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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Parameter Estimation in Astronomy with Poisson-Distributed Data: The (CHI)2(gamma) Statistic - 1

Applying the standard weighted mean formula, [Sigma (sub i)n(sub i)ssigma(sub i, sup -2)], to determine the weighted mean of data, n(sub i), drawn from a Poisson distribution, will, on average, underestimate the true mean by approx. 1 for all true mean values larger than approx.3 when the common assumption is made that the error of the i th observation is sigma(sub i) = max square root of n(sub i), 1).This small, but statistically significant offset, explains the long-known observation that chi-square minimization techniques which use the modified Neyman'chi(sub 2) statistic, chi(sup 2, sub N) equivalent Sigma(sub i)((n(sub i) - y(sub i)(exp 2)) / max(n(sub i), 1), to compare Poisson - distributed data with model values, y(sub i), will typically predict a total number of counts that underestimates the true total by about 1 count per bin. Based on my finding that weighted mean of data drawn from a Poisson distribution can be determined using the formula [Sigma(sub i)[n(sub i) + min(n(sub i), 1)](n(sub i) + 1)(exp -1)] / [Sigma(sub i)(n(sub i) + 1)(exp -1))], I propose that a new chi(sub 2) statistic, chi(sup 2, sub gamma) equivalent, should always be used to analyze Poisson- distributed data in preference to the modified Neyman's chi(exp 2) statistic. I demonstrated the power and usefulness of,chi(sub gamma, sup 2) minimization by using two statistical fitting techniques and five chi(exp 2) statistics to analyze simulated X-ray power - low 15 - channel spectra with large and small counts per bin. I show that chi(sub gamma, sup 2) minimization with the Levenberg - Marquardt or Powell's method can produce excellent results (mean slope errors approx. less than 3%) with spectra having as few as 25 total counts.

Mighell, Kenneth J.↗
NASA NTRS1985

Gamma-ray burst statistics - Guidance or deception?

An evaluation is made of efforts to statistically ascertain the distance scale of gamma-ray bursts. The Venera 11 and 12 KONUS experiment peak count flux P sampling is noted to be incomplete for the longest and shortest bursts. This may be due to the effect being either a real physical limit to the burst population, or an artifact. Current data favor the latter explanation. Where N is the annual rate of bursts with a fluence exceeding S ergs/sq cm, it is shown that an unbounded, isotropic, homogeneous burst source distribution must have d log N/d log P of less than -3/2, for all P independent of any intrinsic P distribution or the existence and relative distance of any separate burst classes. The KONUS data are consistent with earlier conclusions that bursts are distributed on a large galactic scale.

Jennings, M. C.↗
NASA NTRS2014

A Statistical Treatment of Bioassay Pour Fractions

The binomial probability distribution is used to treat the statistics of a microbiological sample that is split into two parts, with only one part evaluated for spore count. One wishes to estimate the total number of spores in the sample based on the counts obtained from the part that is evaluated (pour fraction). Formally, the binomial distribution is recharacterized as a function of the observed counts (successes), with the total number (trials) an unknown. The pour fraction is the probability of success per spore (trial). This distribution must be renormalized in terms of the total number. Finally, the new renormalized distribution is integrated and mathematically inverted to yield the maximum estimate of the total number as a function of a desired level of confidence ( P(<n)=LOC ). Selected results of the indicated numerical calculations are presented. For LOC=0.5, or the likely value, the estimates differ little from the usual calculation: the number of spores counted divided by the pour fraction. The extension to recovery efficiency corrections is also presented. Now the product of recovery efficiency and pour fraction may be small enough that the likely value may be much larger than the usual calculation: the number of spores divided by that product. The use of this analysis would not be limited to microbiological data.

000000↗
DOE OSTI2022

Increases in Future AR Count and Size: Overview of the ARTMIP Tier 2 CMIP5/6 Experiment

Abstract The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi‐model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA‐2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Increases in Future AR Count and Size: Overview of 2 the ARTMIP Tier 2 CMIP5/6 Experiment

The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi-model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA-2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design. Plain Language Summary: Atmospheric rivers (ARs) are a type of weather pattern known to be important for moving water vapor from the warm, moist tropics to the cool, dry polar regions; when they reach midlatitudes in the winter time, they are commonly associated with heavy precipitation. Recent studies that assess the impacts of global climate change on ARs tend to agree that there will be more ARs in a warmer climate, and that ARs will tend to be more extreme. However, it has been increasingly recognized by the AR research community that these results may depend on the method used to identify ARs and the choice of climate model. This study reports results from a controlled experiment, involving an international research community, that aims to show how different AR identification methods and climate models might impact our scientific understanding of ARs in the future. This experiment shows that there will likely be more ARs in the future, and that ARs will generally have a larger spatial footprint. This experiment also shows that uncertainty in these results are large, with the uncertainty from AR identification methods outweighing that of climate models. Future efforts to better understand the physics of ARs may help us reduce this uncertainty.

