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At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Correlations of conserved quantities at finite baryon density

Correlations involving the seven conserved quantities, namely energy, baryon number, electric charge, strangeness, and the three components of momentum, give rise to correlations in heavy-ion collisions. Through the utilization of a simple one-dimensional hydrodynamic model, we calculate the evolution of the entire 7×7 matrix of correlations as a function of relative spatial rapidity. This comprehensive analysis accounts for finite baryon density, which results in off-diagonal correlations between the charge-related quantities and the energy-momentum quantities. These correlations in coordinate space are subsequently transformed into correlations in momentum space using statistical weighting. As a result, the entire matrix of correlations is revealed to be highly sensitive to the equation of state, viscosity, and diffusivity.

Equations of state of nuclear matter↗
DOE OSTI2021

Validation Metrics for Fixed Effects and Mixed-Effects Calibration

The modern scientific process often involves the development of a predictive computational model. To improve its accuracy, a computational model can be calibrated to a set of experimental data. A variety of validation metrics can be used to quantify this process. Some of these metrics have direct physical interpretations and a history of use, while others, especially those for probabilistic data, are more difficult to interpret. In this work, a variety of validation metrics are used to quantify the accuracy of different calibration methods. Frequentist and Bayesian perspectives are used with both fixed effects and mixed-effects statistical models. Through a quantitative comparison of the resulting distributions, the most accurate calibration method can be selected. Two examples are included which compare the results of various validation metrics for different calibration methods. It is quantitatively shown that, in the presence of significant laboratory biases, a fixed effects calibration is significantly less accurate than a mixed-effects calibration. This is because the mixed-effects statistical model better characterizes the underlying parameter distributions than the fixed effects model. The results suggest that validation metrics can be used to select the most accurate calibration model for a particular empirical model with corresponding experimental data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Ultraviolet digital holographic microscopy (DHM) of micron-scale particles from shocked Sn ejecta

A cloud of very fast, O (km/s), and very fine, O (µm), particles may be ejected when a strong shock impacts and possibly melts the free surface of a solid metal. To quantify these dynamics, this work develops an ultraviolet, long-working distance, two-pulse Digital Holographic Microscopy (DHM) configuration and is the first to replace film recording with digital sensors for this challenging application. A proposed multi-iteration DHM processing algorithm is demonstrated for automated measures of the sizes, velocities, and three-dimensional positions of non-spherical particles. Ejecta as small as 2 µm diameter are successfully tracked, while uncertainty simulations indicate that particle size distributions are accurately quantified for diameters ≥4 µm. These techniques are demonstrated on three explosively driven experiments. Measured ejecta size and velocity statistics are shown to be consistent with prior film-based recording, while also revealing spatial variations in velocities and 3D positions that have yet to be widely investigated. Having eliminated time-consuming analog film processing, the methodologies proposed here are expected to significantly accelerate future experimental investigation of ejecta physics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Accelerator Neutrinos

Neutrino beams generated by particle accelerators are essential for probing fundamental physics. This presentation will examine the creation of high-intensity, well-collimated neutrino beams, crucial for long-baseline experiments like DUNE and NOvA. These experiments are pushing proton beam power to multi-MW levels and utilizing large-scale detectors to overcome the challenge of limited event statistics. At LBNF, the DUNE experiment will rigorously test the three-neutrino flavor model and explore CP violation by analyzing oscillation signatures in high-intensity νμ(νμˉ)νμ​(νμ​ˉ​) to νe(νeˉ)νe​(νe​ˉ​) beams. We'll explore the technical complexities of beamline components, the drive towards higher beam powers, and the strategies for measuring and managing neutrino flux. The presentation will also cover advancements in neutrino beam instrumentation and efforts to enhance beam precision, which are key to achieving the next generation of multi-megawatt accelerator facilities. By reviewing past achievements and future prospects, this talk aims to provide a clear overview of the current state and future potential of neutrino beam technology.

Ganguly, Sudeshna↗
DOE OSTI2025

Selection Algorithm Improvement for MicroBooNE

Data selection is an extremely important part of data analysis for any experiment. Finding a physics result is often the result of sifting through a massive amount of data, keeping data that we believe to be signal and throwing out data we do not. This process is called data selection. Creating a selection algorithm is an intensive process that must balance keeping enough data to have statistics and maximizing the signal purity of that data. In this study, we used three different reconstruction tools, Pandora, WireCell, and LANTERN, for the MicroBooNE experiment in conjunction to improve the selection algorithm for analysis. For the case of this study, we look into the charged current N proton 0 pions (CCNp0$\pi$) interaction channel. This is the dominant channel for the Short Baseline Neutrino (SBN) program and is expected to be a large contributor to the Deep Underground Neutrino Experiment (DUNE). We first investigated each of the three tools to find out more about their strengths and weaknesses as reconstructions. We then put together a direct comparison of the three methods to find which method or combination of methods would return the best result for us. While the study is ongoing, we have learned a lot about data selection for the experiment and the differences between the reconstruction tools.

