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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Electron-beam energy reconstruction for neutrino oscillation measurements

Neutrinos exist in one of three types or ‘flavours’—electron, muon and tau neutrinos—and oscillate from one flavour to another when propagating through space. This phenomena is one of the few that cannot be described using the standard model of particle physics (reviewed in ref. 1), and so its experimental study can provide new insight into the nature of our Universe (reviewed in ref. 2). Neutrinos oscillate as a function of their propagation distance (L) divided by their energy (E). Therefore, experiments extract oscillation parameters by measuring their energy distribution at different locations. As accelerator-based oscillation experiments cannot directly measure E, the interpretation of these experiments relies heavily on phenomenological models of neutrino–nucleus interactions to infer E. Here we exploit the similarity of electron–nucleus and neutrino–nucleus interactions, and use electron scattering data with known beam energies to test energy reconstruction methods and interaction models. We find that even in simple interactions where no pions are detected, only a small fraction of events reconstruct to the correct incident energy. More importantly, widely used interaction models reproduce the reconstructed energy distribution only qualitatively and the quality of the reproduction varies strongly with beam energy. This shows both the need and the pathway to improve current models to meet the requirements of next-generation, high-precision experiments such as Hyper-Kamiokande (Japan) and DUNE (USA).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
DOE OSTI2016

Three-dimensional simulations of turbulent convective mixing in ONe and CO classical nova explosions

Classical novae are thermonuclear explosions that take place in the envelopes of accreting white dwarfs in binary systems. The material piles up under degenerate conditions, driving a thermonuclear runaway. The energy released by the suite of nuclear processes operating at the envelope heats the material up to peak temperatures of ~(1-4) × 10 8 K. During these events, about 10 -3 -10 -7 M ⊙ , enriched in CNO and, sometimes, other intermediate-mass elements (e.g., Ne, Na, Mg, Al) are ejected into the interstellar medium. To account for the gross observational properties of classical novae (in particular, the high concentrations of metals spectroscopically inferred in the ejecta), models require mixing between the (solar-like) material transferred from the secondary and the outermost layers (CO- or ONe-rich) of the underlying white dwarf. Recent multidimensional simulations have demonstrated that Kelvin-Helmholtz instabilities can naturally produce self-enrichment of the accreted envelope with material from the underlying white dwarf at levels that agree with observations. However, the feasibility of this mechanism has been explored in the framework of CO white dwarfs, while mixing with different substrates still needs to be properly addressed. We performed three-dimensional simulations of mixing at the core-envelope interface during nova outbursts with the multidimensional code FLASH, for two types of substrates: CO- and ONe-rich. We also show that the presence of an ONe-rich substrate, as in “neon novae”, yields higher metallicity enhancements in the ejecta than CO-rich substrates (i.e., non-neon novae). Finally, a number of requirements and constraints for such 3D simulations (e.g., minimum resolution, size of the computational domain) are also outlined.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Quantum efficiency investigations of type-II InAs/GaSb midwave infrared superlattice photodetectors

We present in this paper a comparison between different type-II InAs/GaSb superlattice (T2SL) photodiodes and focal plane array (FPA) in the mid-wavelength infrared domain to understand which phenomenon drives the performances of the T2SL structure in terms of quantum efficiency (QE). Our measurements on test photodiodes suggest low minority carrier diffusion length in the “InAs-rich” design, which penalizes carriers' collection in this structure for low bias voltage and front side illumination. This analysis is completed by a comparison of the experimental data with a fully analytic model, which allows to infer a hole diffusion length shorter than 100 nm. In addition, measurements on a FPA with backside illumination are finally presented. Results show an average QE in the 3–4.7 μm window equal to 42% for U{sub bias} = −0.1 V, 77 K operating temperature and no anti-reflection coating. These measurements, completed by modulation transfer function and noise measurements, reveal that the InAs-rich design, despite a low hole diffusion length, is promising for high performance infrared imaging applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Quantum learning of classical stochastic processes: The completely positive realization problem

