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

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At least 289 records · Page 16

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DOE OSTI2025

Inferring the Isotropic-Nematic Phase Transition with Generative Machine Learning

Generative machine learning models are capable of learning the phase behavior in condensed matter systems such as the Ising model. We utilize a score-based modeling procedure called thermodynamic maps to describe the isotropic-nematic phase transition in a melt of Gay-Berne ellipsoids. When trained on samples from a single temperature on either side of the phase transition, this generative machine learning approach infers effectively the nematic order parameter at intermediate temperatures. Furthermore, these results demonstrate score-based models’ ability to learn the physics of a nontrivial liquid crystal phase transition.

Critical exponents↗

DOE OSTI2024

Invariant amplitudes, unpolarized cross sections, and polarization asymmetries in neutrino-nucleon and antineutrino-nucleon elastic scattering

At leading order in weak and electromagnetic couplings, cross sections for (anti)neutrino-nucleon elastic scattering are determined by four nucleon form factors that depend on the momentum transfer Q 2 . Including radiative corrections in the Standard Model and potential new physics contributions beyond the Standard Model, eight invariant amplitudes are possible, depending on both Q 2 and the (anti)neutrino energy E ν . We review the definition of these amplitudes and use them to compute both unpolarized and polarized observables including radiative corrections. We show that unpolarized accelerator neutrino cross-section measurements can probe new physics parameter space within the constraints inferred from precision beta decay measurements. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1972

Elastic properties of Apollo 14 and 15 rocks.

Ultrasonic P- and S-wave velocities of lunar samples 14310,72 and 15418,43 and P-wave velocities of sample 15015,18 were measured at room temperature to 5 kb confining pressure. The velocities of both igneous and breccia samples increased sharply over this pressure range. At low confining pressures, the shape of velocity-pressure curves of rocks is determined by the distribution function of crack aspect ratios. We suggest that analogue studies on terrestrial rocks having a wide assortment of crack parameters may be used to infer the nature of cracks in lunar rocks.

Todd, T.↗

NASA NTRS1976

Experimental investigation of the excess charge and time constant of minority carriers in the thin diffused layer of 0.1 ohm-cm silicon solar cells

An experimental method is presented that can be used to interpret the relative roles of bandgap narrowing and recombination processes in the diffused layer. This method involves measuring the device time constant by open-circuit voltage decay and the base region diffusion length by X-ray excitation. A unique illuminated diode method is used to obtain the diode saturation current. These data are interpreted using a simple model to determine individually the minority carrier lifetime and the excess charge. These parameters are then used to infer the relative importance of bandgap narrowing and recombination processes in the diffused layer.

Godlewski, M. P.↗

NASA NTRS1990

Bulk properties and velocity distributions of water group ions at Comet Halley - Giotto measurements

In the region upstream of Comet Halley, pickup heavy ions of cometary origin were directly observed by the implanted ion spectrometer on Giotto. Diffusion of this population in pitch angle and in energy, during the approach to the comet and on the outbound leg is discussed. The two data sets are compared and qualitative ideas on scattering timescales are inferred. In addition the bulk parameters of these distributions have been computed and a comparison of the observed speed in the solar wind frame and the observed density with expectations is presented. Pitch angle scattering occurs more slowly than expected with filled shells appearing at 2,500,000 km, and significant energy diffusion does not occur until the bow shock region. Also the shell distributions downstream of the shock flow at the bispherical bulk speed (related to the Alfven speed) along the magnetic field with respect to the solar wind in accordance with conservation of energy between the pickup ions and the wave turbulence.

Coates, A. J.↗

NASA NTRS1993

A statistical analysis of mesoscale rainfall as a random cascade

The paper considers the random cascade theory for spatial rainfall. Particular attention was given to the following four areas: (1) the relationship of the random cascade theory of rainfall to the simple scaling and the hierarchical cluster-point-process theories, (2) the mathematical foundations for some of the formalisms commonly applied in the develpment of statistical cascade theory, (3) the empirical evidence for a random cascade theory of rainfall, and (4) the way of using data for making estimates of parameters and for making statistical inference within this theoretical framework. An analysis of space-time rainfall data is presented. Cascade simulations are carried out to provide a comparison with methods of analysis that are applied to the rainfall data.