T A OBrien↗
NASA NTRS2012

Blocking Losses With a Photon Counter

It was not known how to assess accurately losses in a communications link due to photodetector blocking, a phenomenon wherein a detector is rendered inactive for a short time after the detection of a photon. When used to detect a communications signal, blocking leads to losses relative to an ideal detector, which may be measured as a reduction in the communications rate for a given received signal power, or an increase in the signal power required to support the same communications rate. This work involved characterizing blocking losses for single detectors and arrays of detectors. Blocking may be mitigated by spreading the signal intensity over an array of detectors, reducing the count rate on any one detector. A simple approximation was made to the blocking loss as a function of the probability that a detector is unblocked at a given time, essentially treating the blocking probability as a scaling of the detection efficiency. An exact statistical characterization was derived for a single detector, and an approximation for multiple detectors. This allowed derivation of several accurate approximations to the loss. Methods were also derived to account for a rise time in recovery, and non-uniform illumination due to diffraction and atmospheric distortion of the phase front. It was assumed that the communications signal is intensity modulated and received by an array of photon-counting photodetectors. For the purpose of this analysis, it was assumed that the detectors are ideal, in that they produce a signal that allows one to reproduce the arrival times of electrons, produced either as photoelectrons or from dark noise, exactly. For single detectors, the performance of the maximum-likelihood (ML) receiver in blocking is illustrated, as well as a maximum-count (MC) receiver, that, when receiving a pulse-position-modulated (PPM) signal, selects the symbol corresponding to the slot with the largest electron count. Whereas the MC receiver saturates at high count rates, the ML receiver may not. The loss in capacity, symbol-error-rate (SER), and count-rate were numerically computed. It was shown that the capacity and symbol-error-rate losses track, whereas the count-rate loss does not generally reflect the SER or capacity loss, as the slot-statistics at the detector output are no longer Poisson. It is also shown that the MC receiver loss may be accurately predicted for dead times on the order of a slot.

Moision, Burce E.↗
NASA NTRSDate not supplied

QuantifyML: How good is my machine learning model?

This paper presents an approach, QuantifyML, which employs model counting to assess the learnability and robustness of machine learning models. Typically the efficacy of machine learning models is determined by computing their accuracy statistically on test data sets. However, this may be misleading, if the test data is not representative of the problem that is being studied. Further, two different models may have the same accuracy on a given data set, measured statistically, but may be very different in their behavior on unseen data. Also, models with high accuracy could have poor adversarial robustness. In QuantifyML, our goal is to precisely quantify the extent to which machine learning models have learned and generalized from the given data. In QuantifyML, a trained model is translated into a C program, which is fed to the CBMC model checking tool to produce a formula in Conjunctive Normal Form (CNF), which in turn is analyzed with state-of-the-art model counters to efficiently obtain precise counts w.r.t different outputs. QuantifyML enables i) evaluating the learnability of models by comparing the counts for the outputs to ground truth, expressed as logical predicates (if available), ii) comparing the performance of different models that may be built with different machine learning algorithms (e.g., decision-trees vs. neural networks), and iii) quantifying the robustness of trained models around given inputs. Our evaluation demonstrates these applications of QuantifyML on decision trees and neural networks trained to learn relational properties of graphs, for which we know the ground truth, and to perform image classification, for which we do not have the ground truth, but we can quantify local robustness.

