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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Astrophysics of Super-Massive Black Hole Mergers

We present here an overview of recent work in the subject of astrophysical manifestations of super-massive black hole (SMBH) mergers. This is a field that has been traditionally driven by theoretical work, but in recent years has also generated a great deal of interest and excitement in the observational astronomy community. In particular, the electromagnetic (EM) counterparts to SMBH mergers provide the means to detect and characterize these highly energetic events at cosmological distances, even in the absence of a space-based gravitational-wave observatory. In addition to providing a mechanism for observing SMBH mergers, EM counterparts also give important information about the environments in which these remarkable events take place, thus teaching us about the mechanisms through which galaxies form and evolve symbiotically with their central black holes.

BLACK HOLE↗
NASA NTRSDate not supplied

Intelligent Change Detection System: Autonomous Intelligent Machine Agent Model Development

NASA’s significant role in facilitating the harmonious integration of unmanned aircraft systems (UAS), with other aerial vehicles operating in the National Airspace System (NAS), has revealed a need for more advanced technological tools than are being utilized currently. This technology would lend itself to significantly assisting Direct-Action Aviation Personnel (DAAP) with the ingress and egress of UAS operations within the NAS. Providing research findings that would reduce technical barriers, associated with UAS-NAS integration, has been a persistent effort by both NASA and the FAA. One such research effort, pursued by NASA’s Transformative Tools and Technologies – Revolutionary Aviation Mobility (T3-RAM) project, is the development of an autonomous intelligent machine (AIM) agent that would aid DAAP functioning as implemented in remote ground control stations (RGCS). This evolution of the “human-machine” symbiosis, within the aviation environment, is necessary for many reasons. For example, there are projections of large increases to the 864,000 registered UAS and 45,000 aviation operations taking place in the NAS each day. With a data output range from 1 to 20 terabytes per flight or each aerial vehicle, which is projected to have proportional rate increase to that of registered UASs. It is evident, that due to the projected increase of UASs and their generated data, the human-agent’s data managing capabilities will be quickly overwhelmed by the enormous amounts of data emanating in the NAS. The research efforts presented in this paper puts forward results from the development, assessment, and verification of a previously conceptualized AIM-Agent that combats actionable-data (information) errors resulting from the visual perception phenomenon known as “change blindness” (CB). CB has been identified as one of the main culprits of information erroring encountered within the ground control station operator (GCSO) community.

Change Blindness↗
NASA NTRS2014

Potential of Pest and Host Phenological Data in the Attribution of Regional Forest Disturbance Detection Maps According to Causal Agent

Near real time forest disturbance detection maps from MODIS NDVI phenology data have been produced since 2010 for the conterminous U.S., as part of the on-line ForWarn national forest threat early warning system. The latter has been used by the forest health community to identify and track many regional forest disturbances caused by multiple biotic and abiotic damage agents. Attribution of causal agents for detected disturbances has been a goal since project initiation in 2006. Combined with detailed cover type maps, geospatial pest phenology data offer a potential means for narrowing the candidate causal agents responsible for a given biotic disturbance. U.S. Aerial Detection Surveys (ADS) employ such phenology data. Historic ADS products provide general locational data on recent insect-induced forest type specific disturbances that may help in determining candidate causal agents for MODIS-based disturbance maps, especially when combined with other historic geospatial disturbance data (e.g., wildfire burn scars and drought maps). Historic ADS disturbance detection polygons can show severe and extensive regional forest disturbances, though they also can show polygons with sparsely scattered or infrequent disturbances. Examples will be discussed that use various historic disturbance data to help determine potential causes of MODIS-detected regional forest disturbance anomalies.

