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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 NTRS2012

The Atacama Cosmology Telescope: Data Characterization and Map Making

We present a description of the data reduction and mapmaking pipeline used for the 2008 observing season of the Atacama Cosmology Telescope (ACT). The data presented here at 148 GHz represent 12% or the 90 TB collected by ACT from 2007 to 2010. In 2008 we observed for 136 days, producing a total of 142h of data (11 TB for the 148 GHz band only), with a daily average of 10.5 h of observation. From these, 108.5 h were devoted to 850 sq deg stripe (11.2 h by 9 deg.1) centered on a declination of -52 deg.7, while 175 h were devoted to a 280 square deg stripe (4.5 h by 4 deg.8) centered at the celestial equator. We discuss sources of statistical and systematic noise, calibration, telescope pointing and data selection. Out of 1260 survey hours and 1024 detectors per array, 816 h and 593 effective detectors remain after data selection for this frequency band, yielding a 38 % survey efficiency. The total sensitivity in 2008, determined from the noise level between 5 Hz and 20 Hz in the time-ordered data stream (TOD), is 32 muK square root of s in CMB units. Atmospheric brightness fluctuations constitute the main contaminant in the data and dominate the detector and noise covariance at low frequencies in the TOD. The maps were made by solving the lease squares problem using the Preconditioned Conjugate Gradient method, incorporating the details of the detector and noise correlations. Cross-correlation with WMAP sky maps as well as analysis from simulations reveal the our maps are unbiased at l > 300. This paper accompanies the public release of the 148 GHz southern stripe maps from 2008. The techniques described here will be applied to future maps and data releases.

Duenner, Rolando↗
DOE OSTI2017

Funding for the 2ND IAEA technical meeting on fusion data processing, validation and analysis

The International Atomic Energy Agency (IAEA) will organize the second Technical Meeting on Fusion Da Processing, Validation and Analysis from 30 May to 02 June, 2017, in Cambridge, MA USA. The meeting w be hosted by the MIT Plasma Science and Fusion Center (PSFC). The objective of the meeting is to provide a platform where a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolation needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge based understanding of the physical processes governing the dynamics of these plasmas. The meeting will aim at fostering, in particular, discussions of research and development results that set out or underline trends observed in the current major fusion confinement devices. General information on the IAEA, including its mission and organization, can be found at the IAEA websit Uncertainty quantification (UQ) Model selection, validation, and verification (V&V) Probability theory and statistical analysis Inverse problems & equilibrium reconstru ction Integrated data analysis Real time data analysis Machine learning Signal/image proc essing & pattern recognition Experimental design and synthetic diagnostics Data management

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1973

An empirical analysis of the distribution of overshoots in a stationary Gaussian stochastic process

The frequency distribution of overshoots in a stationary Gaussian stochastic process is analyzed. The primary processes involved in this analysis are computer simulation and statistical estimation. Computer simulation is used to simulate stationary Gaussian stochastic processes that have selected autocorrelation functions. An analysis of the simulation results reveals a frequency distribution for overshoots with a functional dependence on the mean and variance of the process. Statistical estimation is then used to estimate the mean and variance of a process. It is shown that for an autocorrelation function, the mean and the variance for the number of overshoots, a frequency distribution for overshoots can be estimated.

