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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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NASA NTRS2016

Kepler Planet Detection Metrics: Statistical Bootstrap Test

This document describes the data produced by the Statistical Bootstrap Test over the final three Threshold Crossing Event (TCE) deliveries to NExScI: SOC 9.1 (Q1Q16)1 (Tenenbaum et al. 2014), SOC 9.2 (Q1Q17) aka DR242 (Seader et al. 2015), and SOC 9.3 (Q1Q17) aka DR253 (Twicken et al. 2016). The last few years have seen significant improvements in the SOC science data processing pipeline, leading to higher quality light curves and more sensitive transit searches. The statistical bootstrap analysis results presented here and the numerical results archived at NASAs Exoplanet Science Institute (NExScI) bear witness to these software improvements. This document attempts to introduce and describe the main features and differences between these three data sets as a consequence of the software changes.

Bootstrap↗
NASA NTRS1985

Hyperspectral Image Analysis Program

Information on imaging spectrometry is given in the forms of outlines, graphs, and charts. Topics covered include impacts on science users, program objectives, expert systems for imaging spectrometry, and imaging spectrometer data analysis methods.

Source record↗
NASA NTRS2006

Exploring the Mineralogy of the Moon with M3

From the initial era or lunar exploration, we have learned that many processes active on the early Moon are common to most terrestrial planets, including the record of early and late impact bombardment. Since most major geologic activity ceased on the Moon approx. 3 Gy ago, the Moon's surface provides a record of the earliest era of terrestrial planet evolution. The type and composition of minerals that comprise a planetary surface are a direct result of the initial composition and subsequent thermal and physical processing. Lunar mineralogy seen today is thus a direct record of the early evolution of the lunar crust and subsequent geologic processes. Specifically, the distribution and concentration of specific minerals is closely tied to magma ocean products, lenses of intruded or remelted plutons, basaltic volcanism and fire-fountaining, and any process (e.g. cratering) that might redistribute or transform primary and secondary lunar crustal materials. The Moon Mineralogy Mapper (M3, or "m-cube") is a state-of-the-art imaging spectrometer that will fly on Chandrayaan-1, the Indian Space Research Organization (ISRO) mission to be launched late 2007 to early 2008. M3 is one of several foreign instruments chosen by ISRO to be flown on Chandrayaan-1 to complement the strong ISRO payload package. M3 was selected through a peer-review process as part of NASA s Discovery Program. It is under the oversight of PI Carle Pieters at Brown University and is being built by an experienced team at the Jet Propulsion Laboratory. Data analysis and calibration are carried out by a highly qualified and knowledgeable Science Team. To characterize diagnostic properties of lunar minerals, M3 acquires high spectral resolution reflectance data from 700 to 3000 nm (optional to 430 nm). M3 operates as a pushbroom spectrometer with a slit oriented orthogonal to the S/C orbital motion. Measurements are obtained simultaneously for 640 cross track spatial elements and 261 spectral elements. This translates to 70 m/pixel spatial resolution from a nominal 100 km polar orbit for Chandrayaan-1 . The primary science goal of M3 is to characterize and map lunar surface mineralogy in the context of its geologic evolution as outlined above. This translates into several sub-topics that focus on exploring the mineral character of the highland crust, characterizing the diversity basaltic volcanism, and identifying potential volatile concentrations near the poles. The primary exploration goal is to assess and map lunar mineral resources at high spatial resolution to support planning for future, targeted missions.

Pieters, C. M.↗
DOE OSTI2024

Fractal analysis on Ag 2 O thin film using a data-driven approach

The synthesis of fractal Ag oxide (Ag 2 O) on the surface of Ag thin film has been achieved at room temperature by using Synchrotron X-ray irradiation. We have performed an automated quantitative analysis of a batch of 1879 fractal Ag 2 O patterns in a scanning electron microscopy (SEM) image within a radius of 2000 mm outward from the center of the X-ray beam. The morphology is similar to that of the diffusion-limited cluster aggregation (DLCA) model. The fractal dimension (D) of Ag 2 O is between 1.7 and 1.5 from the center to the edge. The area distribution density of fractal Ag 2 O follows a quadratic function with radius R. It is found that the branches’ number of fractal Ag 2 O is a key factor affecting the fractal dimension. The more branches the fractal has, the greater the D is. This is the first time that Ag fractal has been investigated by combining automated data analysis methods with batch experimental data. This data-driven approach provides a new research perspective for rationally regulating materials’ fractal morphology and performance.