Among several tasks in Machine Learning, a specially important one is the problem of inferring the latent variables of a system and their causal relations with the observed behavior. A paradigmatic instance of this is the task of inferring the hidden Markov model underlying a given stochastic process. This is known as the positive realization problem (PRP), [L. Benvenuti and L. Farina, IEEE Trans. Autom. Control 49(5), 651–664 (2004)] and constitutes a central problem in machine learning. The PRP and its solutions have far-reaching consequences in many areas of systems and control theory, and is nowadays an important piece in the broad field of positive systems theory. We consider the scenario where the latent variables are quantum (i.e., quantum states of a finite-dimensional system) and the system dynamics is constrained only by physical transformations on the quantum system. The observable dynamics is then described by a quantum instrument, and the task is to determine which quantum instrument — if any — yields the process at hand by iterative application. We take as a starting point the theory of quasi-realizations, whence a description of the dynamics of the process is given in terms of linear maps on state vectors and probabilities are given by linear functionals on the state vectors. This description, despite its remarkable resemblance with the hidden Markov model, or the iterated quantum instrument, is however devoid of any stochastic or quantum mechanical interpretation, as said maps fail to satisfy any positivity conditions. The completely positive realization problem then consists in determining whether an equivalent quantum mechanical description of the same process exists. We generalize some key results of stochastic realization theory, and show that the problem has deep connections with operator systems theory, giving possible insight to the lifting problem in quotient operator systems. Our results have potential applications in quantum machine learning, device-independent characterization and reverse-engineering of stochastic processes and quantum processors, and more generally, of dynamical processes with quantum memory [M. Guţă, Phys. Rev. A 83(6), 062324 (2011); M. Guţă and N. Yamamoto, e-print http://arxiv.org/abs/1303.3771 (2013)].

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Effects of preheat and mix on the fuel adiabat of an imploding capsule

We demonstrate the effect of preheat, hydrodynamic mix and vorticity on the adiabat of the deuterium-tritium (DT) fuel in fusion capsule experiments. We show that the adiabat of the DT fuel increases resulting from hydrodynamic mixing due to the phenomenon of entropy of mixture. An upper limit of mix, M clean =M DT ≥ 0:98 is found necessary to keep the DT fuel on a low adiabat. We demonstrate in this study that the use of a high adiabat for the DT fuel in theoretical analysis and with the aid of 1D code simulations could explain some aspects of 3D effects and mix in capsule implosion. Furthermore, we can infer from our physics model and the observed neutron images the adiabat of the DT fuel in the capsule and the amount of mix produced on the hot spot.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Using multiple neutron time of flight detectors to determine the hot spot velocity

An important diagnostic value of a shot at the National Ignition Facility is the resultant center-of-mass motion of the imploding capsule. This residual velocity reduces the efficiency of converting laser energy into plasma temperature. A new analysis method extracts the effective hot spot motion by using information from multiple neutron time-of-flight (nToF) lines-of-sight (LoSs). This technique fits a near Gaussian spectrum to the nToF scope traces and overcomes reliance on models to relate the plasma temperature to the mean energy of the emitted neutrons. This method requires having at least four nToF LoSs. The results of this analysis will be compared to an approach where each LoS is analyzed separately and a model is employed to infer the mean energy of the emitted neutrons

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Simple and sophisticated models of Taylor’s cylinder impact

We present a study based on the simplest possible models for Taylor’s cylinder impact problem, in addition to examina-tion of using convolutional neural networks (CNNs) to map cylinder profiles to strength calibrations. We find that the approximate treatments of Taylor and Hawkyard compare well with hydrodynamic simulations using an equivalent assumption of constant flow stress. The CNN models prove to be well suited to successfully infer parameterizations of the Preston-Tonks-Wallace model of plastic deformation based on the deformed profile of an impacted cylinder.