Gupta, Vijay K.↗

NASA NTRS2005

Current Sheet Evolution in the Aftermath of a CME Event

We report on SOHO-UVCS observations of the coronal restructuring following a Coronal Mass Ejection (CME) on November 26,2002, at the time of a SOHO-Ulysses quadrature campaign. Starting about 3 hours after a CME in the NW quadrant, UVCS began taking spectra at 1.7 solar radius, covering emission from both cool and hot plasma. Observations continued, with occasional gaps, for more than 2 days. Emission in the 974.8 Angstrom line of [Fe XVIII], indicating temperatures above 6 x 10(exp 6) K, was observed throughout the campaign in a spatially limited location. Comparison with EIT images shows the Fe XVIII emission to overlie a growing post-flare loop system formed in the aftermath of the CME. The emission most likely originates in a current sheet overlying the arcade. Analysis of the [Fe XVIII] emission allows us to infer the evolution of physical parameters in the current sheet over the entire span of our observations: in particular, we give the temperature vs. time in the current sheet and estimate the density. At the time of the quadrature, Ulysses was directly above the location of the CME and intercepted the ejecta. High ionization state Fe was detected by Ulysses-SWICS throughout the magnetic cloud associated with the CME. Both the remote and in situ observations are compared with predictions of theoretical CME models.

Bemporad, A.↗

NASA NTRS2005

Coronal Current Sheet Evolution in the Aftermath of a CME

We report on SOHO-UVCS observations of coronal restructuring following a Coronal Mass Ejection (CME) on November 26, 2002, at the time of a SOHO-Ulysses quadrature campaign. Starting about 3 hours after the CME, which was directed towards Ulysses, UVCS began taking spectra at 1.7 solar radii, covering emission from both cool and hot plasma. Observations continued, with occasional gaps, for more than 2 days. Emission in the 974.8 Angstrom line of [Fe XVIII], indicating temperatures above 6x10(6) K, was observed throughout the campaign in a spatially limited location. Comparison with EIT images shows the [Fe XVIII] emission to overlie a growing post-flare loop system formed in the aftermath of the CME. The emission most likely originates in a current sheet overlying the arcade. Analysis of the [Fe XVIII] emission allows us to infer the evolution of physical parameters in the current sheet over the entire span of our observations: in particular, we give the temperature vs. time in the current sheet and estimate the density. Ulysses was directly above the location of the CME and intercepted the ejecta. High ionization state Fe was detected by SWICS throughout the magnetic cloud associated with the CME, although the rapid temporal variation suggests bursty, rather than smooth, reconnection in the coronal current sheet. Both the remote and in situ observations are compared with predictions of theoretical CME models.

Bemporad, A.↗

NASA NTRS2006

Current Sheet Evolution In The Aftermath Of A CME Event

We report on SOHO UVCS observations of the coronal restructuring following a coronal mass ejection (CME) on 2002 November 26, at the time of a SOHO-Ulysses quadrature campaign. Starting about 1.5 hr after a CME in the northwest quadrant, UVCS began taking spectra at 1.7 R, covering emission from both cool and hot plasma. Observations continued, with occasional gaps, for more than 2 days. Emission in the 974.8 A line of [Fe XVIII], indicating temperatures above 6 x 10(exp 6) K, was observed throughout the campaign in a spatially limited location. Comparison with EIT images shows the [Fe XVIII] emission to overlie a growing post-flare loop system formed in the aftermath of the CME. The emission most likely originates in a current sheet overlying the arcade. Analysis of the [Fe XVIII] emission allows us to infer the evolution of physical parameters in the current sheet over the entire span of our observations: in particular, we give the temperature versus time in the current sheet and estimate its density. At the time of the quadrature, Ulysses was directly above the location of the CME and intercepted the ejecta. High ionization state Fe was detected by the Ulysses SWICS throughout the magnetic cloud associated with the CME, although its rapid temporal variation suggests bursty, rather than smooth, reconnection in the coronal current sheet. The SOHO-Ulysses data set provided us with the unique opportunity of analyzing a current sheet structure from its lowest coronal levels out to its in situ properties. Both the remote and in situ observations are compared with predictions of theoretical CME models.