Spruce, Joseph↗
NASA NTRSDate not supplied

High Sensitive and Low Power Nanosensors for Space and Terrestrial Applications

Nanotechnology offers the ability to work at the molecular level, atom by atom, to create large structures with fundamentally new molecular organization. It is essentially concerned with materials, devices, and systems whose structures and components exhibit novel and significantly improved physical, chemical and biological properties, phenomena, and process control due to their nanoscale size. A nanosensor technology has been developed at NASA Ames using nanostructure, single walled carbon nanotubes (SWNTs). The nanosensors have achieved low detection limit of chemicals in the concentration range of ppm to ppb. Due to large surface area, low surface energy barrier and high thermal and mechanical stability, nanostructured chemical sensors offer higher sensitivity, lower power consumption and a more robust solution than most state-of-the-art systems making them attractive for space and terrestrial applications. An example of a sensor module, the first space flown nano device, will be introduced in this presentation. Such sensors have drawn attention from space community for global weather monitoring, space exploration, life search in the universe, and launch pad fuel leak detection and in-flight cabin air and life support system monitoring, and engine operation monitoring. Additionally, the wireless capability of such sensors can be leveraged to network mobile and fixed-base detection and warning systems for civilian population centers, military bases and battlefields, as well as other high-value or high-risk assets and areas in industry.

Jing Li↗
NASA NTRS2003

Multi-scale variation in spatial heterogeneity for microbial community structure in an eastern Virginia agricultural field

To better understand the distribution of soil microbial communities at multiple spatial scales, a survey was conducted to examine the spatial organization of community structure in a wheat field in eastern Virginia (USA). Nearly 200 soil samples were collected at a variety of separation distances ranging from 2.5 cm to 11 m. Whole-community DNA was extracted from each sample, and community structure was compared using amplified fragment length polymorphism (AFLP) DNA fingerprinting. Relative similarity was calculated between each pair of samples and compared using geostatistical variogram analysis to study autocorrelation as a function of separation distance. Spatial autocorrelation was found at scales ranging from 30 cm to more than 6 m, depending on the sampling extent considered. In some locations, up to four different correlation length scales were detected. The presence of nested scales of variability suggests that the environmental factors regulating the development of the communities in this soil may operate at different scales. Kriging was used to generate maps of the spatial organization of communities across the plot, and the results demonstrated that bacterial distributions can be highly structured, even within a habitat that appears relatively homogeneous at the plot and field scale. Different subsets of the microbial community were distributed differently across the plot, and this is thought to be due to the variable response of individual populations to spatial heterogeneity associated with soil properties. c2003 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

NASA Discipline Life Support Systems↗
NASA NTRS2001

Composition of Hydrothermal Vent Microbial Communities as Revealed by Analyses of Signature Lipids, Stable Carbon Isotopes and Aquificales Cultures

