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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

New Energetic Radio Pulsars: An Archival X-Ray Survey

This ADP grant was to analyze archival X-ray data obtained in the direction of radio pulsars that were recently discovered as part of the Parkes Multibeam Pulsar Search, which was done using the 64-m Parkes radio telescope in Australia. The survey discovered nearly 700 pulsars, of which roughly three dozen were possible candidates for the detection of X-ray emission. Our team looked at 30 of the most interesting candidates. In most cases, there was insufficient data in the archive to conclude anything. However in several cases, there were interesting archival observations. In three cases, a detailed analysis proved scientifically interesting, and two publications have resulted.

Source record↗
DOE OSTI2024

Physics-Informed Active Learning With Simultaneous Weak-Form Latent Space Dynamics Identification

The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. Here, to enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared with the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5%–10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1%–7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1779x speed-up.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Integrating Multi-agency Data Products in a Cloud-based Platform for Streamlined Discovery, Visualization, and Use

Earth science data users almost always have an interest in utilizing geospatial data from multiple agencies. As computing capability and cloud-based infrastructures accelerate the pace at which scientific research can be done, there is a growing need to enable search, discovery, and use of multi-agency geospatial observations relevant for a common use case - without undergoing the search and discovery process in a less efficient, disparate path with each agency. NASA’s Earth Observing System Data and Information System (EOSDIS) and NOAA’s National Environmental Satellite, Data and Information Service (NESDIS) both support a wide range of Earth science disciplines’ research, operations, and applications activities. Presently, however, there are few examples of data discovery frameworks supporting an inquiry of both NASA’s and NOAA’s extensive archives of Earth observations that are equally suitable for a particular science scenario, regardless of the agency that “owns” the data. NASA and NOAA are collaborating on a data expedition platform for exploring fire weather using data products from both agencies. Users will be able to search, discover, and visualize NASA and NOAA products in one interface. Each agency will curate metadata for its respective datasets, providing for a rich search experience. The collaboration will pilot a shared search interface into these metadata datastores. Data products will be stored in the cloud in cloud-optimized format(s). These formats will allow for optimized data access and visualization to support the “data expedition”. Avenues for further development and application of this cloud-based, multi-agency data provisioning platform will also be discussed.

cloud-based technology↗
DOE OSTI2025

Machine Learned Empirical Numerical Integrator from Simulated Data

Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Broad absorption line quasars in the Dark Energy Spectroscopic Instrument Early Data Release

Broad absorption line (BAL) quasars are characterized by gas clouds that absorb flux at the wavelength of common quasar spectral features, although blueshifted by velocities that can exceed $0.1c$. BAL features are interesting as signatures of significant feedback, yet they can also compromise cosmological studies with quasars by distorting the shape of the most prominent quasar emission lines, impacting redshift accuracy and measurements of the matter density distribution traced by the Lyman $\alpha$ forest. We present a catalogue of BAL quasars discovered in the Dark Energy Spectroscopic Instrument (DESI) survey Early Data Release, which were observed as part of DESI Survey Validation, as well as the first two months of the main survey. We describe our method to automatically identify BAL quasars in DESI data, the quantities we measure for each BAL, and investigate the completeness and purity of this method with mock DESI observations. We mask the wavelengths of the BAL features and re-evaluate each BAL quasar redshift, finding new redshifts which are $243\, {\rm km}\, {\rm s}^{-1}$ smaller on average for the BAL quasar sample. These new, more accurate redshifts are important to obtain the best measurements of quasar clustering, especially at small scales. Finally, we present some spectra of rarer classes of BALs that illustrate the potential of DESI data to identify such populations for further study.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2017

