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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
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 OSTI2024

Assessing and benchmarking the fidelity of posterior inference methods for astrophysics data analysis

In this era of large and complex astronomical survey data, interpreting, validating, and comparing inference techniques becomes increasingly difficult. This is particularly critical for emerging inference methods like Simulation-Based Inference (SBI), which offer significant speedup potential and posterior modeling flexibility, especially when deep learning is incorporated. We present a study to assess and compare the performance and uncertainty prediction capability of Bayesian inference algorithms – from traditional MCMC sampling of analytic functions to deep learning-enabled SBI. We focus on testing the capacity of hierarchical inference modeling in those scenarios. Before we extend this study to cosmology, we first use astrophysical simulation data to ensure interpretability. We demonstrate a probabilistic programming implementation of hierarchical and non-hierarchical Bayesian inference using simulations derived from the DeepBench software library, a benchmarking tool developed by our group that generates simple and controllable astrophysical objects from first principles. This study will enable astronomers and physicists to harness the inference potential of these methods with confidence.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks

ABSTRACT We present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million galaxies, using the Dark Energy Survey (DES) Year 3 data based on convolutional neural networks (CNNs). Monochromatic i-band DES images with linear, logarithmic, and gradient scales, matched with debiased visual classifications from the Galaxy Zoo 1 (GZ1) catalogue, are used to train our CNN models. With a training set including bright galaxies (16 ≤ i < 18) at low redshift (z < 0.25), we furthermore investigate the limit of the accuracy of our predictions applied to galaxies at fainter magnitude and at higher redshifts. Our final catalogue covers magnitudes 16 ≤ i < 21, and redshifts z < 1.0, and provides predicted probabilities to two galaxy types – ellipticals and spirals (disc galaxies). Our CNN classifications reveal an accuracy of over 99 per cent for bright galaxies when comparing with the GZ1 classifications (i < 18). For fainter galaxies, the visual classification carried out by three of the co-authors shows that the CNN classifier correctly categorizes discy galaxies with rounder and blurred features, which humans often incorrectly visually classify as ellipticals. As a part of the validation, we carry out one of the largest examinations of non-parametric methods, including ∼100 ,000 galaxies with the same coverage of magnitude and redshift as the training set from our catalogue. We find that the Gini coefficient is the best single parameter discriminator between ellipticals and spirals for this data set.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1999

Preliminary Results from a New Analysis Method for EGRET Data

In order to extend the life of EGRET, the gas in the spark chamber was allowed to deteriorate more than was originally planned for the nominal two year Compton Observatory mission. Gamma ray events are lost because the pattern recognition analysis rules are not optimized for the poorer quality data. By changing the rules used by the data analysts, we can recover a significant fraction of the lost events, allowing improved statistics for detection and study of sources. Preliminary results from the Crab, Geminga, and BL Lacertae indicate the feasibility of this analysis.

Thompson, D. J.↗
NASA NTRS1991

Linear and circular digital spectral analysis of serial data

Two methods of digital spectral analysis of unevenly sampled data are developed and illustrated here. One method uses a linear function of time (or space), the other uses circular functions. The circular method turns out to be essentially equivalent to a least-squares sine-wave analysis. The linear, anharmonic method uses only the field of real numbers and elementary algebraic operations, and hence it can be made computationally very fast and accurate. Both methods are very general, properly handling all kinds of time series ranging from simple series consisting only of the times of events to complicated series consisting of pulses with long duty cycles. The two methods are here applied to the analysis of annual mean relative sunspot numbers.