Deep Neural Networks↗
DOE OSTI2026

Rapid neutron and gamma-ray source localization using machine learning

Rapid localization of radiation sources is critical for applications including nuclear emergency response, safeguards, and security. However, conventional imaging systems such as neutron scatter cameras and Compton cameras depend on rare coincidence events, which often result in long acquisition times. In this work, we address the challenge of rapid source localization by developing a machine learning approach to predict the direction of a single radiation source using only count rates from an array of neutron and gamma-ray detectors. The proposed model is a fully connected neural network (FCNN) trained using Monte Carlo simulation data from a 252 Cf source. The model hyperparameters are optimized with a small set of routine 252 Cf measurements. We benchmarked the performance of the trained and optimized machine learning model using additional 252 Cf , 137 Cs , and PuBe measurements under laboratory conditions with varying source-detector configurations. For these measurements, the machine learning model achieved a mean localization error smaller than 30° with 3 x 10 3 system counts, corresponding to 8 s measurement time for the imaging system used in this work. In this low-statistics regime, the method outperformed traditional scatter-based imaging by more than 75% in localization accuracy for the evaluated measurement configurations. These results demonstrate that a machine learning-based approach can significantly reduce the time required for accurate single-source localization, providing a robust and computationally efficient alternative to traditional imaging systems in time-critical nuclear security and emergency response scenarios.

Gamma-ray imaging↗
DOE OSTI2023

Beyond the 3rd moment: a practical study of using lensing convergence CDFs for cosmology with DES Y3

ABSTRACT Widefield surveys probe clustered scalar fields – such as galaxy counts, lensing potential, etc. – which are sensitive to different cosmological and astrophysical processes. Constraining such processes depends on the statistics that summarize the field. We explore the cumulative distribution function (CDF) as a summary of the galaxy lensing convergence field. Using a suite of N-body light-cone simulations, we show the CDFs’ constraining power is modestly better than the second and third moments, as CDFs approximately capture information from all moments. We study the practical aspects of applying CDFs to data, using the Dark Energy Survey (DES Y3) data as an example, and compute the impact of different systematics on the CDFs. The contributions from the point spread function and reduced shear approximation are $\lesssim 1~{{\ \rm per\ cent}}$ of the total signal. Source clustering effects and baryon imprints contribute 1–10 per cent. Enforcing scale cuts to limit systematics-driven biases in parameter constraints degrade these constraints a noticeable amount, and this degradation is similar for the CDFs and the moments. We detect correlations between the observed convergence field and the shape noise field at 13σ. The non-Gaussian correlations in the noise field must be modelled accurately to use the CDFs, or other statistics sensitive to all moments, as a rigorous cosmology tool.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1983

Transition probability of the Si III 189.2-nm intersystem line

Measurement of the lifetime of the metastable 3s3p(3)P(0)1 level of Si(2+) (Si III), which decays by photon emission at 189.2 nm to the 3s2(1)S0 state, is reported. The data were taken from spontaneous emission from metastable Si III stored in an RF ion trap. The Si III ions were produced through electron bombardment of SiH4 and SiF4 at pressures of 1/100,000,000-1/10,000,000 Torr. A photomultiplier was employed to count the photon emissions from the transitions. A total of 11 decay curves were generated for analysis, with Poisson statistics used to set the uncertainties at within 8 pct. Significant systematic effects were controlled, and the lifetime was found to be within 3.6 microsec of 59.9 microsec. The method used is concluded valid for determining the lifetimes of metastable levels of low-Z ions with low charge, and thereby the transition probabilities.

