 s, the prompt emission is variable, which points to a centralengine origin in contrast to a shock-breakout origin, which is used to interpret some other nearby low-luminosity supernova GRBs. Both the curvature effect model and interpreting the early shallow decay as the coasting externalforward shock emission in a wind medium provide a constraint on the bulk Lorentz factor to be around several.Comparing the properties ofGRB 120422A and other supernova GRBs,we find that themain criterion to distinguish engine-driven GRBs from shock-breakout GRBs is the time-averaged -ray luminosity. Engine-driven GRBs likelyhave a luminosity above approximately 10(sup 48) erg s(sup -1).

engine↗
DOE OSTI2026

Deciphering the Solvation Structure of Aqueous ZnCl 2 Solutions from X-ray Absorption Spectra Using the Interpretable Graph Neural Network

Machine learning (ML) provides powerful pathways for predicting spectroscopic observables from atomic structures, but its broader impact depends on making model predictions interpretable in terms of physical and chemical principles. Here, we introduce a physics-guided graph neural network (GNN) model that predicts Zn K-edge X-ray spectroscopy (XAS) spectra of aqueous ZnCl 2 solutions. Training data are generated from ab initio XAS calculations on molecular dynamics snapshots obtained using a machine learning interatomic potential. The GNN reproduces experimental spectra across concentrations from dilute (<0.1 m) to highly concentrated (30 m, “water-in-salt”) regimes and scales efficiently to large, disordered liquid systems beyond the reach of conventional ab initio approaches. Gradient-based attribution analysis reveals that the model learns physically meaningful structure-spectrum relationships. Ligand-specific attributions reflect orbital hybridization patterns and the origin of the excitations derived from the density functional theory. Bond-length attributions recover spectral shifts consistent with multiple-scattering theory. Finally, this work bridges data-driven prediction with electronic-structure theory, establishing a general paradigm for interpretable ML that links atomic structure, electronic structure, and spectroscopic observables.

25 ENERGY STORAGE↗