Stothers, Richard B.↗
DOE OSTI2020

Measurement Uncertainty in One-Of-A-Kind Event Data Analysis

A golden standard in science is to repeat an experiment a statistically significant number of times, recording data using the same set of detectors and the same data analysis methodology. In such case experimental error includes both the range of true values generated by repetitions of the experiment, and measurement uncertainty caused by the detector. They are independent. It is a huge and too frequently used simplification, to assume that one can measure multiple repetitions of an identical experiment, resulting in identical true experimental value. Repetitions, as similar is it is experimentally achievable, have unavoidable built-in differences resulting in a range of the true values rather than in a single value. When modern, very sensitive and well calibrated measurement systems are used, this range is not negligible, and sometimes dominates over the measurement uncertainty. Range of true values depends on built-in differences in physics of the experiment. Stochastic physical processes result typically in a broader range of true values than non-stochastic processes do. Measurement uncertainty depends on a measurement method (properties of the detector not of the experiment). Modern measurement methods, including digital ones, frequently make the measurement uncertainty very small. When data from one–of –a kind experiment are analyzed, only the measurement uncertainty is reported. It provides no information about the range of true experimental values, neither about reliability of a reported data point. Reliability of a data point is in general independent from its measurement uncertainty. However, in practice reliable measurement methods frequently have high measurement uncertainty, while low reliability methods are applied to limit measurement uncertainty. Comparison of reliable data with high measurement uncertainty to not so reliable data measured with low uncertainty is discussed – in different scenarios different data analysis methods are applicable. Methods for data analysis from an experiment repeated statistically significant number of times are very well developed. They do not require a detailed expertise in physics of an experiment, nor in the properties of the measurement system used, and meaning of the reported uncertainty is well understood in any scientific community. It all changes when data from one-of-a-kind experiment is analyzed. Analyst’s expertise is required both in the physics of the experiment and in all aspects of the measurement system, all possible malfunctions. Data users must remember that only measurement uncertainty is reported from any one-of-a-kind experiment. Theory with simulations may provide estimation of expected built-in differences in the experiment, and by this of expected range of true values for a given experiment; yet measurement uncertainty can never be used in place of the range of true experimental values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1992

Advanced statistical methods for improved data analysis of NASA astrophysics missions

The investigators under this grant studied ways to improve the statistical analysis of astronomical data. They looked at existing techniques, the development of new techniques, and the production and distribution of specialized software to the astronomical community. Abstracts of nine papers that were produced are included, as well as brief descriptions of four software packages. The articles that are abstracted discuss analytical and Monte Carlo comparisons of six different linear least squares fits, a (second) paper on linear regression in astronomy, two reviews of public domain software for the astronomer, subsample and half-sample methods for estimating sampling distributions, a nonparametric estimation of survival functions under dependent competing risks, censoring in astronomical data due to nondetections, an astronomy survival analysis computer package called ASURV, and improving the statistical methodology of astronomical data analysis.

Feigelson, Eric D.↗
DOE OSTI2020

Comprehensive Data Analysis and Analytic Method Development for PBX9501 Material (FY2019 Annual Report of Aging and Lifetimes Program)

In the polymer bonded explosive (PBX) 9501, a binder consisting of 2.5 wt% nitroplasticizer (NP) and 2.5 wt% Estane R 5703 (Estane) is combined with 94.9 wt% of the high-explosive HMX and 0.1 wt% stabilizer. Because of its flexibility and tensile strength, this binder lowers the sensitivity and improves the manufacturability of PBX 9501. However, like many plasticizers comprised of low molecular weight components, NP has a tendency to diffuse out of the PBX 9501 matrix and can decompose at moderate temperatures into reactive byproducts, such as NO, NO 2 , H 2 O, and HNO x . Through oxidation and hydrolysis, these molecules can further degrade NP and Estane, ultimately degrading the properties of PBX 9501. While gaseous molecules can readily diffuse out of the PBX 9501 charges, the intermediates with lower volatility are more likely trapped in the condensed phase inside the charges and, in a closed system, will co-exist with Estane for an extended period of time creating an ongoing reactive environment. Understanding the rates of formation for these volatiles and intermediates during the NP degradation is therefore a critical prerequisite for understanding the long-term stability of polymeric binders used in munition systems. To work toward this ultimate goal, in the past year, we have conducted comprehensive studies on the physical properties of NP and finished three-year long aging experiment to understand the aging behavior of NP under thermal treatment. Important results are summarized in this annual report. Although NP is widely used in the DOE complex, their physical properties are rather scattered and inconsistent in the open literature. For example, there are at least two widely different values, 14.5°C and -15°C, for the melting point of NP. Although it is known that NP is a eutectic 50:50 mixture of BDNPA and BDNPF, their eutectic phase diagram is not well documented. Furthermore, the effect of temperature on the miscibility between water and eutectic BDNPA/F mixture is rarely reported. To fill these knowledge gaps, a large set of BDNPA/F mixtures with BDNPA concentration ranging from 0 to 100 wt% was analyzed using DSC techniques. In addition to determining the eutectic melt point as -25°C, a phase diagram of the BDNPA/F system was constructed from -30°C to 45°C. With this phase diagram, the phase transition temperatures and composition can be readily found for the BDNPA/F mixtures with various mass ratios.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Results of a low power ice protection system test and a new method of imaging data analysis