Carter, M. C.↗
NASA NTRS2005

Improved Compression of Wavelet-Transformed Images

A recently developed data-compression method is an adaptive technique for coding quantized wavelet-transformed data, nominally as part of a complete image-data compressor. Unlike some other approaches, this method admits a simple implementation and does not rely on the use of large code tables. A common data compression approach, particularly for images, is to perform a wavelet transform on the input data, and then losslessly compress a quantized version of the wavelet-transformed data. Under this compression approach, it is common for the quantized data to include long sequences, or runs, of zeros. The new coding method uses prefixfree codes for the nonnegative integers as part of an adaptive algorithm for compressing the quantized wavelet-transformed data by run-length coding. In the form of run-length coding used here, the data sequence to be encoded is parsed into strings consisting of some number (possibly 0) of zeros, followed by a nonzero value. The nonzero value and the length of the run of zeros are encoded. For a data stream that contains a sufficiently high frequency of zeros, this method is known to be more effective than using a single variable length code to encode each symbol. The specific prefix-free codes used are from two classes of variable-length codes: a class known as Golomb codes, and a class known as exponential-Golomb codes. The codes within each class are indexed by a single integer parameter. The present method uses exponential-Golomb codes for the lengths of the runs of zeros, and Golomb codes for the nonzero values. The code parameters within each code class are determined adaptively on the fly as compression proceeds, on the basis of statistics from previously encoded values. In particular, a simple adaptive method has been devised to select the parameter identifying the particular exponential-Golomb code to use. The method tracks the average number of bits used to encode recent runlengths, and takes the difference between this average length and the code parameter. When this difference falls outside a fixed range, the code parameter is updated (increased or decreased). The Golomb code parameter is selected based on the average magnitude of recently encoded nonzero samples. The coding method requires no floating- point operations, and more readily adapts to local statistics than other methods. The method can also accommodate arbitrarily large input values and arbitrarily long runs of zeros. In practice, this means that changes in the dynamic range or size of the input data set would not require a change to the compressor. The algorithm has been tested in computational experiments on test images. A comparison with a previously developed algorithm that uses large code tables (generated via Huffman coding on training data) suggests that the data-compression effectiveness of the present algorithm is comparable to the best performance achievable by the previously developed algorithm.

Kiely, Aaron↗
NASA NTRS1997

Development of Large Area Emulsion Chamber Methods with a Super Conducting Magnet for Observation of Cosmic Ray Nuclei from 1 GeV to 1,000 TeV (Emulsion Techniques)

The research developed the fundamental techniques of the emulsion chamber methods that permit measurements of the composition and energy spectra of cosmic rays at energies ranging from 1 GeV/n to over 1,000 TeV/n. The research program consisted of exploring new principles and techniques in measuring very high energy cosmic nuclei with large-area emulsion chambers for high statistics experiments. These tasks have been accomplished and their use was essential in successful analysis of the balloon-borne emulsion chamber experiments up to 10(exp 14) eV. It also provided the fundamental technologies for designing large-area detectors that are aimed at measuring the composition at above 1015 eV region. The latter is now partially succeeded by a NASA Mission Concept, Advanced Cosmic Composition Experiments on the Space Station (ACCESS). The cosmic ray group at the University of Alabama in Huntsville has performed technological R & D as well as contributing to the Japanese-American-Emulsion-Chamber-Experiments (JACEE) Collaboration with the regular data analysis. While primary research support for other institutions' efforts in the JACEE experiments came from NSF and DOE, primary support for the University of Alabama in Huntsville was this contract. Supplemental tasks to standardize the data base and hardware upgrades (automatized microscope) had this institutions cooperation. Investigation of new techniques in this program consisted of development of a fast calorimetry, magnetic/scattering selection of high momentum tracks for a pairmeter, and high statistics momentum measurements for low energy nuclei (E < 1 TeV/n). The highest energy calorimetry and a pairmeter have been considered as strawman instruments by the GOAL (Galactic Origin and Acceleration Limit) proposal of the NASA Cosmic Ray Working Group for long- duration balloon flights. We accomplished the objectives of the GOAL program with three circumpolar, Antarctic JACEE balloon flights during 1992 - 1994.

Takahashi, Yoshiyuki↗
NASA NTRS1977

The local time variation of ELF emissions during periods of substorm activity

A statistical study is reported concerning the occurrence probability of ELF emissions observed on the low-altitude polar-orbiting satellite OGO 6 during periods of substorm activity. Over 160 individual substorm periods have been selected and analyzed during the period from June 1969 to October 1970. The statistical results are discussed, taking into account ELF response to substorm activity and emissions during the recovery phase of magnetic storms.

