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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Second order classical perturbation theory for the sticking probability of heavy atoms scattered on surfaces

A second order classical perturbation theory is developed to calculate the sticking probability of a particle scattered from an uncorrugated thermal surface. An analytic expression for the temperature dependent energy loss of the particle to the surface is derived by employing a one-dimensional generalized Langevin equation. The surface temperature reduces the energy loss, since the thermal surface transfers energy to the particle. Using a Gaussian energy loss kernel and the multiple collision theory of Fan and Manson [J. Chem. Phys. 130, 064703 (2009)], enables the determination of the fraction of particles trapped on the surface after subsequent momentum reversals of the colliding particle. This then leads to an estimate of the trapping probability. The theory is tested for the model scattering of Ar on a LiF(100) surface. Comparison with numerical simulations shows excellent agreement of the analytical theory with simulations, provided that the energy loss is determined by the second order perturbation theory.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Bayesian probability of agreement for comparing the similarity of response surfaces

For a variety of linear and generalized linear models, there may be interest in evaluating whether the expected response values in two different populations are sufficiently similar to be considered practically equivalent. The probability of agreement has been introduced as a strategy for quantifying the similarity between two groups. We propose a Bayesian version of the probability of agreement to quantify the similarity between linear and generalized linear response surfaces. The proposed methodology is based on Markov chain Monte Carlo estimation and it allows for an intuitive interpretation based directly on population parameters. As demonstrated, the methodology can be flexibly applied to a variety of different models. We illustrate its use with three examples for which the response depends on predictor variables through a linear, logistic, or Poisson regression model. The computation associated with this approach has been automated with a freely available R Shiny app.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Vectorization techniques for probability distribution functions using VecCore

Probability distribution functions (PDFs) are very used in modeling random processes and physics simulations. Improving the performance of algorithms that generate many random numbers under complex PDFs is often a very challenging task when methods as direct functions are not available. In this work we present general strategies on how to vectorize some PDFs using VecCore library. We show the results for the Exponential, Gaussian, discrete Poisson and Gamma probability distributions.

Chaparro Amaro, Oscar R.↗
DOE OSTI2022

Photon detection probability prediction using one-dimensional generative neural network

Abstract Photon detection is important for liquid argon detectors for direct dark matter searches or neutrino property measurements. Precise simulation of photon transport is widely used to understand the probability of photon detection in liquid argon detectors. Traditional photon transport simulation, which tracks every photon using the Geant4 simulation toolkit, is a major computational challenge for kilo-tonne-scale liquid argon detectors and GeV-level energy depositions. In this work, we propose a one-dimensional generative model which efficiently generates features using an O u t e r P r o d u c t -layer. This model bypasses photon transport simulation and predicts the number of photons detected by particular photon detectors at the same level of detail as the Geant4 simulation. The application to simulating photon detection systems in kilo-tonne-scale liquid argon detectors demonstrates this novel generative model is able to reproduce Geant4 simulation with good accuracy and 20 to 50 times faster. This generative model can be used to quickly predict photon detection probability in huge liquid argon detectors like ProtoDUNE or DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro 1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Mapping variations of redshift distributions with probability integral transforms

We present a method for mapping variations between probability distribution functions and apply this method within the context of measuring galaxy redshift distributions from imaging survey data. This method, which we name PITPZ for the probability integral transformations it relies on, uses a difference in curves between distribution functions in an ensemble as a transformation to apply to another distribution function, thus transferring the variation in the ensemble to the latter distribution function. This procedure is broadly applicable to the problem of uncertainty propagation. In the context of redshift distributions, for example, the uncertainty contribution due to certain effects can be studied effectively only in simulations, thus necessitating a transfer of variation measured in simulations to the redshift distributions measured from data. We illustrate the use of PITPZ by using the method to propagate photometric calibration uncertainty to redshift distributions of the Dark Energy Survey Year 3 weak lensing source galaxies. For this test case, we find that PITPZ yields a lensing amplitude uncertainty estimate due to photometric calibration error within 1 per cent of the truth, compared to as much as a 30 per cent underestimate when using traditional methods.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The most probable host of CHIME FRB 190425A, associated with binary neutron star merger GW190425, and a late-time transient search

