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Results for “explainability”

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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Self-generated cosmic-ray turbulence can explain the morphology of TeV halos

Observations have shown that spatially extended “TeV halos” are a common (and potentially generic) feature surrounding young and middle-aged pulsars. However, their morphology is not understood. They are larger than the “compact” region where the stellar remnant dominates the properties of the interstellar medium, but smaller than expected in models of cosmic-ray diffusion through the standard interstellar medium. Several explanations have been proposed, but all have shortcomings. In this work, we revisit a class of models where the cosmic-ray gradient produced by the central source induces a streaming stability that “self-confines” the cosmic-ray population. We find that previous studies significantly underpredicted the degree of cosmic-ray confinement and show that corrected models can significantly inhibit cosmic-ray diffusion throughout the TeV halo, especially when similar contributions from the coincident supernova remnant are included.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Can axionlike particles explain the alignments of the polarizations of light from quasars?

The standard axionlike particle explanation of the observed large-scale coherent orientations of quasar polarization vectors is ruled out by the recent measurements of vanishing circular polarization. We introduce a more general wave-packet formalism and show that, although decoherence effects between waves of different frequencies can reduce significantly the amount of circular polarization, the axionlike particle hypothesis is disfavored given the bandwidth with which part of the observations were performed. Finally, we show that a more sophisticated model of extragalactic fields does not lead to an alignment of polarizations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Nuclear yield reduction in inertial confinement fusion exploding-pusher targets explained by fuel-pusher mixing through hybrid kinetic-fluid modeling

In inertial confinement fusion implosion experiments involving fast laser heating of glass capsules containing gaseous nuclear fuel, some nuclear measurements cannot be rendered by standard hydrodynamics numerical simulations. Here, the calculated values of the nuclear yield are found to be too high by more than a decade in some cases. The first kinetic simulations of the enclosed reacting gas have bridged only half that gap. In this Rapid Communication, using a hybrid ion kinetic-fluid numerical model which takes into account the kinetic interaction between the fuel and the pusher, we reach full agreement with the experimental yield data. We thus unambiguously demonstrate that the kinetic behavior of the system, resulting in both nonthermodynamic equilibrium of the ions comprising the fuel and strong pusher-fuel mixing, is the correct interpretation of the data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Can Ab Initio Theory Explain the Phenomenon of Parity Inversion in Be 11 ?

The weakly bound exotic 11 Be nucleus, famous for its ground-state parity inversion and distinct n + 10 Be halo structure, is investigated from first principles using chiral two- and three-nucleon forces. An explicit treatment of continuum effects is found to be indispensable. We study the sensitivity of the 11 Be spectrum to the details of the three-nucleon force and demonstrate that only certain chiral interactions are capable of reproducing the parity inversion. With such interactions, the extremely large E 1 transition between the bound states is reproduced. We compare our photodisintegration calculations to conflicting experimental data and predict a distinct dip around the 3/2 – 1 resonance energy. Finally, we predict low-lying 3/2 + and 9/2 + resonances that are not or not sufficiently measured in experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Explainable machine learning for hydrogen diffusion in metals and random binary alloys

Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared errors between 98–119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies. By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importance, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. In conclusion, this framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Explaining Health Risk Behaviors in the U.S. with Social Deprivation at Local and Regional Levels

Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.

Gokhale, Swapna↗