36 MATERIALS SCIENCE↗
DOE OSTI2015

New near-infrared period–luminosity–metallicity relations for RR Lyrae stars and the outlook for GAIA

We present results of the analysis of 70 RR Lyrae stars located in the bar of the Large Magellanic Cloud (LMC). Combining the spectroscopically determined metallicity of these stars from the literature with precise periods from the OGLE III catalog and multi-epoch K{sub s} photometry from the VISTA survey of the Magellanic Clouds system, we derive a new near-infrared period–luminosity–metallicity (PL{sub K{sub s}}Z) relation for RR Lyrae variables. In order to fit the relation we use a fitting method developed specifically for this study. The zero-point of the relation is estimated two different ways: by assuming the value of the distance to the LMC and by using Hubble Space Telescope parallaxes of five RR Lyrae stars in the Milky Way (MW). The difference in distance moduli derived by applying these two approaches is ∼0.2 mag. To investigate this point further we derive the PL{sub K{sub s}}Z relation based on 23 MW RR Lyrae stars that had been analyzed in Baade–Wesselink studies. We compared the derived PL{sub K{sub s}}Z relations for RR Lyrae stars in the MW and LMC. Slopes and zero-points are different, but still consistent within the errors. The shallow slope of the metallicity term is confirmed by both LMC and MW variables. The astrometric space mission Gaia is expected to provide a huge contribution to the determination of the RR Lyrae PL{sub K{sub s}}Z relation; however, calculating an absolute magnitude from the trigonometric parallax of each star and fitting a PL{sub K{sub s}}Z relation directly to period and absolute magnitude leads to biased results. We present a tool to achieve an unbiased solution by modeling the data and inferring the slope and zero-point of the relation via statistical methods.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

CONSTRUCTING A FLEXIBLE LIKELIHOOD FUNCTION FOR SPECTROSCOPIC INFERENCE

We present a modular, extensible likelihood framework for spectroscopic inference based on synthetic model spectra. The subtraction of an imperfect model from a continuously sampled spectrum introduces covariance between adjacent datapoints (pixels) into the residual spectrum. For the high signal-to-noise data with large spectral range that is commonly employed in stellar astrophysics, that covariant structure can lead to dramatically underestimated parameter uncertainties (and, in some cases, biases). We construct a likelihood function that accounts for the structure of the covariance matrix, utilizing the machinery of Gaussian process kernels. This framework specifically addresses the common problem of mismatches in model spectral line strengths (with respect to data) due to intrinsic model imperfections (e.g., in the atomic/molecular databases or opacity prescriptions) by developing a novel local covariance kernel formalism that identifies and self-consistently downweights pathological spectral line “outliers.” By fitting many spectra in a hierarchical manner, these local kernels provide a mechanism to learn about and build data-driven corrections to synthetic spectral libraries. An open-source software implementation of this approach is available at http://iancze.github.io/Starfish, including a sophisticated probabilistic scheme for spectral interpolation when using model libraries that are sparsely sampled in the stellar parameters. We demonstrate some salient features of the framework by fitting the high-resolution V-band spectrum of WASP-14, an F5 dwarf with a transiting exoplanet, and the moderate-resolution K-band spectrum of Gliese 51, an M5 field dwarf.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Accelerating multilevel Markov Chain Monte Carlo using machine learning models

Here, this work presents an efficient approach for accelerating multilevel Markov Chain Monte Carlo (MCMC) sampling for large-scale problems using low-fidelity machine learning models. While conventional techniques for large-scale Bayesian inference often substitute computationally expensive high-fidelity models with machine learning models, thereby introducing approximation errors, our approach offers a computationally efficient alternative by augmenting high-fidelity models with low-fidelity ones within a hierarchical framework. The multilevel approach utilizes the low-fidelity machine learning model (MLM) for inexpensive evaluation of proposed samples thereby improving the acceptance of samples by the high-fidelity model. The hierarchy in our multilevel algorithm is derived from geometric multigrid hierarchy. We utilize an MLM to accelerate the coarse level sampling. Training machine learning model for the coarsest level significantly reduces the computational cost associated with generating training data and training the model. We present an MCMC algorithm to accelerate the coarsest level sampling using MLM and account for the approximation error introduced. We provide theoretical proofs of detailed balance and demonstrate that our multilevel approach constitutes a consistent MCMC algorithm. Additionally, we derive the expression for cost reduction due to machine learning model to facilitate cost analysis of the hierarchical sampling algorithm. Our technique is demonstrated on a standard benchmark inference problem in groundwater flow, where we estimate the probability density of a quantity of interest using a four-level MCMC algorithm. Our proposed algorithm accelerates multilevel sampling by a factor of two while achieving similar accuracy compared to sampling using the standard multilevel algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Modulations of the cosmic muon signal in ten years of Borexino data