The identification and localization of fast radio bursts (FRBs) to their host galaxies have revealed important details about the progenitors of these mysterious, millisecond-long bursts of coherent radio emission. In this work, we study the most probable host galaxy of the apparently non-repeating CHIME/FRB event FRB 20190425A – a particularly high-luminosity, low-dispersion measure event that was demonstrated in a recent paper to be temporally and spatially coincident with the LIGO-Virgo-KAGRA binary neutron star merger GW190425, suggesting an astrophysical association ( p -value 0.0052). In this paper, we remain agnostic to this result, and we confirm UGC10667 as the most probable host galaxy of FRB 20190425A, demonstrating that the host galaxies of low-dispersion measure, one-off CHIME FRBs can be plausibly identified. We then perform multiwavelength observations to characterize the galaxy and search for any afterglow emission associated with the FRB and its putative GW counterpart. We find no radio or optical transient emission in our observations 2.5 yr post-burst. UGC10667 is a spiral galaxy at z ∼ 0.03, dominated by an old stellar population. We find no evidence of a large population of young stars, with nebular emission dominated by star formation at a rate of 1 -2 M $\odot$ yr -1 . While we cannot rule out a young magnetar as the origin of FRB 20190425A, our observations are consistent with an origin in a long delay-time neutron star binary merger.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Testing redMaPPer centring probabilities using galaxy clustering and galaxy–galaxy lensing

Here, galaxy cluster centring is a key issue for precision cosmology studies using galaxy surveys. Mis-identification of central galaxies causes systematics in various studies such as cluster lensing, satellite kinematics, and galaxy clustering. The red-sequence Matched-filter Probabilistic Percolation (redMaPPer) estimates the probability that each member galaxy is central from photometric information rather than specifying one central galaxy. The redMaPPer estimates can be used for calibrating the off-centring effect, however, the centring algorithm has not previously been well-tested. We test the centring probabilities of redMaPPer cluster catalogue using the projected cross-correlation between redMaPPer clusters with photometric red galaxies and galaxy–galaxy lensing. We focus on the subsample of redMaPPer clusters in which the redMaPPer central galaxies (RMCGs) are not the brightest member galaxies (BMEM) and both of them have spectroscopic redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Reconstructing probability distributions with Gaussian processes

Modern cosmological analyses constrain physical parameters using Markov Chain Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques are computationally expensive to run and require up to thousands of CPU hours to complete. Here we present a method for reconstructing the log-probability distributions of completed experiments from an existing chain (or any set of posterior samples). Here, the reconstruction is performed using Gaussian process regression for interpolating the log-probability. This allows for easy resampling, importance sampling, marginalization, testing different samplers, investigating chain convergence, and other operations. As an example use case, we reconstruct the posterior distribution of the most recent Planck 2018 analysis. We then resample the posterior, and generate a new chain with 40 times as many points in only 30 min. Our likelihood reconstruction tool is made publicly available online.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Intrinsic nonlocality of spin- and polarization-resolved probabilities in strong-field quantum electrodynamics

Spin and polarization are central to precision tests of fundamental physics and for interpreting radiation from astrophysical sources and ultraintense laser-matter experiments. Here, focusing on the fundamental process of nonlinear Compton scattering, we demonstrate that a key assumption underlying current strong-field quantum electrodynamics models, i.e., that emission can be treated as an instantaneous random event sampled from a local differential rate, is inconsistent once emission angles, electron spin, and/or photon polarization are resolved. Namely, even in strictly constant and uniform fields , the resulting fully differential distribution is sign indefinite, yielding negative inferred probabilities. The physical reason is that the photon emission probability builds up over a finite length of the electron trajectory, the formation region, during which the electron direction changes by roughly the same small angle that defines the radiation cone. Therefore, we put forward a new method where we integrate over this formation region analytically to obtain a physically consistent electron spin and photon polarization model. We show that the implementation of our model is compatible with existing Monte Carlo and particle-in-cell workflows. Simulations of a GeV-class electron-laser collision accessible at current petawatt facilities and of emission in a pulsarlike magnetic field are shown to reveal spin and polarization patterns that differ even qualitatively from state-of-the-art local models. In particular, our new model predicts substantial angle-dependent circular photon polarization where the well-known collinear-emission approach yields none, and a pronounced helicity bias in the recoiling electrons absent from current predictions. These findings have direct implications for upcoming strong-field QED experiments and for interpreting polarized radiation from extreme astrophysical environments.

astrophysical electromagnetic fields↗
DOE OSTI2022

Hot and dense matter equation of state probability distributions for astrophysical simulations