Dillon, Brayden [Michigan State U.]↗
DOE OSTI2021

Extreme metrics from large ensembles: investigating the effects of ensemble size on their estimates

Abstract. We consider the problem of estimating the ensemble sizes required to characterize the forced component and the internal variability of a number of extreme metrics. While we exploit existing large ensembles, our perspective is that of a modeling center wanting to estimate a priori such sizes on the basis of an existing small ensemble (we assume the availability of only five members here). We therefore ask if such a small-size ensemble is sufficient to estimate accurately the population variance (i.e., the ensemble internal variability) and then apply a well-established formula that quantifies the expected error in the estimation of the population mean (i.e., the forced component) as a function of the sample size n, here taken to mean the ensemble size. We find that indeed we can anticipate errors in the estimation of the forced component for temperature and precipitation extremes as a function of n by plugging into the formula an estimate of the population variance derived on the basis of five members. For a range of spatial and temporal scales, forcing levels (we use simulations under Representative Concentration Pathway 8.5) and two models considered here as our proof of concept, it appears that an ensemble size of 20 or 25 members can provide estimates of the forced component for the extreme metrics considered that remain within small absolute and percentage errors. Additional members beyond 20 or 25 add only marginal precision to the estimate, and this remains true when statistical inference through extreme value analysis is used. We then ask about the ensemble size required to estimate the ensemble variance (a measure of internal variability) along the length of the simulation and – importantly – about the ensemble size required to detect significant changes in such variance along the simulation with increased external forcings. Using the F test, we find that estimates on the basis of only 5 or 10 ensemble members accurately represent the full ensemble variance even when the analysis is conducted at the grid-point scale. The detection of changes in the variance when comparing different times along the simulation, especially for the precipitation-based metrics, requires larger sizes but not larger than 15 or 20 members. While we recognize that there will always exist applications and metric definitions requiring larger statistical power and therefore ensemble sizes, our results suggest that for a wide range of analysis targets and scales an effective estimate of both forced component and internal variability can be achieved with sizes below 30 members. This invites consideration of the possibility of exploring additional sources of uncertainty, such as physics parameter settings, when designing ensemble simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Particle transport in Markov stochastic mixtures with spatial gradients

Markov geometries are a prototype class of stochastic media that are widely used to model complex disordered systems. In a series of recent works, we have shown that spatially homogeneous (but possibly non-isotropic) Poisson tessellations can be conveniently used in order to generate three-dimensional realizations of Markov media and thus characterize the features of particle transport e.g. in fragmented fuel elements following severe nuclear accidents or Rayleigh-Taylor turbulent mixing for inertial confinement fusion. In this paper we show that Poisson tessellations can be extended to take into account the presence of spatial gradients, which commonly occur in real-world applications due to material stratification. For this purpose, we will provide an explicit construction of spatially-non-homogeneous Poisson tessellations, fully characterize their statistical properties, and finally illustrate the behaviour of particle transport in such random media. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

The Helicity Sign of Flux Transfer Event Flux Ropes and Its Relationship to the Guide Field and Hall Physics in Magnetic Reconnection at the Magnetopause

Flux Transfer Events (FTEs) are transient magnetic flux ropes typically found at the Earth's magnetopause on the dayside. While it is known that FTEs are generated by magnetic reconnection, it remains unclear how the details of magnetic reconnection controls their properties. A recent study showed that the helicity sign of FTEs positively correlates with the east-west (B y ) component of the Interplanetary Magnetic Field (IMF). With data from the Cluster and Magnetospheric Multiscale missions, we performed a statistical study of 166 quasi force-free FTEs. We focus on their helicity sign and possible association with upstream solar wind conditions and local magnetic reconnection properties. Using both in situ data and magnetic shear modeling, we find that FTEs whose helicity sign corresponds to the IMF B y are associated with moderate magnetic shears while those that do not correspond to the IMF B y are associated with higher magnetic shears. While uncertainty in IMF propagation to the magnetopause may lead to randomness in the determination of the flux rope core field and helicity, we rather propose that for small IMF B y , which corresponds to high shear and low guide field, the Hall pattern of magnetic reconnection determines the FTE core field and helicity sign. In that context we explain how the temporal sequence of multiple X-line formation and the reconnection rate are important in determining the flux rope helicity sign. This work highlights a fundamental connection between kinetic processes at work in magnetic reconnection and the macroscale structure of FTEs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Feature Detection