Educational and Scientific Applications of Climate Model Diagnostic Analyzer

Climate Model Diagnostic Analyzer (CMDA) is a web-based information system designed for the climate modeling and model analysis community to analyze climate data from models and observations. CMDA provides tools to diagnostically analyze climate data for model validation and improvement, and to systematically manage analysis provenance for sharing results with other investigators. CMDA utilizes cloud computing resources, multi-threading computing, machine-learning algorithms, web service technologies, and provenance-supporting technologies to address technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. As CMDA technology and infrastructure have matured, we have developed the educational and scientific applications of CMDA. Educationally, CMDA supported the summer school of the JPL Center for Climate Sciences in 2014, 2015, and 2016. In the summer school, the students work on group research projects where CMDA provide datasets, analysis tools, and provenance support utility tools. Each student is assigned to a virtual machine with CMDA installed in Amazon Web Services. Scientifically, we have developed several science use cases of CMDA covering various topics, datasets, and analysis types. Each of the science use cases is described in terms of a scientific goal, datasets used, the analysis tools used, scientific results discovered, an analysis result such as output plots and data files, and a link to the corresponding analysis service call with all the input arguments filled.

Bao, Qihao↗
NASA NTRS1983

Calibration of the Nimbus-7 SMMR. 2: Polarization mixing corrections

Averaged radiance data obtained over the oceans from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) are used to produce an empirical correction algorithm for the polarization mixing which is inherent to the SMMR because of its fixed multichannel receiving horn and its scanning antenna dish. The algorithm, developed without the use of an ocean/atmosphere radiance model, also properly accounts for off center and scan independent polarization mixing, which were discovered after launch as a result of analyzing the aforementioned data. The radiance averaging consisted of collecting data for each beam position of each of the ten radiance channels of SMMR (nominal horizontal and vertical polarizations of the five SMMR wavelengths, 0.81, 1.4, 1.7, 2.8, and 4.6 cm) for about 300 orbits, subdividing the global ocean radiance data into 10 deg latitude bands and ascending (daytime) and descending (nighttime) orbits. This served to smooth out atmospheric and ocean surface variations in order to perform the polarization mixing analysis.

Gloersen, P.↗
NASA NTRS2020

SCExAO/CHARIS High-contrast Imaging of Spirals and Darkening Features in the HD 34700 A Protoplanetary Disk

We present Subaru/SCExAO+Coronagraphic High Angular Resolution Imaging Spectrograph (CHARIS) broadband (JHK-band) integral field spectroscopy of HD 34700 A. CHARIS data recover HD 34700 A's disk ring and confirm multiple spirals discovered by Monnier et al. We set limits on substellar companions of ∼12 MJup at 0.″3 (in the ring gap) and ∼5 M(Jup) at 0."75 (outside the ring). The data reveal darkening effects on the ring and spiral, although we do not identify the origin of each feature such as shadows or physical features related to the outer spirals. Geometric albedos converted from the surface brightness suggest a greater scale height and/or prominently abundant submicron dust at position angles between ∼45° and 90°. Spiral fitting resulted in very large pitch angles (∼30°–50°); a stellar flyby of HD 34700 B or infall from a possible envelope is perhaps a reasonable scenario to explain the large pitch angles.

Taichi Uyama↗
NASA NTRSDate not supplied

A Newly Developing Community-Oriented Data System from NASA GES DISC

Data services are essential to facilitate data access and to aid efficiency of conducting research and application activities. With emerging technologies such as cloud computing and AI/ML (Artificial Intelligence/Machine Learning) leading the pace of the data world, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), home to the permanent archive for multidisciplinary Earth Observation (EO) geospatial data to study atmospheric composition, weather and climate variability, and water and energy cycles is no exception.Interfacing directly with users as part of data center work, we understand the challenges for the required time and effort to discover, visualize, and analyze large varieties and quantities of Earth Observation information for research, monitoring, and decision-making, largely due to the existing data and information systems aim to support experienced users, but has been proved difficult for non-earth scientists and new users that are unfamiliar with the variety of formats and structures in which data, metadata, and information are stored, as well as the required methods to use them. To address these challenges, I will update our latest activities with regard to water-and energy-related products and community-oriented and user-friendly services at the GES DISC, including our plans for the emerging technologies.