Bemporad, A.↗

NASA NTRS2006

Lifetime Evolution of UV Jets

We report on observations acquired in May 2003 during a SOHO-Ulysses quadrature campaign. From May 25 to May 28, the SoHO LASCO Coronal Mass Ejection (CME) catalog lists a number of events which might have been observed by SOHO/UVCS, whose slit was centered along the Ulysses direction. However, because of time gaps in the observing schedule, or because of the unfavorable position of some CMEs, the most interesting events recorded by UVCS were a few short-lived ejections that represent the extension at higher altitudes of recursive EIT jets. We focus on jets occurring on May 26/27, visible also in EIT and LASCO images, which seem to propagate along the radial to Ulysses. UVCS spectra at 1.7 Rsun showed an unusually high emission in cool lines, lasting for about 10 to 25 minutes, with no evidence of hot plasma. Analysis of the cool line emission allowed us to infer the evolution of physical parameters during the jets lifetime and derive a crude estimate of the energy needed to account for their properties. We also looked for any evidence of the event in in situ data. Whether UVCS is observing jets or narrow CMEs is discussed in the contest of previous works on these classes of events and, in the last Section, we propose a scenario that accounts for our observations.

Corti, G.↗

NASA NTRS2009

Lunar Neutral Exposphere Properties from Pickup Ion Analysis

Composition and structure of neutral constituents in the lunar exosphere can be determined through measurements of phase space distributions of pickup ions borne from the exosphere [1]. An essential point made in an early study [ 1 ] and inferred by recent pickup ion measurements [2, 3] is that much lower neutral exosphere densities can be derived from ion mass spectrometer measurements of pickup ions than can be determined by conventional neutral mass spectrometers or remote sensing instruments. One approach for deriving properties of neutral exospheric source gasses is to first compare observed ion spectra with pickup ion model phase space distributions. Neutral exosphere properties are then inferred by adjusting exosphere model parameters to obtain the best fit between the resulting model pickup ion distributions and the observed ion spectra. Adopting this path, we obtain ion distributions from a new general pickup ion model, an extension of a simpler analytic description obtained from the Vlasov equation with an ion source [4]. In turn, the ion source is formed from a three-dimensional exospheric density distribution, which can range from the classical Chamberlain type distribution to one with variable exobase temperatures and nonthermal constituents as well as those empirically derived. The initial stage of this approach uses the Moon's known neutral He and Na exospheres to deriv e He+ and Na+ pickup ion exospheres, including their phase space distributions, densities and fluxes. The neutral exospheres used are those based on existing models and remote sensing studies. As mentioned, future ion measurements can be used to constrain the pickup ion model and subsequently improve the neutral exosphere descriptions. The pickup ion model is also used to estimate the exosphere sources of recently observed pickup ions on KAGUYA [3]. Future missions carrying ion spectrometers (e.g., ARTEMIS) will be able to study the lunar neutral exosphere with great sensitivity, yielding the necessary ion velocity spectra needed to further analysis of parent neutral exosphere properties.

Hartle, R. E.↗

NASA NTRS2014

Learning Extended Finite State Machines

We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.

DOE OSTI2024

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial insights into cosmology, but traditional Monte Carlo methods for cosmological inference are computationally prohibitive and inadequate for processing the thousands of lenses anticipated from future cosmic surveys. New tools for inference, such as SBI using Neural Ratio Estimation (NRE), address this challenge effectively. By training a machine learning model on simulated data of strong lenses, we can learn the likelihood-to-evidence ratio for robust inference. Our scalable approach enables more constrained population-level inference of $w$ compared to individual lens analysis, constraining $w$ to within $1\sigma$.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1981

Diurnal variability of regional cloud radiative parameters from geostationary satellite data

In the discussed study, Geostationary Operational Environmental Satellite visible and infrared brightness measurements are used to infer regional cloud cover and several associated radiative parameters on an hourly basis. Several techniques are utilized to derive the parameters of interest. These parameters include the areal cloud fraction, cloud-atmosphere albedo, surface-atmosphere albedo, and equivalent blackbody surface and cloud-top temperatures. Attention is given to clear radiance temperature determinations, surface temperature modeling, the infrared threshold method, narrowband-broadband correlations, and albedo determination. Monthly mean values were computed for each parameter as a function of local time. Cloud amounts and temperatures are considered along with longwave emission and shortwave albedo, and composite results.

Minnis, P.↗

NASA NTRS1984

Vertical temperature sounding of the atmosphere

Attention is given to physical principles that affect the determination of atmospheric temperature profiles from observations of outgoing IR and microwave radiation. Simultaneous analysis of IR and microwave observations enhances observations in each spectral region. While IR soundings have higher sensitivity to lower tropospheric temperatures, ground temperatures, and sea surface temperatures, microwave observations are not only more suited to stratospheric sounding but are sensitive to snow and ice cover. They significantly aid correction of IR observations for cloud effects. Because effective cloud parameters and snow and ice cover can be inferred from the observations, 'temperature sounders' also yield data for a number of important climatic parameters.

Suskind, J.↗

DOE OSTI2025

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