Focal Area(s): This proposal aims to develop and evaluate statistical models and machine learning algorithms for detecting and tracking features in spatiotemporal remotely sensed data with uncertainty quantification. We focus a particular application on the detection of sea ice leads and ridges in the Arctic and use these key sea ice features for model calibration and to gain insight into the physics of sea ice thermodynamics and deformation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Unrecognized Sources of Uncertainties ( USU ) in Experimental Nuclear Data

Evaluated nuclear data uncertainties reported in the literature or archived in data libraries are often perceived as unrealistic, most often because they are thought to be too small. The impact of this issue in applied nuclear science has been discussed widely in recent years. Commonly suggested causes are: poor estimates of specific error components, neglect of uncertainty correlations, and overlooked known error sources. However, instances have been reported where very careful, objective assessments of all known error sources have been made with realistic error magnitudes and correlations provided, yet the resulting evaluated uncertainties still appear to be inconsistent with observed scatter of predicted mean values. These discrepancies might be attributed to significant unrecognized sources of uncertainty ( USU ) that limit the accuracy to which these physical quantities can be determined. The objective of our work reported herein has been to develop qualitative and quantitative procedures for revealing and including USU estimates in nuclear data evaluations involving experimental input data. This paper identifies several specific clues that can be explored by evaluators in identifying the existence of USU . It then describes numerical procedures we have introduced to generate quantitative estimates of USU magnitudes. Key requirements for these procedures to be viable are that sufficient numbers of data points be available, for statistical reasons, and that additional supporting information about the measurements be provided by the experimenters. Several realistic examples are described here to illustrate these procedures and demonstrate their outcomes and limitations. Our work strongly supports the view that USU is an important issue in nuclear data evaluation, with significant consequences for applications, and that this topic warrants further investigation by the nuclear science community.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A hybrid deep neural operator/finite element method for ice-sheet modeling

One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work we develop a hybrid approach to approximate existing ice sheet models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while we retain a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model, compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier and can therefore be used for generating samples for uncertainty quantification. Further, we study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Evidence of Sub-MeV EMIC-Driven Trapped Electron Flux Dropouts From GPS Observations

We report that for many years, it was believed that resonant interactions between electromagnetic ion cyclotron (EMIC) waves and radiation belt electrons were restricted to electron energies >1–2 MeV. In recent years, however, a growing body of experimental evidence has shown that EMIC waves can cause the scattering loss of electrons down to sub-MeV energies. Using measurements of trapped electron flux from the Global Positioning System satellite constellation, we investigate the ability of EMIC waves to cause significant depletions of radiation belt electron populations between 4 ≤ L* ≤ 5. For the first time, we present statistical evidence demonstrating global decreases in sub-MeV trapped electron flux in response to EMIC wave activity. Although we find that electron losses extend down to sub-MeV energies, we also show strong statistical support for the ability of EMIC waves to preferentially cause substantial depletions of ultra-relativistic electrons in the radiation belts.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Geometric Tail Approximation for Reliability and Survivability

A common problem in developing high-reliability systems is estimating the reliability for a population of components that cannot be 100% tested. The radiation survivability of a population of components is often estimated by testing a very small sample to some multiple of the required specification level, known as an overtest. Given a successful test with a sufficient overtest margin, the population of components is assumed to have the required survivability or radiation reliability. However, no mathematical justification for such claims has been crafted without making aggressive assumptions regarding the statistics of the unknown distribution. Here we illustrate a new approach that leverages geometric bounding arguments founded on relatively modest distribution assumptions to produce conservative estimates of component reliability.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Designing ITER motional Stark effect line shift (MSE-LS) spectrometers

As a part of ITER beam aided diagnostics, the design of Motional Stark Effect (MSE) diagnostic observing the emission from the Balmer-α line is underway. The physics of Stark splitting shows that the Stark manifold is polarization dependent, and the energy splitting results in a line shift proportional to the electric field. Due to the challenges of maintaining the calibration of the plasma facing mirrors in ITER, the conventional MSE polarimetry measurement technique is replaced with a spectral approach that is deemed more favorable in the ITER environment. The MSE line shift (LS) diagnostic is designed to quantify the Lorentz electric field magnitude by measuring the Stark manifold using visible spectroscopy. In the presence of large magnetic fields and high energy heating beams of 1 MeV, the expected Stark splitting is much larger than in typical devices. The MSE-LS design has unique challenges requiring careful consideration and modeling of its viewing geometry and photon budget. The MSE-LS approach on ITER is promising but has stringent demands on the allowable errors for the statistical and systematic fitting uncertainties. In this study, a full system model and numerical simulations of data for each sightline are completed. For a range of optical transmission fractions, photon noise analysis is conducted to determine the statistical uncertainties. This provides guidance on the spectrometer throughput, dispersion at the detector, optics, and other design choices. A conceptual design of a high throughput spectrometer with a volume phase transmission grating is presented.