Thorne, R. M.↗
NASA NTRS2015

Detection and Modeling of High-Dimensional Thresholds for Fault Detection and Diagnosis

Many Fault Detection and Diagnosis (FDD) systems use discrete models for detection and reasoning. To obtain categorical values like oil pressure too high, analog sensor values need to be discretized using a suitablethreshold. Time series of analog and discrete sensor readings are processed and discretized as they come in. This task isusually performed by the wrapper code'' of the FDD system, together with signal preprocessing and filtering. In practice,selecting the right threshold is very difficult, because it heavily influences the quality of diagnosis. If a threshold causesthe alarm trigger even in nominal situations, false alarms will be the consequence. On the other hand, if threshold settingdoes not trigger in case of an off-nominal condition, important alarms might be missed, potentially causing hazardoussituations. In this paper, we will in detail describe the underlying statistical modeling techniques and algorithm as well as the Bayesian method for selecting the most likely shape and its parameters. Our approach will be illustrated by several examples from the Aerospace domain.

Computer Systems↗
NASA NTRS2013

Spatial Statistical Data Fusion (SSDF)

As remote sensing for scientific purposes has transitioned from an experimental technology to an operational one, the selection of instruments has become more coordinated, so that the scientific community can exploit complementary measurements. However, tech nological and scientific heterogeneity across devices means that the statistical characteristics of the data they collect are different. The challenge addressed here is how to combine heterogeneous remote sensing data sets in a way that yields optimal statistical estimates of the underlying geophysical field, and provides rigorous uncertainty measures for those estimates. Different remote sensing data sets may have different spatial resolutions, different measurement error biases and variances, and other disparate characteristics. A state-of-the-art spatial statistical model was used to relate the true, but not directly observed, geophysical field to noisy, spatial aggregates observed by remote sensing instruments. The spatial covariances of the true field and the covariances of the true field with the observations were modeled. The observations are spatial averages of the true field values, over pixels, with different measurement noise superimposed. A kriging framework is used to infer optimal (minimum mean squared error and unbiased) estimates of the true field at point locations from pixel-level, noisy observations. A key feature of the spatial statistical model is the spatial mixed effects model that underlies it. The approach models the spatial covariance function of the underlying field using linear combinations of basis functions of fixed size. Approaches based on kriging require the inversion of very large spatial covariance matrices, and this is usually done by making simplifying assumptions about spatial covariance structure that simply do not hold for geophysical variables. In contrast, this method does not require these assumptions, and is also computationally much faster. This method is fundamentally different than other approaches to data fusion for remote sensing data because it is inferential rather than merely descriptive. All approaches combine data in a way that minimizes some specified loss function. Most of these are more or less ad hoc criteria based on what looks good to the eye, or some criteria that relate only to the data at hand.

Braverman, Amy J.↗
DOE OSTI2018

Cold Commissioning Simulant Selection for the Hanford Tank Waste Treatment Plant LAW Facility - 18465

Commissioning the Low-Activity Waste (LAW) Facility of the Hanford Tank Waste Treatment and Immobilization Plant (WTP) will require large volumes (∼2,000,000 liters) of a non-radioactive, chemically analogous waste simulant. Cold commissioning will provide for operator training in an integrated facility, demonstration of mechanical process equipment capacity, environmental performance demonstration of emissions control systems, and process control systems shakedown and tuning. Selection of the cold commissioning simulant is following a structured four-phase process to arrive at the preferred composition and recipe. Phases I, II and III are complete. Phase I laid the framework for decision making with preparation of a Project Management Plan, an update to the Simulant Development, Approval, Validation, and Documentation procedure, and a kickoff meeting to gather stakeholder input into the simulant definition and selection criteria. Phase II consisted of a comprehensive literature search and compilation of LAW simulant recipes, followed by initial screening against the selection criteria identified in Phase I. This process identified 72 candidate simulant recipes and down-selected 9 for further study. Phase III studies involved a) statistical comparisons of the similarity of candidate simulant compositions to the tank waste feed vector, b) process modelling of the commissioning sequence with each simulant to estimate glass former and simulant material consumption, offgas and effluent volumes and compositions, and product glass volumes and compositions, and c) evaluation of simulants against the immobilized LAW glass composition algorithm to determine allo able waste loading to ensure compliance with product specifications and process constraints. The results of these studies identified two of the candidate recipes to be carried forward to full-scale testing. Phase IV consists of testing these two recipes at vendor facilities to determine scalability, fabricability, and stability of the simulants when produced at industrial scale. Scalability is the ability to take a recipe from bench scale (1 to 40 liters) batch size to full scale (>35,000 liters) batch size. Fabricability is the ability of a recipe to be prepared in large batches without the need for complex or expensive mechanical equipment, heating, or cooling systems. Stability is the ability of a simulant to withstand long term storage and variations in environmental conditions without unacceptable changes in key characteristics (the set of chemical, physical, and rheological parameters that the simulant is intended to exhibit during cold commissioning tests). Simulants will also be tested for compatibility with other startup material (i.e., frit), tuning simulants, and environmental performance demonstration test spiking solutions to ensure against unexpected situations. Using a structured process is essential when making high impact decisions such as waste simulant selection. Proper planning and a clear explanation of how the decision process will unfold is key to obtaining stakeholder buy-in as more information is gathered. The documentation resulting from this structured selection process will be compiled and summarized in a Cold Commissioning Simulant Definition deliverable due to the Department of Energy 24 months before the start of Cold Commissioning. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
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.]↗
NASA NTRS1999