Extremely thermophilic microbial communities associated with the siliceous vent walls and outflow channel of Octopus Spring, Yellowstone National Park, have been examined for lipid biomarkers and carbon isotopic signatures. These data were compared with that obtained from representatives of three Aquificales genera. Thermocrinis ruber. "Thermocrinis sp. HI", Hydrogenobacter thermophilus TK-6, Aquifex pyrophilus and Aquifex aeolicus all contained phospholipids composed not only of the usual ester-linked fatty acids, but also ether-linked alkyls. The fatty acids of all cultured organisms were dominated by a very distinct pattern of n-C-20:1 and cy-C-21 compounds. The alkyl glycerol ethers were present primarily as CIS() monoethers with the expection of the Aquifex spp. in which dialkyl glycerol ethers with a boarder carbon-number distribution were also present. These Aquificales biomarker lipids were the major constituents in the lipid extracts of the Octopus Spring microbial samples. Two natural samples, a microbial biofilm growing in association with deposition of amorphous silica on the vent walls at 92 C, and the well-known 'pink-streamers community' (PSC), siliceous filaments of a microbial consortia growing in the upper outflow channel at 87 C were analyzed. Both the biofilm and PSC samples contained mono and dialkyl glycerol ethers with a prevalence of C-18 and C-20 alkyls. Phospholipid fatty acids were comprised of both the characteristic Aquificales n-C-20:1 and cy-C-21, and in addition, a series of iso-branched fatty acids from i-C-15:0 to i-C-21:0, With i-C-17:0 dominant in the PSC and i-C-19:0 in the biofilm, suggesting the presence of two major bacterial groups. Bacteriohopanepolyols were absent and the minute quantities of archaeol detected showed that Archaea were only minor constituents. Carbon isotopic compositions of the PSC yielded information about community structure and likely physiology. Biomass was C-13-depleted (10.9%) relative to available CO2 from the source water inorganic carbon pool with lipids further depleted by 6.3% relative to biomass The C-20-21 Aquificales fatty acids of the PSC were somewhat heavier than the iso-branched fatty acids. The carbon isotopic signatures of lipid biomarkers were also explored using a pure culture, T ruber, previously isolated from the PSC. Cells grown on C02 with O2 and both H2 and thiosulfate as electron donors were only slightly depleted (3.3%) relative to the C-source while cells grown on formate with O2 showed a major discrimination (19.7%), possibly the result of a metabolic branch point involving the assimilation of C-formate to biomass and the dissimilation to CO2 associated with energy production. T. ruber lipids were slightly heavier than biomass (+1.3%) whether cells were grown using CO2 or formate. Fatty acids from CO2 grown T. ruber cells were a so slightly heavier (average +2.1%) than biomass. The relatively depleted PSC C-20-21 fatty acids suggest that any associated Thermocrinis biomass would also be similarly depleted and much too light to be explained by growth on CO2. The C-fractionations determined with the pure culture suggest that growth of Thermocrinis in the PSC is more likely to occur on formate, presumably generated by geothermal activity. This study points to the value of the analysis of the structural and isotopic composition of lipid blomarkers both in pure culture studies, and in establishing community structure and physiology, as a complement to genomic profiles of microbial diversity. This is especially so when the members of the microbial community are novel and difficult to cultivate in the laboratory.

Jahnke, Linda L.↗
NASA NTRS2019

An Integrated Data Analytics Platform

An Integrated Science Data Analytics Platform is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources which subsequently enable the research community to focus on the investigation rather than spending time on security, data preparation, management, etc. OceanWorks is a NASA technology integration project to establish a cloud-based Integrated Ocean Science Data Analytics Platform at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) for big ocean science. It focuses on advancement and maturity by bringing together several NASA open-source, big data projects for parallel analytics, anomaly detection, in-situ to satellite data matchup, quality-screened data subsetting, search relevancy, and data discovery. Our communities are relying on data distributed through data centers such as the PO.DAAC, COAPS, NCAR, and many others to conduct their research. In typical investigations, scientists would engage in: search for data, evaluate the relevance of that data, download it, and then apply algorithms to identify trends. Such workflow cannot scale if the research involves a massive amount of data or multi-variate measurements. NASA’s Surface Water and Ocean Topography (SWOT) mission is expected to produce massive amount of observational data during its 3-year nominal mission. Collections like SWOT challenges all existing Earth Science data archival, distribution and analysis paradigms. In this paper, we will discuss how OceanWorks enhances the analysis of physical ocean data where the computation is done on an elastic cloud platform next to the archive to deliver fast, web-accessible services for working with oceanographic measurements.

Yang, Chaowei↗
NASA NTRS1980

A continuation of base-line studies for environmentally monitoring space transportation systems at John F. Kennedy Space Center. Executive summary

Studies conducted in and around John F. Kennedy Space Center, Merrit Island, Florida were designed to establish baseline conditions for various attributes of the biotic and abiotic environment. Features were described and quantitatively measured. Certain condition were found to be appropriate for the detection and assessment of possible environmental perturbations caused by future NASA activities. These include plant communities, mammal populations, rainfall chemistry, fish populations, and the status of rare and endangered species. On the basis of statistical analysis of quantitative data and logistic considerations, it was possible to refine the kinds and amounts of measurements needed to evaluate perturbations in selected environmental components.