36 MATERIALS SCIENCE↗
NASA NTRS2013

RUSHMAPS: Real-Time Uploadable Spherical Harmonic Moment Analysis for Particle Spectrometers

RUSHMAPS is a new onboard data reduction scheme that gives real-time access to key science parameters (e.g. moments) of a class of heliophysics science and/or solar system exploration investigation that includes plasma particle spectrometers (PPS), but requires moments reporting (density, bulk-velocity, temperature, pressure, etc.) of higher-level quality, and tolerates a lowpass (variable quality) spectral representation of the corresponding particle velocity distributions, such that telemetry use is minimized. The proposed methodology trades access to the full-resolution velocity distribution data, saving on telemetry, for real-time access to both the moments and an adjustable-quality (increasing quality increases volume) spectral representation of distribution functions. Traditional onboard data storage and downlink bandwidth constraints severely limit PPS system functionality and drive cost, which, as a consequence, drives a limited data collection and lower angular energy and time resolution. This prototypical system exploit, using high-performance processing technology at GSFC (Goddard Space Flight Center), uses a SpaceCube and/or Maestro-type platform for processing. These processing platforms are currently being used on the International Space Station as a technology demonstration, and work is currently ongoing in a new onboard computation system for the Earth Science missions, but they have never been implemented in heliospheric science or solar system exploration missions. Preliminary analysis confirms that the targeted processor platforms possess the processing resources required for realtime application of these algorithms to the spectrometer data. SpaceCube platforms demonstrate that the target architecture possesses the sort of compact, low-mass/power, radiation-tolerant characteristics needed for flight. These high-performing hybrid systems embed unprecedented amounts of onboard processing power in the CPU (central processing unit), FPGAs (field programmable gate arrays), and DSP (digital signal processing) elements. The fundamental computational algorithm de constructs 3D velocity distributions in terms of spherical harmonic spectral coefficients (which are analogous to a Fourier sine-cosine decomposition), but uses instead spherical harmonics Legendre polynomial orthogonal functions as a basis for the expansion, portraying each 2D angular distribution at every energy or, geometrically, spherical speed-shell swept by the particle spectrometer. Optionally, these spherical harmonic spectral coefficients may be telemetered to the ground. These will provide a smoothed description of the velocity distribution function whose quality will depend on the number of coefficients determined. Successfully implemented on the GSFC-developed processor, the capability to integrate the proposed methodology with both heritage and anticipated future plasma particle spectrometer designs is demonstrated (with sufficiently detailed design analysis to advance TRL) to show specific science relevancy with future HSD (Heliophysics Science Division) solar-interplanetary, planetary missions, sounding rockets and/or CubeSat missions.

Figueroa-Vinas, Adolfo↗
NASA NTRS1995

SAVS: A Space and Atmospheric Visualization Science system

The research environment faced by space and atmospheric scientists in the 1990s is characterized by unprecedented volumes of new data, by ever-increasing repositories of unexploited mission files, and by the widespread use of empirical and large-scale computational models needed for the synthesis of understanding across data sets and discipline boundaries. The effective analysis and interpretation of such massive amounts of information have become the subjects of legitimate concern. With SAVS (a Space and Atmospheric Visualization Science System), we address these issues by creating a 'push-button' software environment that mimics the logical scientific processes in data acquisition, reduction, and analysis without requiring a detailed understanding of the methods, networks, and modules that link the tools and effectively execute the functions. SAVS provides (1) a customizable framework for accessing a powerful set of visualization tools based on the popular AVS visualization software with hooks to PV-Wave and access to Khoros modules, (2) a set of mathematical and statistical tools, (3) an extensible library of discipline-specific functions and models (e.g., MSIS, IRI, Feldstein Oval, IGRF, satellite tracking with CADRE-3, etc.), and (4) capabilities for local and remote data base access. The system treats scalar, vector, and image data, and runs on most common Unix workstations. We present a description of SAVS and its components, followed by several applications based on generic research interests in interplanetary and magnetospheric physics (IMP/ISTP), active experiments in space (CRRES), and mission planning focused on the Earth's thermospheric, ionospheric, and mesospheric domains (TIMED).