Tests were conducted on a BF Goodrich De-Icing System's Pneumatic Impulse Ice Protection (PIIP) system in the NASA Lewis Icing Research Tunnel (IRT). Characterization studies were done on shed ice particle size by changing the input pressure and cycling time of the PIIP de-icer. The shed ice particle size was quantified using a newly developed image software package. The tests were conducted on a 1.83 m (6 ft) span, 0.53 m (221 in) chord NACA 0012 airfoil operated at a 4 degree angle of attack. The IRT test conditions were a -6.7 C (20 F) glaze ice, and a -20 C (-4 F) rime ice. The ice shedding events were recorded with a high speed video system. A detailed description of the image processing package and the results generated from this analytical tool are presented.

Shin, Jaiwon↗
NASA NTRS1992

Analyses of exobiological and potential resource materials in the Martian soil

Potential Martian soil components relevant to exobiology include water, organic matter, evaporites, clays, and oxides. These materials are also resources for human expeditions to Mars. When found in particular combinations, some of these materials constitute diagnostic paleobiomarker suites, allowing insight to be gained into the probability of life originating on Mars. Critically important to exobiology is the method of data analysis and data interpretation. To that end, methods of analysis of potential biomarker and paleobiomarker compounds and resource materials in soils and rocks pertinent to Martian geology are investigated. Differential thermal analysis coupled with gas chromotography is shown to be a highly useful analytical technique for detecting this wide and complex variety of materials.

Mancinelli, Rocco L.↗
NASA NTRS1992

Results of a low power ice protection system test and a new method of imaging data analysis

Tests were conducted on a BF Goodrich De-Icing System's Pneumatic Impulse Ice Protection (PIIP) system in the NASA Lewis Icing Research Tunnel (IRT). Characterization studies were done on shed ice particle size by changing the input pressure and cycling time of the PIIP de-icer. The shed ice particle size was quantified using a newly developed image software package. The tests were conducted on a 1.83 m (6 ft) span, 0.53 m (221 in) chord NACA 0012 airfoil operated at a 4 degree angle of attack. The IRT test conditions were a -6.7 C (20 F) glaze ice, and a -20 C (-4 F) rime ice. The ice shedding events were recorded with a high speed video system. A detailed description of the image processing package and the results generated from this analytical tool are presented.

Shin, Jaiwon↗
NASA NTRS1991

ATMOS data processing and science analysis methods

The atmospheric trace molecule spectroscopy (ATMOS) instrument, a high-speed Fourier transform spectrometer operating in the middle IR (2.2-16 microns), recorded more than 1500 solar spectra at about 0.0105/cm resolution during its first mission onboard the shuttle Challenger in the spring of 1985. These spectra were acquired during high-sun conditions for studies of the solar atmosphere and during low-sun conditions for studies of the earth's upper atmosphere. This paper describes the steps by which the telemetry data were converted into spectra suitable for analysis, the analysis software and methods developed for the atmospheric and solar studies, and the ATMOS data analysis facility.