Jennifer Wei↗
NASA NTRS1994

Gamma-ray bright quasars and BL Lacertae objects: EGRET data

The Compton Gamma-Ray Observatory (CGRO) Instruments, in particular, EGRET, have recently discovered new, gamma-ray bright active galactic nuclei (AGNs)(fluxes approximately 0.5-3 times as bright as 3C 273). Yet current sample sizes are modest (for example, EGRET as of 1993 January has detected 17 quasars and five BL Lac objects), and the sources probe only a small range in flux. Only by using a large, unbiased sample of AGNs can we answer key questions about source evolution, contributions to the gamma-ray background, and the different gamma-ray properties of quasars and BL Lac objects. Therefore, we need gamma-ray data on a much larger number of AGNs. We are currently pursuing two approaches toward remedying this situation: first, new (phase 2) OSSE, COMPTEL, and EGRET observations of X-ray selected AGNs predicted to be gamma-ray bright on simple assumptions; even an upper limit can set constraints on continuum models (e.g., pair production, synchrotron self-Compton); second, searches for radio-selected AGNs in wide-field EGRET data obtained during the pointings. For these AGN, we expect a large number of faint sources (i.e., mainly low signal-to-noise ratio detections and upper limits), a continuous progression down to the limiting EGRET sensitivity. Although special techniques must be employed to deal carefully with low-significance sources, the end product will be a much larger sample than currently available on which to base deductions about the gamma-ray properties of AGNs. Here we report the first results of this program. We find an upper limit for 3C 382 and demonstrate, using the 3C 382 field, how serendipitous blazars can systematically searched for in existing EGRET public (Phase 1) data. Preliminary results indicate that synchrotron self-Compton processes dominate.

Schachter, Jonathan↗
NASA NTRSDate not supplied

NASA’s Near-Earth Object Observations Program: Abbreviated History and Update

In the wake of Comet Shoemaker-Levy 9’s (D/1993 F2) collision with Jupiter, NASA’s Near-Earth Object (NEO) Observations Program was established more than two decades ago. The NEO Observations Program is responsible for finding, tracking, and characterizing NEOs. Since the Program’s inception in 1998, NASA-funded efforts have discovered more than 98% of the more than 30,000 NEOs currently known. This unique NASA Program supports several NEO surveys that contribute to a sustained and productive campaign to find and track NEOs; collecting data of sufficient precision to allow accurate predictions of the future trajectories of discovered objects. It is a key element of the Planetary Defense Coordination Office (PDCO). The Program also sponsors applied planetary science research conducted at NASA field centers, astronomical observatories, and other locations around the United States. The PDCO relies on data from projects supported by the NEO Observations Program which, in turn, also coordinates NEO observation efforts conducted at ground-based observatories sponsored by the National Science Foundation (NSF) and the space domain awareness facilities of the United States Space Force (USSF). PDCO’s current priority is the NEO Surveyor Mission (NEOSM) , launched away from the vicinity of Earth to SEL1 and working in coordination with large ground-based surveys could complete the NEO survey within a decade following launch. NEOSM’s has three primary mission objectives: - assess the current risk to the Earth of asteroid impact(s); - examine the origin and ultimate fate of the asteroid population (within our solar system); - to find suitable, low-Δv targets for future robotic and piloted missions of exploration Operating at SEL1, NEOSM will also find NEOs with long synodic periods (i.e., several decades or longer) and unfavorable orbital viewing geometries. NEOSM will obviate the inherent Earth-based geometric observing handicap.