Szuszczewicz, E. P.↗
NASA NTRS2005

Enabling Autonomous Rover Science through Dynamic Planning and Scheduling

This paper describes how dynamic planning and scheduling techniques can be used onboard a rover to autonomously adjust rover activities in support of science goals. These goals could be identified by scientists on the ground or could be identified by onboard data-analysis software. Several different types of dynamic decisions are described, including the handling of opportunistic science goals identified during rover traverses, preserving high priority science targets when resources, such as power, are unexpectedly over-subscribed, and dynamically adding additional, ground-specified science targets when rover actions are executed more quickly than expected. After describing our specific system approach, we discuss some of the particular challenges we have examined to support autonomous rover decision-making. These include interaction with rover navigation and path-planning software and handling large amounts of uncertainty in state and resource estimations.

planning↗
NASA NTRS1991

Scientific requirements for space science data systems

In the 1990's space plasma physics studies will increasingly involve correlative analysis of observations from multiple instruments and multiple spacecraft. The solar terrestrial physics missions in the 1990's will be designed around simultaneous observations from spacecraft monitoring the solar wind, the polar magnetosphere and the near and distant magnetotail. Within these regions, clusters of spacecraft flying in formation will increasingly involve comparative magnetospheric studies. No single lab will have the expertise to process and analyze all of the different types of data so the data repositories will be distributed. Catalog and browse systems will be required to help select events for study. Data compression techniques may be useful in designing the data bases used for selecting events for study. Data compression onboard the spacecraft will be necessary, since instrument data rates will be much larger than available telemetry rates. However, considerable care will be necessary to avoid losing valuable data when applying data compression algorithms.

Walker, Raymond J.↗
NASA NTRS2016

Biospecimen Culling: Temporal RNA Integrity Analysis Across Spaceflight Missions Dating from 1985 to 2011

The Ames Life Science Data Archive (ALSDA) at NASA Ames Research Center is managed by the Space Biosciences Division and has been operational since 1993. The ALSDA is responsible for archiving information and biospecimens collected from life science spaceflight experiments and matching ground control experiments. They are stored in the Ames biobank, which is located in the Biospecimen Storage Facility (BSF). The ALSDA also manages a Biospecimen Sharing Program, performs curation and long-term storage operations, and makes biospecimens available to the scientific community for research purposes via the Life Science Data Archive public website (https:lsda.jsc.nasa.gov). The BSF maintains both fixed and frozen spaceflight and ground tissues, collected from recent and past spaceflight missions. Due to the ever increasing demand for space to preserve current and future flight biospecimens, the ALSDA has initiated the development of a culling plan for biospecimens currently stored in the BSF. Culling enables the ALSDA to assess the quality of archived samples, and supports the development of standardized culling procedures that improve the operational efficiency of the BSF. The culling plan focuses on generating disposition recommendations for samples in the BSF, and currently is based on measuring ribonucleic acid (RNA) integrity number (RIN). The culling process includes (1) sorting and identification of candidate samples for RIN analysis, (2) completion of RIN analysis on select samples, and (3) development of disposition recommendations for specimens based on the RIN values. Furthermore, our approach allows for unique scientific opportunities, including development of a RIN-based methodology for culling, and temporal assessment of the quality of the tissues that have been stored in BSF since the 1980s. Results of this work will also support NASA open science initiatives.

biospecimen↗
NASA NTRS2005

NASA GES DISC On-line Visualization and Analysis System for Gridded Remote Sensing Data

The ability to use data stored in the current NASA Earth Observing System (EOS) archives for studying regional or global phenomena is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. Gaining this understanding and applying it to data reduction is a time-consuming task that must be undertaken before the core investigation can begin. This is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets that are usually of different formats, structures, and resolutions. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step towards meeting this challenge by developing an infrastructure with a Web interface that allows users to perform interactive analysis online without downloading any data, the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni." Giovanni provides interactive, online, analysis tools for data users to facilitate their research. There have been several instances of this interface created to serve TRMM users, Aerosol scientists, Ocean Color and Agriculture applications users. The first generation of these tools support gridded data only. The user selects geophysical parameters, area of interest, time period; and the system generates an output on screen in a matter of seconds. The currently available output options are: Area plot averaged or accumulated over any available data period for any rectangular area; Time plot time series averaged over any rectangular area; Hovmoller plots image view of any longitude-time and latitude-time cross sections; ASCII output for all plot types; Image animation for area plot. Another analysis suite deals with parameter intercomparison: scatter plots, temporal correlation maps, GIs-compatible outputs, etc. This allow user to focus on data content (i.e. science parameters) and eliminate the need for expensive learning, development and processing tasks that are redundantly incurred by an archive's user community. The current implementation utilizes the GrADS-DODS Server (GDS), and provides subsetting and analysis services across the Internet for any GrADS-readable dataset. The subsetting capability allows users to retrieve a specified temporal and/or spatial subdomain from a large dataset, eliminating the need to download everything simply to access a small relevant portion of a dataset. The analysis capability allows users to retrieve the results of an operation applied to one or more datasets on the server. We use this approach to read pre-processed binary files and/or to read and extract the needed parts directly from HDF or HDF-EOS files. These subsets then serve as inputs into GrADS analysis scripts. It can be used in a wide variety of Earth science applications: climate and weather events study and monitoring; modeling. It can be easily configured for new applications.