Instruments & Instrumentation↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

State of the art astronomical simulations have provided datasets which enabled the training of novel deep learning techniques for constraining cosmological parameters. However, differences in subgrid physics implementation and numerical approximations among simulation suites lead to differences in simulated datasets, which pose a hard challenge when trying to generalize across diverse data domains and ultimately when applying models to observational data. Recent work reveals deep learning algorithms are able to extract more information from complex cosmological simulations than summary statistics like power spectra. We introduce Domain Adaptive Graph Neural Networks (DA-GNNs), trained on CAMELS data, inspired by CosmoGraphNet (Villanueva-Domingo et al 2023). By utilizing GNNs, we can capitalize on their capacity to capture both astrophysical and topological features of galaxy distributions. Mixing these capabilities with domain adaptation techniques such as Maximum Mean Discrepancy (MMD), which enable extraction of domain-invariant features, our framework demonstrates enhanced accuracy and robustness. We present experimental results, including the alignment of distributions across domains through data visualization. These findings suggest that DA-GNNs are an efficient way of extracting domain independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Maximizing the impact of the Vera Rubin Observatory LSST, and DESI and CMB-S4 experiments in constraining gravity and dark sector physics

The work in this award has addressed our objectives on a number of fronts: Current graduate student Chris Wilson and Bean are determining how cosmic voids can be used to test the properties of gravity, leveraging simulations from DESI collaborator Baojiu Li and refining theoretical Gaussian Streaming Model approaches to predict galaxy dynamics in voids when the cosmology is not GR. This work is still underway, including analyses of the efficacy of void quadrupole correlation statistics and void size functions when studying dark matter halo statistics or the expected galaxy distributions from upcoming galaxy surveys; Current graduate student Yulin Gong, Bean with collaborators are refining techniques to extract out the kinetic Sunyaev Zel’dovich effect from galaxy cluster survey data and CMB temperature anisotropies, with the intent to further ready this pipeline development in readiness for spectroscopic data from DESI and LSST along with CMB-S4 measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Effects of non-thermal termolecular reactions on wedge-induced oblique detonation waves

The shock-induced combustion ramjet (Shcramjet) based on oblique detonation waves (ODWs) is among the promising choices for hypersonic propulsion systems. An understanding of the ignition, propagation, and stability of ODWs is critical to harnessing their propulsive potential. In such high speed reacting flows, there is a high probability of occurrence of non-thermal reactions due to the presence of non-trivial amounts of highly reactive radicals including H, O and OH apart from O 2 as demonstrated recently [M. P. Burke, S. J. Klippenstein, Nat. Chem. 9 (2017) 1078–1082, Y. Tao, A. W. Jasper, Y. Georgievskii, S. J. Klippenstein, R. Sivaramakrishnan, Proc. Combust. Inst. 38 (2021) 515–522]. The present work focuses on examining the initiation, propagation and structure of oblique detonation waves in stoichiometric H 2 -air mixtures through numerical simulations with and without non-thermal reactivity on a two-dimensional adaptive grid. Non-thermal reactions were included in the macroscopic kinetic model as chemically termolecular reactions facilitated by the H + OH radical-radical recombination and the H + O 2 radical-molecule association reactions. Since, the non-thermal reactions result in a corresponding decrease in the reaction fluxes of the incipient recombination/association reactions, an additional simulation was performed by applying corrections to the respective incipient recombination/association rate constants using the methodology demonstrated by Tao et al. [Y. Tao, A. W. Jasper, Y. Georgievskii, S. J. Klippenstein, R. Sivaramakrishnan, Proc. Combust. Inst. 38 (2021) 515–522]. Results show that, under ODWE relevant conditions, non-thermal reactivity fundamentally alters the induction length, intensity as well as the structure of the ODW. Specifically, it is found that non-thermal reactivity leads to a noticeable reduction in initiation length and a simultaneous increase in instantaneous peak heat release rate and the degree of unsteadiness of the ODW. Finally, statistical analysis of key thermo-chemical variables is performed to elucidate the important species as well as reactions responsible for the observed variations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