We have measured the flux of cosmic muons in the Laboratori Nazionali del Gran Sasso at 3800\,m\,w.e. to be (3.432±0.003)∙10 -4 m -2 s -1 based on ten years of Borexino data acquired between May 2007 and May 2017. A seasonal modulation with a period of (366.3±0.6)d and a relative amplitude of (1.36±0.04)% is observed. The phase is measured to be (181.7±0.4)d, corresponding to a maximum at the 1st of July. Using data inferred from global atmospheric models, we show the muon flux to be positively correlated with the atmospheric temperature and measure the effective temperature coefficient α T =0.90±0.02. The origin of cosmic muons from pion and kaon decays in the atmosphere allows to interpret the effective temperature coefficient as an indirect measurement of the atmospheric kaon-to-pion production ratio rK/π=0.11 +0.11 -0.07 for primary energies above 18TeV. We find evidence for a long-term modulation of the muon flux with a period of ~3000d and a maximum in June 2012 that is not present in the atmospheric temperature data. A possible correlation between this modulation and the solar activity is investigated. The cosmogenic neutron production rate is found to show a seasonal modulation in phase with the cosmic muon flux but with an increased amplitude of (2.6±0.4)%.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

An outflow powers the optical rise of the nearby, fast-evolving tidal disruption event AT2019qiz

ABSTRACT At 66 Mpc, AT2019qiz is the closest optical tidal disruption event (TDE) to date, with a luminosity intermediate between the bulk of the population and the faint-and-fast event iPTF16fnl. Its proximity allowed a very early detection and triggering of multiwavelength and spectroscopic follow-up well before maximum light. The velocity dispersion of the host galaxy and fits to the TDE light curve indicate a black hole mass ≈106 M⊙, disrupting a star of ≈1 M⊙. By analysing our comprehensive UV, optical, and X-ray data, we show that the early optical emission is dominated by an outflow, with a luminosity evolution L ∝ t2, consistent with a photosphere expanding at constant velocity (≳2000 km s−1), and a line-forming region producing initially blueshifted H and He ii profiles with v = 3000–10 000 km s−1. The fastest optical ejecta approach the velocity inferred from radio detections (modelled in a forthcoming companion paper from K. D. Alexander et al.), thus the same outflow may be responsible for both the fast optical rise and the radio emission – the first time this connection has been observed in a TDE. The light-curve rise begins 29 ± 2 d before maximum light, peaking when the photosphere reaches the radius where optical photons can escape. The photosphere then undergoes a sudden transition, first cooling at constant radius then contracting at constant temperature. At the same time, the blueshifts disappear from the spectrum and Bowen fluorescence lines (N iii) become prominent, implying a source of far-UV photons, while the X-ray light curve peaks at ≈1041 erg s−1. Assuming that these X-rays are from prompt accretion, the size and mass of the outflow are consistent with the reprocessing layer needed to explain the large optical to X-ray ratio in this and other optical TDEs, possibly favouring accretion-powered over collision-powered outflow models.