Leptoukh, Gregory G.↗
NASA NTRS2014

LISA Pathfinder and eLISA news

Two important gatherings of the space-based gravitational-wave detector community were held in Zurich, Switzerland this past March. The first was a meeting of the Science Working Team for LISA Pathfinder (LPF), a dedicated technology demonstrator mission for a future LISA-like gravitational wave observatory. LPF is entering an extremely exciting phase with launch less than 15 months away. All flight components for both the European science payload, known as the LISA Technology Package (LTP), and the NASA science payload, known as the Space Technology 7 Disturbance Reduction System (ST7-DRS), have been delivered and are undergoing integration. The final flight component for the spacecraft bus, a cold-gas thruster based on the successful GAIA design, will be delivered later this year. Current focus is on completing integration of the science payload (see Figures 1 and 2) and preparation for operations and data analysis. After a launch in Summer 2015, LPF will take approximately 90 days to reach its operational orbit around the Earth-Sun Lagrange point (L1), where it will begin science operations. After 90 days of LTP operations followed by 90 days of DRS operations, LPF will have completed its prime mission of paving the way for a space-based observatory of gravitational waves in the milliHertz band. Immediately following the meeting of the LPF team, the eLISA consortium held its third progress meeting. The consortium (www.elisascience.org) is the organizing body of the European space-based gravitational-wave community, and it was responsible for the "The Gravitational Universe" whitepaper that resulted in the November 2013 election of a gravitational-wave science theme for ESA's Cosmic Visions L3 opportunity. In preparation for an L3 mission concept call, which is expected later this decade, and for launch in the mid 2030s, the eLISA consortium members are coordinating technology development and mission study activities which will build on the LPF results. The final mission concept is expected to include some international (non-European) contributions, and NASA has expressed an interest in participating in this ground-breaking mission. The US research community supports such a collaboration, or any other mission scenario that achieves the high-priority science of a space-based gravitational-wave observatory at the earliest possible date.

pathfinder↗
NASA NTRSDate not supplied

Distributed Lunar Data Platform with Advanced Machine Learning Capabilities in Support of Lunar Science and Exploration

The United States 2020 Space Policy directive declares that NASA, in cooperation with private industry, will “extend human economic activity into deep space by establishing a permanent human presence on the Moon”. This goal will require advanced data management, as well as analysis, modeling and representation of lunar information in order to prepare for Artemis human missions, lunar science investigations and exploration. To meet this requirement, we conceptualize and present an implementation strategy for a distributed platform for lunar data retrieval, inferencing and analysis, which will be based on federated learning and the NASA Celestial Mapping System (CMS). In addition to demonstrating the imperative of enabling lunar-borne data to remain in-situ but still accessible, this presentation will also include examples of how third parties could contribute both datasets and new functionality into this platform using an AI-based data import pipeline and a plug-in architecture respectively.

Artificial Intelligence↗
NASA NTRS2017

Distinct Chlorine Isotopic Reservoirs on Mars: Implications for Character, Extent and Relative Timing of Crustal Interaction with Mantle-Derived Magmas, Evolution of the Martian Atmosphere, and the Building Blocks of an Early Mars

The style, magnitude, timing, and mixing components involved in the interaction between mantle derived Martian magmas and Martian crust have long been a point of debate. Understanding this process is fundamental to deciphering the composition of the Martian crust and its interaction with the atmosphere, the compositional diversity and oxygen fugacity variations in the Martian mantle, the bulk composition of Mars and the materials from which it accreted, and the noble gas composition of Mars and the Sun. Recent studies of the chlorine isotopic composition of Martian meteorites imply that although the variation in delta (sup 37) Cl is limited (total range of approximately14 per mille), there appears to be distinct signatures for the Martian crust and mantle. However, there are potential issues with this interpretation. New Cl isotope data from the SAM (Sample Analysis at Mars) instrument on the Mars Science Lab indicate a very wide range of Cl isotopic compositions on the Martian surface. Recent measurements by [10] duplicated the results of [7,8], but placed them within the context of SAM surface data. In addition, Martian meteorite Chassigny contains trapped noble gases with isotopic ratios similar to solar abundance, and has long been considered a pristine, mantle derived sample. However, previous studies of apatite in Chassigny indicate that crustal fluids have interacted with regions interstitial to the cumulus olivine. The initial Cl isotope measurements of apatite in Chassigny suggest an addition of crustal component to this lithology, apparently contradicting the rare gas data. Here, we examine the Cl isotopic composition of multiple generations and textures of apatite in Chassigny to extricate the crustal and mantle components in this meteorite and to reveal the style and timing of the addition of crustal components to mantle-derived magmas. These data reveal distinct Martian Cl sources whose signatures have their origins linked to both the early Solar System and the evolving Martian atmosphere.