Kwong, H. S.↗
NASA NTRS2003

First Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Foreground Emission

The WMAP mission has mapped the full sky to determine the geometry, content, and evolution of the universe. Full sky maps are made in five microwave frequency bands to separate the temperature anisotropy of the cosmic microwave background (CMB) from foreground emission, including diffuse Galactic emission and Galactic and extragalactic point sources. We define masks that excise regions of high foreground emission, so CMB analyses can became out with minimal foreground contamination. We also present maps and spectra of the individual emission components, leading to an improved understanding of Galactic astrophysical processes. The effectiveness of template fits to remove foreground emission from the WMAP data is also examined. These efforts result in a CMB map with minimal contamination and a demonstration that the WMAP CMB power spectrum is insensitive to residual foreground emission. We use a Maximum Entropy Method to construct a model of the Galactic emission components. The observed total Galactic emission matches the model to less than 1% and the individual model components are accurate to a few percent. We find that the Milky Way resembles other normal spiral galaxies between 408 MHz and 23 GHz, with a synchrotron spectral index that is flattest (beta(sub s) approx. -2.5) near star-forming regions, especially in the plane, and steepest (beta(sub s) approx. -3) in the halo. This is consistent with a picture of relativistic cosmic ray electron generation in star-forming regions and diffusion and convection within the plane. The significant synchrotron index steepening out of the plane suggests a diffusion process in which the halo electrons are trapped in the Galactic potential long enough to suffer synchrotron and inverse Compton energy losses and hence a spectral steepening. The synchrotron index is steeper in the WMAP bands than in lower frequency radio surveys, with a spectral break near 20 GHz to beta(sub s) less than -3. The modeled thermal dust spectral index is also steep in the WMAP bands, with beta(sub d) approx. = 2.2. Our model is driven to these conclusions by the low level of total foreground contamination at approx. 60 GHz. Microwave and Ha measurements of the ionized gas agree well with one another at about the expected levels. Spinning dust emission is limited to less than 5% of the Ka-band foreground emission. A catalog of 208 point sources is presented. The reliability of the catalog is 98%, i.e., we expect five of the 208 sources to be statistically spurious. The mean spectral index of the point sources is alpha approx. 0(beta approx. -2). Derived source counts suggest a contribution to the anisotropy power from unresolved sources of (15.0 +/- 1.4) x 10(exp -3)micro sq K sr at Q-band and negligible levels at V-band and W-band. The Sunyaev-Zeldovich effect is shown to be a negligible "contamination" to the maps.

Bennett, C. L.↗
NASA NTRS2015

Relationships Between Excessive Heat and Daily Mortality over the Coterminous U.S

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. Using National Land Data Assimilation System (NLDAS) meteorological reanalysis data, we have developed several measures of extreme heat to enable assessments of the impacts of heat on public health over the coterminous U.S. These measures include daily maximum and minimum air temperatures, daily maximum heat indices and a new heat stress variable called Net Daily Heat Stress (NDHS) that gives an integrated measure of heat stress (and relief) over the course of a day. All output has been created on the NLDAS 1/8 degree (approximately 12 km) grid and aggregated to the county level, which is the preferred geographic scale of analysis for public health researchers. County-level statistics have been made available through the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. We have examined the relationship between excessive heat events, as defined in eight different ways from the various daily heat metrics, and heat-related and all-cause mortality defined in CDC's National Center for Health Statistics 'Multiple Causes of Death 1999-2010' dataset. To do this, we linked daily, county-level heat mortality counts with EHE occurrence based on each of the eight EHE definitions by region and nationally for the period 1999-2010. The objectives of this analysis are to determine (1) whether heat-related deaths can be clearly tied to excessive heat events, (2) what time lags are critical for predicting heat-related deaths, and (3) which of the heat metrics correlates best with mortality in each US region. Results show large regional differences in the correlations between heat and mortality. Also, the heat metric that provides the best indicator of mortality varied by region. Results from this research will potentially lead to improvements in our ability to anticipate and mitigate any significant impacts of extreme heat events on health.

heat↗
DOE OSTI2023

Characterizing the expected behavior of non-Poissonian template fitting

We have performed a systematic study of the statistical behavior of non-Poissonian template fitting (NPTF), a method designed to analyze and characterize unresolved point sources in general counts datasets. Here, in this paper, we focus on the properties and characteristics of the Fermi-LAT gamma-ray data set. In particular, we have simulated and analyzed gamma-ray sky maps under varying conditions of exposure, angular resolution, pixel size, energy window, event selection, and source brightness. We describe how these conditions affect the sensitivity of NPTF to the presence of point sources, for inner-galaxy studies of point sources within the Galactic Center excess, and for the simplified case of isotropic emission. We do not find opportunities for major gains in sensitivity from varying these choices, within the range available with current Fermi-LAT data. We provide an analytic estimate of the NPTF sensitivity to point sources for the case of isotropic emission and perfect angular resolution, and find good agreement with our numerical results for that case.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Physical implications of the extrapolation and statistical bootstrap of nucleon structure function ratios F 2 n F 2 p for mirror nuclei He 3 and H 3