NEO↗
DOE OSTI2025

Determination of the spin and parity of all-charm tetraquarks

The traditional quark model accounts for the existence of baryons, such as protons and neutrons, which consist of three quarks, as well as mesons, composed of a quark–antiquark pair. Only recently has substantial evidence started to accumulate for exotic states composed of four or five quarks and antiquarks. The exact nature of their internal structure remains uncertain. Here we report the first measurement of quantum numbers of the recently discovered family of three all-charm tetraquarks, using data collected by the CMS experiment at the Large Hadron Collider from 2016 to 2018 . The angular analysis techniques developed for the discovery and characterization of the Higgs boson have been applied to the new exotic states. Here we show that the quantum numbers for parity P and charge conjugation C symmetries are found to be +1. The spin J of these exotic states is determined to be consistent with 2ħ, while 0ħ and 1ħ are excluded at 95% and 99% confidence levels, respectively. The J PC = 2 ++ assignment implies particular configurations of constituent spins and orbital angular momenta, which constrain the possible internal structure of these tetraquarks.

Physics↗
NASA NTRS1993

Evidence for a near-Earth asteroid belt

In January 1991, the 0.9-m Spacewatch telescope made the first observation of an asteroid outside Earth's atmosphere but in the neighborhood of the Earth-moon system. Since then, more than 40 Earth-approaching asteroids have been discovered, including 13 smaller than 50 m. Using these data, one of us has shown that there is an excess of Earth-approaching asteroids with diameters less than 50 m, relative to the population inferred from the distribution of larger objects. Here we argue that these smaller objects - characterized by low eccentricities, widely ranging inclinations and unusual spectral properties - form a previously undetected asteroid belt concentrated near Earth. The recent discovery of additional small Earth-approaching asteroids supports this conclusion.

Rabinowitz, D. L.↗
NASA NTRS1993

Infrared spectroscopy of Jupiter and Saturn

Infrared spectroscopy provides unique insights into the chemistry and dynamics of the atmospheres of Jupiter, Saturn, and Titan. In 1991 we obtained data at J, H, K, and M and made repeated observations of Titan's albedo as the satellite orbited Saturn. The J albedo is 12% +/- 3% greater than the albedo measured in 1979; the H and K albedos are the same. There was no evidence for variations at any wavelength over the eastern half of Titan's orbit. We also obtained low resolution (R=50) spectra of Titan between 3.1 and 5.1 microns. The spectra contain evidence for CO and CH3D absorptions. Spectra of Callisto and Ganymede in the 4.5 micron spectral region are featureless and give albedos of 0.08 and 0.04 respectively. If Titan's atmosphere is transparent near 5 microns, its surface albedo there is similar to Callisto's. In 1992 and 1993 we obtained further spectroscopic data of Titan with the UKIRT CGS4 spectrometer. We discovered two unexpected and unexplained spectral features in the 3-4 micron spectrum of Titan. An apparent emission feature near the 3 micron (nu sub 3) band of methane indicated temperatures higher than known to be present in Titan's upper stratosphere and may be caused by unexpected non-LTE emission. An absorption feature near 3.47 microns may be caused by absorption in solid grains or aerosols in Titan's clouds. The feature is similar but not identical to organics in the interstellar matter and in comets.

Knacke, Roger F.↗
NASA NTRS1994

Infrared spectroscopy of Jupiter and Saturn

Infrared spectroscopy provides unique insights into the chemistry and dynamics of the atmospheres of Jupiter and Saturn -- and of Titan, the enigmatic satellite of Saturn. The 5 micron spectral region of these objects is transparent to deep levels, and is therefore particularly useful for the identification of molecules in the deep atmosphere at very low (parts per billion) concentrations. In 1991 we obtained data at J, H, K, and M and made repeated observations of Titan's albedo as the satellite orbited Saturn. The J albedo is 12 + -3% greater than the albedo measured in 1979; the H and K albedos are the same. There was no evidence for variations at any wavelength over the eastern half of Titan's orbit. We also obtained low resolution (R = 50) spectra of Titan between 3.1 and 5.1 microns. The spectra contain evidence for CO and CH3D absorptions. Spectra of Callisto and Ganymede in the 4.5 micron spectral region are featureless and give albedos of 0.08 and 0.04 respectively. If Titan's atmosphere is transparent near 5 microns, its surface albedo there is similar to Callisto's. In 1992 and 1993 we obtained further spectroscopic data of Titan with the UKIRT CGS4 spectrometer. We discovered two unexpected and unexplained spectral features in the 3-4 micron /spectrum of Titan. An apparent emission feature near the 3 micron (nu3) band of methane indicates temperatures higher than known to be present in Titan's upper stratosphere and may be caused by unexpected non-LTE emission. An absorption feature near 3.47 microns may be caused by absorption in solid grains or aerosol's in Titan's clouds. The feature is similar, but not identical to organics in the interstellar matter and in comets.