Norton, Robert H.↗
DOE OSTI2023

Characterizing Reactor Operations from Realistic Simulated Environmental Samples: Combining High-Performance Computing and Data Analytics

Environmental sampling is a common technique employed by inspectors and facility operators in nuclear safeguards, proliferation detection, and process monitoring contexts. Interpreting measurements performed on samples or collections of samples and ensuring the information extracted is accurate and precise is difficult. To date, these analyses have relied on simulated data to enable systematic studies; however, these models are inherently limited by the fidelity of the models and the implicit spatial averaging of isotopic composition or other signatures of interest. To advance this capability, we have refined the spatial discretization and expanded the range of physics in the simulation codes we use to perform reactor simulations and depletion calculations. This allows us to generate data that are more representative of real environmental samples, especially for the length scale of the isotopic composition and associated variation. Accordingly, these new data allow a more realistic assessment of traditional and new data analytic analysis methods. Here we present motivation for developing reactor simulations using high-performance computing methods and resources, impacts of these new simulations on our assessment of data analysis and interpretation methods, and initial results of developing and systematically testing data analytic methods designed to overcome the challenges expected of real-world samples. We also quantify the performance of these analyses using defensible statistical methods.

Dayman, Ken J.↗
NASA NTRS1997

Demonstration of Wavelet Techniques in the Spectral Analysis of Bypass Transition Data

A number of wavelet-based techniques for the analysis of experimental data are developed and illustrated. A multiscale analysis based on the Mexican hat wavelet is demonstrated as a tool for acquiring physical and quantitative information not obtainable by standard signal analysis methods. Experimental data for the analysis came from simultaneous hot-wire velocity traces in a bypass transition of the boundary layer on a heated flat plate. A pair of traces (two components of velocity) at one location was excerpted. A number of ensemble and conditional statistics related to dominant time scales for energy and momentum transport were calculated. The analysis revealed a lack of energy-dominant time scales inside turbulent spots but identified transport-dominant scales inside spots that account for the largest part of the Reynolds stress. Momentum transport was much more intermittent than were energetic fluctuations. This work is the first step in a continuing study of the spatial evolution of these scale-related statistics, the goal being to apply the multiscale analysis results to improve the modeling of transitional and turbulent industrial flows.

Lewalle, Jacques↗
NASA NTRS1972

A generalized method for the identification of aircraft stability and control derivatives from flight test data.

This paper discusses the application of a generalized identification method for flight test data analysis. The method is based on the maximum likelihood (ML) criterion and includes output error and equation error methods as special cases. Both the linear and nonlinear models with and without process noise are considered. The flight test data from lateral maneuvers of HL-10 and M2/F3 lifting bodies are processed to determine the lateral stability and control derivatives, instrumentation accuracies and biases. A comparison is made between the results of the output error method and the generalized ML method for M2/F3 data containing gusts. It is shown that better fits to time histories are obtained by using the generalized ML method.

Mehra, R. K.↗
NASA NTRS1974

Maximum likelihood identification of aircraft stability and control derivatives

Application of a generalized identification method to flight test data analysis. The method is based on the maximum likelihood (ML) criterion and includes output error and equation error methods as special cases. Both the linear and nonlinear models with and without process noise are considered. The flight test data from lateral maneuvers of HL-10 and M2/F3 lifting bodies are processed to determine the lateral stability and control derivatives, instrumentation accuracies, and biases. A comparison is made between the results of the output error method and the ML method for M2/F3 data containing gusts. It is shown that better fits to time histories are obtained by using the ML method. The nonlinear model considered corresponds to the longitudinal equations of the X-22 VTOL aircraft. The data are obtained from a computer simulation and contain both process and measurement noise. The applicability of the ML method to nonlinear models with both process and measurement noise is demonstrated.

Mehra, R. K.↗