We add an ensemble of nuclei to the equation of state for homogeneous nucleonic matter to generate a new set of models suitable for astrophysical simulations of core-collapse supernovae and neutron star mergers. We implement empirical constraints from (i) nuclear mass measurements, (ii) proton-proton scattering phase shifts, and (iii) neutron star observations. Our model is also guided by microscopic many-body theory calculations based on realistic nuclear forces, including the zero-temperature neutron matter equation of state from quantum Monte Carlo simulations and thermal contributions to the free energy from finite-temperature many-body perturbation theory. We ensure that the parameters of our model can be varied while preserving thermodynamic consistency and the connection to experimental or observational data, thus providing a probability distribution of the astrophysical hot and dense matter equation of state. Furthermore, we compare our results with those obtained from other available equations of state. While our probability distributions indeed represent a large number of possible equations of state, we cannot yet claim to have fully explored all of the uncertainties, especially with regard to the structure of nuclei in the hot and dense medium.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

β -decay half-lives and β -delayed neutron emission probabilities for several isotopes of Au, Hg, Tl, Pb, and Bi, beyond N = 126

Background: There have been measurements on roughly 230 nuclei that are β-delayed neutron emitters. They range from 8 He up to 150 La . Apart from 210 Tl, with a branching ratio of only 0.007%, no other neutron emitter has been measured beyond A = 150 . Therefore, new data are needed, particularly in the region of heavy nuclei around N = 126 , in order to guide theoretical models and help understand the formation of the third r-process peak at A ~ 195. Purpose: To measure both β-decay half-lives and neutron branching ratios of several neutron-rich Au, Hg, Tl, Pb, and Bi isotopes beyond N = 126 . Method: Ions of interest were produced by fragmentation of a 238 U beam, selected and identified via the GSI-FRS fragment separator. A stack of segmented silicon detectors (SIMBA) was used to measure ion implants and β decays. An array of 30 3 He tubes embedded in a polyethylene matrix (BELEN) was used to detect neutrons with high efficiency and selectivity. A self-triggered digital system is employed to acquire data and to enable time correlations. The latter were analyzed with an analytical model and results for the half-lives and neutron-branching ratios were derived by using the binned maximum-likelihood method. Results: Twenty new β-decay half-lives are reported for 204-206 Au, 208 – 211 Hg, 211 – 216 Tl , 215 – 218 Pb, and 218 – 220 Bi, nine of them for the first time. Neutron emission probabilities are reported for 210, 211 Hg and 211 – 216 Tl . Conclusions: The new β-decay half-lives are in good agreement with previous measurements on nuclei in this region. Lastly, the measured neutron emission probabilities are comparable to or smaller than values predicted by global models such as relativistic Hartree Bogoliubov plus the relativistic quasi-particle random phase approximation (RHB + RQRPA).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Fast and accurate algorithm for calculating long-baseline neutrino oscillation probabilities with matter effects

Neutrino oscillation experiments will be entering the precision era in the next decade with the advent of high statistics experiments like DUNE, HK, and JUNO. Correctly estimating the confidence intervals from data for the oscillation parameters requires very large Monte Carlo datasets involving calculating the oscillation probabilities in matter many, many times. In this paper, we leverage past work to present a new, fast, precise technique for calculating neutrino oscillation probabilities in matter optimized for long-baseline neutrino oscillations in the Earth’s crust including both accelerator and reactor experiments. For ease of use by theorists and experimentalists, we provide fast ++ and codes . Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

β-Delayed One and Two Neutron Emission Probabilities Southeast of 132 Sn and the Odd-Even Systematics in r-Process Nuclide Abundances

Here, the β-delayed one- and two-neutron emission probabilities (P 1n and P 2n ) of 20 neutron-rich nuclei with N≥82 have been measured at the RIBF facility of the RIKEN Nishina Center. P 1n of 130,131 Ag, 133,134 Cd, 135,136 In, and 138,139 Sn were determined for the first time, and stringent upper limits were placed on P2n for nearly all cases. β-delayed two-neutron emission (β2n) was unambiguously identified in 133 Cd and 135,136 In, and their P 2n were measured. Weak β2n was also detected from 137,138 Sn. Our results highlight the effect of the N=82 and Z=50 shell closures on β-delayed neutron emission probability and provide stringent benchmarks for newly developed macroscopic-microscopic and self-consistent global models with the inclusion of a statistical treatment of neutron and γ emission. The impact of our measurements on r-process nucleosynthesis was studied in a neutron star merger scenario. Our P 1n and P 2n have a direct impact on the odd-even staggering of the final abundance, improving the agreement between calculated and observed Solar System abundances. The odd isotope fraction of Ba in r-process-enhanced (r-II) stars is also better reproduced using our new data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