Nicholl, M.↗
DOE OSTI2021

Impact of point spread function higher moments error on weak gravitational lensing

ABSTRACT Weak gravitational lensing is one of the most powerful tools for cosmology, while subject to challenges in quantifying subtle systematic biases. The point spread function (PSF) can cause biases in weak lensing shear inference when the PSF model does not match the true PSF that is convolved with the galaxy light profile. Although the effect of PSF size and shape errors – i.e. errors in second moments – is well studied, weak lensing systematics associated with errors in higher moments of the PSF model require further investigation. The goal of our study is to estimate their potential impact for LSST weak lensing analysis. We go beyond second moments of the PSF by using image simulations to relate multiplicative bias in shear to errors in the higher moments of the PSF model. We find that the current level of errors in higher moments of the PSF model in data from the Hyper Suprime-Cam survey can induce a ∼0.05 per cent shear bias, making this effect unimportant for ongoing surveys but relevant at the precision of upcoming surveys such as LSST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

A unified analysis of four cosmic shear surveys

In the past few years, several independent collaborations have presented cosmological constraints from tomographic cosmic shear analyses. These analyses differ in many aspects: the datasets, the shear and photometric redshift estimation algorithms, the theory model assumptions, and the inference pipelines. To assess the robustness of the existing cosmic shear results, we present in this paper a unified analysis of four of the recent cosmic shear surveys: the Deep Lens Survey (DLS), the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), the Science Verification data from the Dark Energy Survey (DES-SV), and the 450 deg$^{2}$ release of the Kilo-Degree Survey (KiDS-450). By using a unified pipeline, we show how the cosmological constraints are sensitive to the various details of the pipeline. We identify several analysis choices that can shift the cosmological constraints by a significant fraction of the uncertainties. For our fiducial analysis choice, considering a Gaussian covariance, conservative scale cuts, assuming no baryonic feedback contamination, identical cosmological parameter priors and intrinsic alignment treatments, we find the constraints (mean, 16% and 84% confidence intervals) on the parameter $S_{8}\equiv \sigma_{8}(\Omega_{\rm m}/0.3)^{0.5}$ to be $S_{8}=0.94_{-0.045}^{+0.046}$ (DLS), $0.66_{-0.071}^{+0.070}$ (CFHTLenS), $0.84_{-0.061}^{+0.062}$ (DES-SV) and $0.76_{-0.049}^{+0.048}$ (KiDS-450). From the goodness-of-fit and the Bayesian evidence ratio, we determine that amongst the four surveys, the two more recent surveys, DES-SV and KiDS-450, have acceptable goodness-of-fit and are consistent with each other. Furthermore the combined constraints are $S_{8}=0.79^{+0.042}_{-0.041}$, which is in good agreement with the first year of DES cosmic shear results and recent CMB constraints from the Planck satellite.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Proton decay spectroscopy of S 28 and Cl 30

States in 28 S and 30 Cl have been studied using one- and two-proton decay spectroscopy. In the first spectrometer setting, states in 28 S were populated following one-neutron knockout from a fast 29 S beam. Three new states are observed in 28 S from one- and two-proton decay. For the two-proton case the nature of the decay was investigated and found to proceed via sequential two-proton emission. For the second setting, states in 30 Cl were populated via one-proton knockout from a fast 31 Ar beam. The decay energy of the ground and first excited state were measured, with the ground-state decay energy found to be in disagreement with a previous measurement. Here, the spin and parity of these two 30 Cl states were inferred from shell-model calculations.

20 ≤ A ≤ 38↗
DOE OSTI2023

X-ray Thomson scattering spectra from density functional theory molecular dynamics simulations based on a modified Chihara formula

Here, we study ab initio approaches for calculating x-ray Thomson scattering spectra from density functional theory molecular dynamics simulations based on a modified Chihara formula that expresses the inelastic contribution in terms of the dielectric function. We study the electronic dynamic structure factor computed from the Mermin dielectric function using an ab initio electron-ion collision frequency in comparison to computations using a linear-response time-dependent density functional theory (LR-TDDFT) framework for hydrogen and beryllium and investigate the dispersion of free-free and bound-free contributions to the scattering signal. A separate treatment of these contributions, where only the free-free part follows the Mermin dispersion, shows good agreement with LR-TDDFT results for ambient-density beryllium, but breaks down for highly compressed matter where the bound states become pressure ionized. LR-TDDFT is used to reanalyze x-ray Thomson scattering experiments on beryllium demonstrating strong deviations from the plasma conditions inferred with traditional analytic models at small scattering angles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