Preliminary Statistical Analysis of the 1995 Evaluation by NASA LaRC of the IAI Automatic Balance Calibration Machine

The NASA Langley Research Center (LARC) participated in a national cooperative evaluation of the Israel Aircraft Industries (IAI) automatic balance calibration machine at Microcraft, San Diego in September 1995. A LaRC-designed six-component strain gauge balance was selected for test and calibration during LaRC's scheduled evaluation period. Eight calibrations were conducted using three selected experimental designs. Raw data were exported to LaRC facilities for reduction and statistical analysis using the techniques outlined in Tripp and Tcheng (1994). This report presents preliminary assessments of the results, and compares IAI calibration results with manual calibration results obtained at the Modern Machine and Tool Co., Inc. (MM & T). Newport News, VA. A more comprehensive report is forthcoming.

Tcheng, Ping↗
DOE OSTI2020

The LSST DESC data challenge 1: generation and analysis of synthetic images for next-generation surveys

Data Challenge 1 (DC1) is the first synthetic data set produced by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). DC1 is designed to develop and validate data reduction and analysis and to study the impact of systematic effects that will affect the LSST data set. DC1 is comprised of r -band observations of 40 deg 2 to 10 yr LSST depth. In this paper, we present each stage of the simulation and analysis process: (a) generation, by synthesizing sources from cosmological N -body simulations in individual sensor-visit images with different observing conditions; (b) reduction using a development version of the LSST Science Pipelines; and (c) matching to the input cosmological catalogue for validation and testing. We verify that testable LSST requirements pass within the fidelity of DC1. We establish a selection procedure that produces a sufficiently clean extragalactic sample for clustering analyses and we discuss residual sample contamination, including contributions from inefficiency in star–galaxy separation and imperfect deblending. We compute the galaxy power spectrum on the simulated field and conclude that: (i) survey properties have an impact of 50 per cent of the statistical uncertainty for the scales and models used in DC1; (ii) a selection to eliminate artefacts in the catalogues is necessary to avoid biases in the measured clustering; and (iii) the presence of bright objects has a significant impact (2σ–6σ) in the estimated power spectra at small scales (ℓ > 1200), highlighting the impact of blending in studies at small angular scales in LSST.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Validation of LOCA2 and STAR-ESDM Statistically Downscaled Products