Stout, I. J.↗
NASA NTRS2001

Dynamic Light Scattering Developed to Look Through the Eye's Window Into the Body

Microgravity researcher Dr. Rafat R. Ansari, from the NASA Glenn Research Center, has found that the eye operates much like a camera and is the "window to the body." The eye contains transparent tissue through which light passes, providing us a view of what's going on inside. These transparent tissues represent nearly every tissue type that exists throughout the body. With the correlations and comparisons of these tissues done at Glenn, we hope to improve doctors' ability to diagnose diseases at much earlier stages. The medical community will be able to look noninvasively and quantitatively into a patient's eyes to detect disease before symptoms appear. Since the eye is easily accessed by light, the optical technologies created at Glenn can be used to evaluate its structure and physiology in health, aging, and disease.

Stauber, Laurel J.↗
NASA NTRS2012

Spitzer Photometry of WISE-Selected Brown Dwarf and Hyper-Lumninous Infrared Galaxy Candidates

We present Spitzer 3.6 and 4.5 micrometer photometry and positions for a sample of 1510 brown dwarf candidates identified by the Wide-field Infrared Survey Explorer (WISE) all-sky survey. Of these, 166 have been spectroscopically classified as objects with spectral types M(1), L(7), T(146), and Y(12). Sixteen other objects are non-(sub)stellar in nature. The remainder are most likely distant L and T dwarfs lacking spectroscopic verification, other Y dwarf candidates still awaiting follow-up, and assorted other objects whose Spitzer photometry reveals them to be background sources. We present a catalog of Spitzer photometry for all astrophysical sources identified in these fields and use this catalog to identify seven fainter (4.5 m to approximately 17.0 mag) brown dwarf candidates, which are possibly wide-field companions to the original WISE sources. To test this hypothesis, we use a sample of 919 Spitzer observations around WISE-selected high-redshift hyper-luminous infrared galaxy candidates. For this control sample, we find another six brown dwarf candidates, suggesting that the seven companion candidates are not physically associated. In fact, only one of these seven Spitzer brown dwarf candidates has a photometric distance estimate consistent with being a companion to the WISE brown dwarf candidate. Other than this, there is no evidence for any widely separated (greater than 20 AU) ultra-cool binaries. As an adjunct to this paper, we make available a source catalog of 7.33 x 10(exp 5) objects detected in all of these Spitzer follow-up fields for use by the astronomical community. The complete catalog includes the Spitzer 3.6 and 4.5 m photometry, along with positionally matched B and R photometry from USNO-B; J, H, and Ks photometry from Two Micron All-Sky Survey; and W1, W2, W3, and W4 photometry from the WISE all-sky catalog.

Griffith, Roger L.↗
NASA NTRS2019

Machine Learning-Based Atmospheric Phenomena Detection Platform

As the number of Earth pointing satellites has increased over the last several decades, the data volume retrieved from instruments onboard these satellites has also increased. It is expected that this trend will continue as more data intensive missions and small satellite constellations are launched. Currently, feature detection - namely atmospheric phenomena - in these datasets is performed manually and is thus not scalable with the growing data archives. Recent advancements in computational efficiency allow for the Earth science community to leverage machine learning to identify interesting atmospheric phenomena. Given the wide range of distinctive features in various atmospheric phenomena, a specialized machine learning model is required for accurate detection of these phenomena independently. The Phenomena Portal, developed at NASA IMPACT, is designed to provide visualization for the output from these machine learning models. In addition, detected events for each atmospheric phenomena are stored in a database that can be used to more easily use/subset larger spatiotemporal datasets. The user interface also incorporates additional features to enhance the user experience including spatiotemporal analysis, multiple base layer images, and a slider to filter events with lower probabilities of positive detection. Each detection supports user feedback on whether the detection is true or false that can then be stored and used to improve the machine learning model performance.