A nuclear physics example of statistical bootstrap is used on the MARATHON nucleon structure function ratio data in the quark momentum fraction regions x B → 0 and x B → 1. The extrapolated F 2 ratio as quark momentum fraction x B → 1 is $\frac{F^n_2}{F^p_2}$ → 0.4 ± 0.05 and this value is compared to theoretical predictions. The extrapolated ratio when x B → 0 favors the simple model of isospin symmetry with the complete dominance of sea quarks at low momentum fraction. At high- x B , the proton quark distribution function ratio d/u is derived from the F 2 ratio and found to be d/u → 1/6. Our extrapolated values for both the $\frac{F^n_2}{F^p_2}$ ratio and the d/u parton distribution function ratio are within uncertainties of perturbative QCD values from quark counting, helicity conservation arguments, and a Dyson-Schwinger equation with a contact interaction model. In addition, it is possible to match the statistical bootstrap value to theoretical predictions by allowing two compatible models to act simultaneously in the nucleon wave function. Finally, one such example is nucleon wave functions composed of a linear combination of a quark-diquark state and a three-valence quark correlated state with coefficients that combine to give the extrapolated F 2 ratio at x B = 1.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Challenges and Practical Solutions for Conducting Structural MARSSIM Surveys in Elevated Radon Environments - 20413

The Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) [1] provides comprehensive guidance for the development and implementation of radiological surveys conducted to demonstrate compliance with a dose- or risk-based cleanup goal during final status survey. The MARSSIM methodology follows an integrated strategy combining direct measurements and sampling to assess uniform residual contamination, and scanning surveys to identify localized areas of elevated activity. Building surveys assessing residual surface contamination present unique challenges from reliance on field measurements that may be subject to less than ideal environmental conditions. Additionally, limited characterization and excessive conservatism incorporated into the conceptual site model may result in derived concentration guideline levels (DCGL) and, as a consequence of relatively inefficient field instrumentation, corresponding attributable count rates generally comparable to background. The presence of elevated ambient radon concentrations and associated progeny deposition both exacerbate these intrinsic survey challenges and introduce a series of technical, logistic, and administrative obstacles that can wreak havoc on the statistical legitimacy of each component of the MARSSIM process. Short-lived radon progeny including polonium-218, lead-214, bismuth-214, and polonium-214 produce excessive alpha and beta radiation that may decrease instrument sensitivity through an increase in background count rates, and, at worst, may result in incorrect survey unit failure through the simulation of residual surface activity. Aside from the obvious solution of exhaustive radon mitigation, the primary objectives are to distinguish between radon progeny deposition and actual surface activity, and collect sufficient defensible data. The following is a technical discussion of the consequences of performing building MARSSIM surveys in the presence of elevated ambient radon atmospheres, conceptual examples, and an overview of practical strategies to minimize the effects of radon interference while collecting sufficient defensible data to complete the survey. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1984

The HEAO 1 A-4 catalog of high-energy X-ray sources

Results are reported from an all-sky survey carried out at high X-ray energies (13-180 keV) from August 1977 until January 1979 using data obtained with the UCSD/MIT Hard X-Ray and Low-Energy Gamma-Ray Instrument on the HEAO 1 satellite. Visual displays are presented which indicate qualitatively the location, intensities, and time variability of the detected high-energy X-ray sources. A model-dependent procedure for the quantitative analysis of the sky survey data is described. The results of this procedure are presented in tabular form and include fitted count rates in four broad energy bands for about 70 sources. All sources which were detected at a level of statistical significance of not less than about 6 sigma were clearly evident in the visual displays of sky survey data. The survey is therefore complete, except in regions of source confusion, down to an intensity level of about 1/75 of the Crab Nebula in the 13-80 keV band. Forty-four sources were detected in the 40-80 keV energy band, and 14 were detected in the 80-180 keV band. Although most of the detected sources are galactic, seven are extragalactic.

Levine, A. M.↗
NASA NTRS1990

Blood biochemical and cellular changes during decompression and simulated extravehicular activity

Blood biochemical and cellular parameters were measured in human subjects before and after exposure to a decompression schedule involving 6 h of oxygen prebreathing. The exposure was designed to simulate extravehicular activity for 6 h (subjects performed exercise while exposed to 29.6 kPa). There were no significant differences between blood samples from subjects who were susceptible (n = 11) versus those who were resistant (n = 27) to formation of venous gas emboli. Although several statistically significant (P less than 0.05) changes in blood parameters were observed following the exposure (increases in white blood cell count, prothrombin time, and total bilirubin, and decreases in triglycerides, very-low-density lipoprotein cholesterol, and blood urea nitrogen), the changes were small in magnitude and blood factor levels remained within normal clinical ranges. Thus, the decompression schedule used in this study is not likely to result in blood changes that would pose a threat to astronauts during extravehicular activity.

Decompression Sickness/blood/etiology/prevention &↗