The National Climate Assessment (NCA) is the preeminent national report examining current and future risks posed by climate change. Countless agencies, policymakers, stakeholders and other end-users rely upon guidance from the NCA to plan for an uncertain future. These groups all depend on modern curated data, provided alongside the NCA, to quantify the impact of climate change on metrics of relevance for their decision processes. In its fifth iteration (NCA5), two statistically downscaled ensemble products, each providing data at grid spacing of approximately 5km over the contiguous United States, were selected to accompany the report. These include LOCalized Analogs version 2 (LOCA2) and Seasonal Trends and Analysis of Residuals Empirical-Statistical Downscaling Model (STAR-ESDM). Both data products are produced through a process known as statistical downscaling, where relatively coarse Global Climate Model (GCM) data is refined to locally relevant scales through the application of scientifically-supported empirical and algorithmic relationships. In support of the NCA effort, this report provides an independent validation of these two products against historical observations, with a focus on precipitation and near-surface temperature variables. Based on the results of this validation, several recommendations are provided related to the use of these data products. The structure of this report is as follows: In section 2, we review three gridded observational products that are used as part of our intercomparison. In section 3, we describe the two statistical downscaling techniques and their corresponding datasets that are the focus of this study. In section 4, the methodology we employ for validation is described. Section 5 provides results of the validation, which in turn motivate our recommendations on the use of these data products. A brief summary is provided in section 6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Sagittarius stream in Gaia Early Data Release 3 and the origin of the bifurcations

The Sagittarius dwarf spheroidal (Sgr) is a dissolving galaxy being tidally disrupted by the Milky Way (MW). Its stellar stream still poses serious modelling challenges, which hinders our ability to use it effectively as a prospective probe of the MW gravitational potential at large radii. Our goal is to construct the largest and most stringent sample of stars in the stream with which we can advance our understanding of the Sgr-MW interaction, focusing on the characterization of the bifurcations. We improved on previous methods based on the use of the wavelet transform to systematically search for the kinematic signature of the Sgr stream throughout the whole sky in the Gaia data. We then refined our selection via the use of a clustering algorithm on the statistical properties of the colour-magnitude diagrams. Our final sample contains more than 700 000 candidate stars and is three times larger than previous Gaia samples. With it, we have been able to detect the bifurcation of the stream in both the northern and southern hemispheres, which requires four branches (two bright and two faint) to fully describe the system. We present the detailed proper motion distribution of the trailing arm as a function of the angular coordinate along the stream, showing, for the first time, the presence of a sharp edge (on the side of the small proper motions) beyond which there are no Sgr stars. We also characterize the correlation between kinematics and distance. Finally, the chemical analysis of our sample shows that the faint branch of the bifurcation is more metal poor than the bright. We provide analytical descriptions for the proper motion trends as well as for the sky distribution of the four branches of the stream. Based on our analysis, we interpret the bifurcations as a misaligned overlap of the material stripped at the antepenultimate pericentre (faint branches) with the stars ejected at the penultimate pericentre (bright branch), given that Sgr just went through its perigalacticon. The source of this misalignment is still unknown, but we argue that models with some internal rotation in the progenitor – at least during the time of stripping of the stars that are now in the faint branches – are worth exploring.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Comparing Designed Training Sets to Optimize Multivariate Regression Models for Pr, Nd, and Nitric Acid Using Spectrophotometry

Chemometric regression models were developed for the quantification of praseodymium (Pr, 0–1000 µg/mL), neodymium (Nd, 0–1000 µg/mL), and nitric acid (HNO 3 , 0.1–5 M) using spectrophotometry. Designed calibration sets were composed of 20 samples each: 10 model points and 10 lack-of-fit (LOF) points. The D-optimal designs effectively minimized the number of samples required to build models, and each design resulted in similar prediction performance, suggesting that statistical design of experiments can provide a reliable framework for selecting training set samples in three-variable systems. Partial least squares regression (PLSR) models were validated against a one-factor-at-a-time validation set composed of 125 samples (three variables, five levels). The top PLS-1 models resulted in average percent root mean square error of prediction error values of 3.5%, 1.7%, and 1.2% for Pr(III), Nd(III), and HNO 3 , respectively. Power set augmentations of the model and LOF samples were investigated to optimize the number of training set samples. PLSR models built using just required model points (10) had similar predictive capabilities as models including the LOF points (20) but with fewer samples. The number of validation samples was also varied systematically to learn how many samples are needed to validate regression models. This work addresses long-standing questions in the field of chemometrics to help make this approach amenable to the near-real-time quantification of hazardous species in remote settings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