Shearer, C. K.↗
DOE OSTI2025

Advanced Interactive 3D Visualization Tool for Customizable Analyses of Tomography Datasets in Material Science

Current methods for visualizing and analyzing 3D tomography datasets in materials science often lack the interactivity and depth required for detailed structural insights. This limitation restricts a researchers' ability to accurately interpret complex data, which is critical for advancing material innovations and understanding structural properties. To address this issue, we have developed a novel, web-based interactive 3D visualization and analysis tool from the Trame framework that offers customizable features to enhance data interpretability. The tool allows users to adjust parameters such as visible range, slice planes, data rotation, and layering, providing a more detailed and dynamic view of complex structures. Its user-friendly web interface increases the accessibility and ease of use for both novice and experienced researchers, to visualize large volumetric datasets. The tool supports a diverse range of data formats, making it versatile for various research applications. Unique capabilities include real-time data manipulation, automated feature detection, context-sensitive feedback, and real-time volume calculations and distributions per sliced region or layer, alongside the ability to quickly generate high-quality screenshots and videos for presentations and reports. These advancements offer a comprehensive solution for enhanced 3D data exploration, significantly improving the analysis process and communication of results in materials science.

36 - MATERIALS SCIENCE↗
NASA NTRS1984

Large-scale analysis and forecast experiments with wind data from the Seasat A scatterometer

A series of data assimilation experiments is performed to assess the impact of Seasat A satellite scatterometer (SASS) wind data on Goddard Laboratory for Atmospheric Sciences (GLAS) model forecasts. The SASS data are dealiased as part of an objective analysis system utilizing a three-pass procedure. The impact of the SASS data is evaluated with and without temperature soundings from the NOAA 4 Vertical Temperature Profile Radiometer (VTPR) instrument in order to study possible redundancy between surface wind data and upper air temperature data. In the northern hemisphere the SASS data are generally found to have a negligible effect on the forecasts. In the southern hemisphere the forecast impact from SASS data is somewhat larger and primarily beneficial in the absence of VTPR data. However, the inclusion of VTPR data effectively eliminates the positive impact over Australia and South America. This indicates that SASS data can be beneficial for numerical weather prediction in regions with large data gaps, but in the presence of satellite soundings the usefulness of SASS data is significantly reduced.

Baker, W. E.↗
NASA NTRS2001

Earth Remote Sensing: A Column Closure Approach

Entering the new millennium, there is no doubt that scientists expand greatly their scientific knowledge of the Earth system by utilizing unique capabilities from the vantage points of space. These global satellite observations include the NASA/NOAA Pathfinder and other ongoing data analysis projects, the Earth Observing System (EOS) program, the Earth System Science Pathfinders (ESSP) small research satellite missions, multi-agency planning for the National Polar-orbiting Operational Environmental Satellite (NPOESS), and other international satellite missions. However, using satellite remotely sensed data alone cannot explore fully the physical processes and energetic balance involved in our changing climate. To close the loop, the ground-based remote sensing and airborne in situ measurements are required. This talk provides an overview of the general strategy of Earth remote sensing for a column closure approach and discusses necessary instrumentation.

Tsay, Si-Chee↗
DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗
NASA NTRS2019

Data Analysis Challenges for Multi-Messenger Astrophysics

Recent multi-messenger observations of gravitational-wave and high-energy neutrino sources together with electromagnetic signatures have opened new ways of observing the Universe. These promise a future in which physics and astronomy will be advanced by combining observations and data from across the electromagnetic spectrum with gravitational waves and neutrinos. We consider the challenges the field is facing in fully utilizing data for multi-messenger astrophysics. Such data come from heterogeneous detector networks and standards, and their analysis is often time-critical to guide further observations. In this area, science capabilities depend on the interplay among observation, theory and computational/modeling work. Advances in data science and computing present additional opportunities and considerations in analyzing such data. We invited ADASS participants to a Birds of a Feather session to engage in discussion on the challenges and opportunities in data analysis for multimessenger astrophysics.

Peter S Shawhan↗