Gurung, Iksha↗
NASA NTRSDate not supplied

Enhanced Monitoring and Forecasting of South Asian Air Quality Episodes with Multi-Sensor Satellite Products and Dispersion Modeling

Air pollution and particulates represent a serious environmental, public health, and overarching societal concern in the Hindu Kush Himalaya (HKH) region of south-central Asia, especially during the boreal winter and spring months. Frequent contributors to poor air quality of the region include dust and particulate matter transport from the Middle East region and western India, persistent nocturnal fog and smog during the stable dry monsoon months, and biomass burning during the pre-monsoon months of boreal spring. Nocturnal and multi-day persistent stable fog and smog events can occur during the stable conditions of the dry monsoon months that lead to hazardous visibility reductions (e.g., at major airports such as Delhi, India)and poor air quality, particularly in urban corridors.Our project goal is therefore to design and implement a robust air quality and chemistry observation and modeling system using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) models. This system effectively assimilates aerosol and trace gas retrievals from geostationary and polar-orbiting satellites to advance the monitoring and prediction capabilities of air quality and visibility reductions throughout the HKH region. For this presentation, we highlight several[multi-spectral] geostationary satellite products and dispersion modeling capabilities being developed and implemented for improved situational awareness and forecasting of air quality episodes.Launched in December 2018, the Advanced Meteorological Imager (AMI) aboard the Geostationary-Korean Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A, or GK2A) provides 16 channels of multi-spectral information similar to the U.S. GOES-16/17 satellites. Proven community recipes for generating multi-spectral Red-Green-Blue (RGB) composite products to detect specific meteorological phenomena are applied to the AMI data for the HKH region. We implemented four such RGB products consisting of truecolor with Rayleigh correction to aid in smoke detection; dust RGB for monitoring larger particulate matter; nighttime microphysics for fog/smog detection;and natural color fire RGB and the shortwave 3.8 micron channel for fire hot-spot detection.We additionally configured HYSPLIT for dust/sand dispersion and concentration forecasts over HKH. The HYSPLIT dispersion simulations are initially being conducted with two different methodologies: (1) dust/sand lofting based on initializing plume releases from visual inspection of the dust RGB imagery; and (2) dust/sand release and dispersion using the internal HYSPLIT algorithm that determines lofting from input wind speeds and prescribed land use. The goal is to develop a near real-time HYSPLIT modeling solution for routinely simulating the transport and dispersion of dust plumes across the HKH region. This presentation illustrates these products and capabilities for various use cases from the 2019 to 2021 period, highlighting a particularly unhealthy episode during late March 2021 across Nepal.

Remote Sensing↗
NASA NTRS2025

Laminae as Structural Biosignatures in NASA’s Life Detection Knowledge Base

Laminae are millimeter-scale features in rocks created by physiochemical processes that can be influenced by the presence and activities of communities of organisms that occur as biofilms and microbial mats. The structure and composition of laminae reflect the processes involved in their formation and can be preserved in the rock record over geologic time, however diagenetic and metamorphic alteration can lead to the loss of primary information and confusion over the interpretation of their origins. As potential records of ancient life, laminae can preserve evidence of microbial activity over billions of years of Earth’s history. On planetary bodies beyond Earth, laminae in sedimentary rocks are the most common sedimentary structure and represent significant features of interest that can record habitable conditions (e.g., the presence of liquid water) at the time of their formation. Here we review the significance of laminae as targets for astrobiological exploration. We discuss common mechanisms by which laminae form in natural environments on Earth, present arguments and evidence for laminae as biosignatures, and describe how such information is incorporated into the NASA Life Detection Knowledge Base.

Astrobiology↗
NASA NTRS2016

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

anomaly detection↗
NASA NTRS2018

Flash Optical Energy from the Geostationary Lightning Mapper

The Geostationary Operational Environmental Satellite -16 (GOES-16) Geostationary Lightning Mapper (GLM) is evaluated for many months during the Post Launch Product Test (PLPT) phase in order to ensure that optimal products are available for both the operational forecasting and broader scientific research communities. The emphasis of the PLPT phase is to validate the GLM performance (i.e., lightning flash detection efficiency, geolocation and time-stamp accuracy) using an extensive network of independent ground-based, in-situ, and space-based reference lightning detection systems. However, another essential aspect of the PLPT phase is to obtain benchmarks of the GLM lightning optical amplitude, so that any long-term degradation in the nadir-staring GLM camera system can be realized and quantitatively assessed. This is accomplished in a straight-forward manner by collecting a very large sample of lightning flashes across many geographical regions in the GLM field-of-view so that statistically meaningful benchmarks of lightning optical amplitude (i.e., optical energy in units of femto-joules per flash) are obtained. The benchmarking is particularly important to follow-on studies that will attempt to incorporate the flash optical energy product into new derived products (e.g., energy-weighted lightning "jump" warning algorithms, and lightning nitrogen oxides production estimates).