Knacke, Roger F.↗
NASA NTRS2020

TOI-824 b: A New Planet on the Lower Edge of the Hot Neptune Desert

We report the detection of a transiting hot Neptune exoplanet orbiting TOI-824 (SCR J1448-5735), a nearby (d = 64 pc) K4V star, using data from the Transiting Exoplanet Survey Satellite. The newly discovered planet has a radius Rp = 2.93 ± 0.20 Rꚛ and an orbital period of 1.393 days. Radial velocity measurements using the Planet Finder Spectrograph and the High Accuracy Radial velocity Planet Searcher spectrograph confirm the existence of the planet, and we estimate its mass to be 18.47 ± 1.84 Mꚛ. The planet's mean density is ρ(p)= 4.03 (+0.98,-0.78)g/cu. cm, making it more than twice as dense as Neptune. TOI-824 b's high equilibrium temperature makes the planet likely to have a cloud-free atmosphere, and thus it is an excellent candidate for follow-up atmospheric studies. The detectability of TOI-824 b's atmosphere from both ground and space is promising and could lead to the detailed characterization of the most irradiated small planet at the edge of the hot Neptune desert that has retained its atmosphere to date.

Jennifer A. Burt↗
NASA NTRS2021

K2-138 g: Spitzer Spots a Sixth Planet for the Citizen Science System

K2 greatly extended Kepler's ability to find new planets, but it was typically limited to identifying transiting planets with orbital periods below 40 days. While analyzing K2 data through the Exoplanet Explorers project, citizen scientists helped discover one super-Earth and four sub-Neptune sized planets in the relatively bright (V = 12.21, K = 10.3) K2-138 system, all which orbit near 3:2 mean-motion resonances. The K2 light curve showed two additional transit events consistent with a sixth planet. Using Spitzer photometry, we validate the sixth planet's orbital period of 41.966 ± 0.006 days and measure a radius of 3.44 (+0.32,-.031)Rꚛ, solidifying K2-138 as the K2 system with the most currently known planets. There is a sizeable gap between the outer two planets, since the fifth planet in the system, K2-138 f, orbits at 12.76 days. We explore the possibility of additional nontransiting planets in the gap between f and g. Due to the relative brightness of the K2-138 host star, and the near resonance of the inner planets, K2-138 could be a key benchmark system for both radial velocity and transit-timing variation mass measurements, and indeed radial velocity masses for the inner four planets have already been obtained. With its five sub-Neptunes and one super-Earth, the K2-138 system provides a unique test bed for comparative atmospheric studies of warm to temperate planets of similar size, dynamical studies of near-resonant planets, and models of planet formation and migration.

Kevin K. Hardegree-Ullman↗
DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is continuous generation of an extremely large amount of equipment reliability (ER) data. These data elements come in textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) forms. They provide system engineers with valuable insights and information by discovering anomalous behaviors or degradation trends, identifying possible causes behind such behaviors and trends, and predicting their direct consequences. This paper directly targets the knowledge generation from ER data by putting “data into context.” We employ model-based system engineering (MBSE) of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by first identifying which of the developed MBSE elements they are referring to. This task is harder for textual data since the information contained in issue or maintenance reports needs to be “understood” by a computational tool. We called this process “knowledge extraction” since our methods extract knowledge from textual data. Last, once numeric and textual ER data elements have been processed and “understood,” we discover possible cause-effect relations among them. This is performed by observing whether a logical connection through the MBSE models exists, and if there is a temporal relationship among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 - MATHEMATICS AND COMPUTING↗