First Measurement of the Neutron-Emission Probability with a Surrogate Reaction in Inverse Kinematics at a Heavy-Ion Storage Ring

Neutron-induced reaction cross sections of short-lived nuclei are imperative to understand the origin of heavy elements in stellar nucleosynthesis and for societal applications, but their measurement is extremely complicated due to the radioactivity of the targets involved. One way of overcoming this issue is to combine surrogate reactions with the unique possibilities offered by heavy-ion storage rings. In this work, we describe the first surrogate-reaction experiment in inverse kinematics, which we successfully conducted at the Experimental Storage Ring (ESR) of the GSI/FAIR facility, using the 208 Pb⁢(p,p') reaction as a surrogate for neutron capture on 207 Pb. Thanks to the outstanding detection efficiencies possible at the ESR, we were able to measure for the first time the neutron-emission probability as a function of the excitation energy of 208 Pb. We have used this probability to select different descriptions of the γ -ray strength function and nuclear level density, and provide reliable results for the neutron-induced radiative capture cross section of 207 Pb at energies for which no experimental data exist.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Propagating Mixed Uncertainties in Cyber Attacker Payoffs: Exploration of Two-Phase Monte Carlo Sampling and Probability Bounds Analysis

Securing cyber-systems on a continual basis against a multitude of adverse events is a challenging undertaking. Game-theoretic approaches, that model actions of strategic decision-makers, are increasingly being applied to address cybersecurity resource allocation challenges. Such game-based models account for multiple player actions and represent cyber attacker payoffs mostly as point utility estimates. Since a cyber-attacker’s payoff generation mechanism is largely unknown, appropriate representation and propagation of uncertainty is a critical task. In this paper we expand on prior work and focus on operationalizing the probabilistic uncertainty quantification framework, for a notional cyber system, through: 1) representation of uncertain attacker and system-related modeling variables as probability distributions and mathematical intervals, and 2) exploration of uncertainty propagation techniques including two-phase Monte Carlo sampling and probability bounds analysis.

cyber security↗
DOE OSTI2024

Cascading Transformer Failure Probability Model Under Geomagnetic Disturbances

This paper develops a probabilistic model to assess the cascading failure of transformers in an electric power grid experiencing geomagnetic disturbances caused by a solar storm. We propose a model in which the probability of failure is a function of the intensity of the solar storm, the physical properties of the transformer, the geographical location of the transformer, and the flow of electrical power. We demonstrate the proposed model using the IEEE 14-bus system and several notional solar storms. The model quickly computes the initial and cascading failure probabilities of the transformers in the system as a first step towards quantifying the risks posed by future solar storms.

Shukla, Pratishtha↗
DOE OSTI2015

SU-E-T-144: Bayesian Inference of Local Relapse Data Using a Poisson-Based Tumour Control Probability Model

Purpose: The purpose of this project is to develop a robust method of parameter estimation for a Poisson-based TCP model using Bayesian inference. Methods: Bayesian inference was performed using the PyMC3 probabilistic programming framework written in Python. A Poisson-based TCP regression model that accounts for clonogen proliferation was fit to observed rates of local relapse as a function of equivalent dose in 2 Gy fractions for a population of 623 stage-I non-small-cell lung cancer patients. The Slice Markov Chain Monte Carlo sampling algorithm was used to sample the posterior distributions, and was initiated using the maximum of the posterior distributions found by optimization. The calculation of TCP with each sample step required integration over the free parameter α, which was performed using an adaptive 24-point Gauss-Legendre quadrature. Convergence was verified via inspection of the trace plot and posterior distribution for each of the fit parameters, as well as with comparisons of the most probable parameter values with their respective maximum likelihood estimates. Results: Posterior distributions for α, the standard deviation of α (σ), the average tumour cell-doubling time (Td), and the repopulation delay time (Tk), were generated assuming α/β = 10 Gy, and a fixed clonogen density of 10{sup 7} cm−{sup 3}. Posterior predictive plots generated from samples from these posterior distributions are in excellent agreement with the observed rates of local relapse used in the Bayesian inference. The most probable values of the model parameters also agree well with maximum likelihood estimates. Conclusion: A robust method of performing Bayesian inference of TCP data using a complex TCP model has been established.

60 APPLIED LIFE SCIENCES↗