Lightning↗
NASA NTRSDate not supplied

Why Near Real-time/Low Latency is Important to Monitor the Changing World

An essential factor for remote sensing data products in impacting decision making is latency, or the time between earth observation and data are available to users. In many applications areas, latency plays an important or even decisive role where low latency earth observations help people to make timely, data-based decisions. Within the open and free NASA resources, NASA’s Land, Atmosphere Near real-time Capability for Earth Observing System (EOS) (LANCE) supports users interested in monitoring a wide variety of natural and man-made phenomena using near real-time (NRT) data products that are made available much quicker than routine processing allows. The combination of all available LANCE satellite products provides global coverage at multiple times per day, which makes it possible to meet user needs in various areas of applications including water resources, agriculture, air quality, wildland fire and many other disasters monitoring and management. As one of the prime users of LANCE, NASA’s Earth Science Applied Sciences Program promotes the use of LANCE data products to demonstrate applications in decision making and facilitates the end-user feedback to the science team to improve data products. One of the most critical applications that we expect data to be processed as close to the user as possible is wildland file response and management. User feedback indicates that LANCE NRT fire data products within 3 hours latency would meet the needs of the wildland fire community. Other examples of earth science application areas for which low latency is particularly important include detecting volcanic eruptions, early warning of disasters, tracking extreme weather events, and monitoring air quality.

Tian Yao↗
NASA NTRS2024

ENLIGHTEN: Electrical Network Line Inspection Guided by High-Speed Technology & Electromagnetic Navigation

Natural disasters are a major cause of power outages, primarily due to their damage to power line infrastructure. As a result, the current state of power maintenance, with a heavy reliance on manual labor, is in trouble. Its lack of speed and autonomy must be addressed to ensure power security for households and institutions worldwide. This innovative approach explores how High-Speed Unmanned Aerial Vehicle (HSUAV) technology can inspect power line infrastructure rapidly in response to natural disaster scenarios. The HSUAV technology will be accompanied by enhanced sensory equipment including light detection and ranging (LiDAR), thermal imaging, and electromagnetic field (EMF) navigation. It would also be supplemented by innovative forms of machine learning models and recharging systems to efficiently detect anomalies in power line infrastructure. The proposed system, ENLIGHTEN, can help restore power in affected communities after a devastating natural disaster, greatly improving relief efforts. In the future, ENLIGHTEN can be expanded beyond the United States and shared worldwide, effectively mitigating the consequences on a larger scale.

UAV systems, Power line management, Natural disast↗
NASA NTRS2017

PandExo: A Community Tool for Transiting Exoplanet Science with JWST and HST

As we approach the James Webb Space Telescope (JWST) era, several studies have emerged that aim to (1) characterize how the instruments will perform and (2) determine what atmospheric spectral features could theoretically be detected using transmission and emission spectroscopy. To some degree, all these studies have relied on modeling of JWST's theoretical instrument noise. With under two years left until launch, it is imperative that the exoplanet community begins to digest and integrate these studies into their observing plans, as well as think about how to leverage the Hubble Space Telescope (HST) to optimize JWST observations. To encourage this and to allow all members of the community access to JWST & HST noise simulations, we present here an open-source Python package and online interface for creating observation simulations of all observatory-supported timeseries spectroscopy modes. This noise simulator, called PandExo, relies on some aspects of Space Telescope Science Institute's Exposure Time Calculator, Pandeia. We describe PandExo and the formalism for computing noise sources for JWST. Then we benchmark PandExoʼs performance against each instrument team's independently written noise simulator for JWST, and previous observations for HST. We find that PandExo is within 10% agreement for HST/WFC3 and for all JWST instruments.

Batalha, Natasha E.↗