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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Modeling Forest Understory Fires in an Eastern Amazonian Landscape

Forest understory fires are an increasingly important cause of forest impoverishment in Ammonia, but little is known of the landscape characteristics and climatic phenomena that determine their occurrence. We developed empirical functions relating the occurrence of understory fires to landscape features near Paragominas, a 35- yr-old ranching and logging center in eastern Ammonia. An historical sequence of maps of forest understory fire was created based on field interviews With local farmers and Landsat TM images. Several landscape features that might explain spatial variations in the occurrence of understory fires were also mapped and co-registered for each of the sample dates, including: forest fragment size and shape, forest impoverishment through logging and understory fires, source of ignition (settlements and charcoal pits), roads, forest edges, and others. The spatial relationship between forest understory fire and each landscape characteristic was tested by regression analyses. Fire probability models were then developed for various combinations of landscape characteristics. The analyses were conducted separately for years of the El Nino Southern Oscillation (ENSO), which are associated with severe drought in eastern Amazonia, and non-ENS0 years. Most (91 %) of the forest area that burned during the 10-yr sequence caught fire during ENSO years, when severe drought may have increased both forest flammability and the escape of agricultural management fires. Forest understory fires were associated with forest edges, as reported in previous studies from Ammonia. But the strongest predictor of forest fire was the percentage of the forest fragment that had been previously logged or burned. Forest fragment size, distance to charcoal pits, distance to agricultural settlement, proximity to forest edge, and distance to roads were also correlated with forest understory fire. Logistic regression models using information on fragment degradation and distance to ignition sources accurately predicted the location of lss than 80% of the forest fires observed during the ENSO event of 1997- 1998. In this Amazon landscape, forest understory fire is a complex function of several variables that influence both the flammability and ignition exposure of the forest.

Alencar, A. A. C.↗
NASA NTRS2006

Grain Surface Chemistry and the Composition of Interstellar Ices

Submicron sized dust grains are an important component of the interstellar medium. In particular they provide surface where active chemistry can take place. At the low temperatures (-10 K) of the interstellar medium, colliding gas phase species will stick, diffuse, react, and form an icy mantle on these dust grains. This talk will review the principles of grain surface chemistry and delineate important grain surface routes, focusing on reactions involving H, D, and O among each other and with molecules such as CO. Interstellar ice mantles can be studied through the fundamental vibrations of molecular species in the mid-infrared spectra of sources embedded in or located behind dense molecular clouds. Analysis of this type of data has provided a complex view of the composition of these ices and the processes involved. Specifically, besides grain surface chemistry, the composition of interstellar ices is also affected by thermal processing due to nearby newly formed stars. This leads to segregation between different ice components as well as outgassing. The latter results in the formation of a so-called Hot Core region with a gas phase composition dominated by evaporated mantle species. Studies of such regions provide thus a different view on the ice composition and the chemical processes involved. Interstellar ices can also be processed by FUV photons and high energy cosmic ray ions. Cosmic ray processing likely dominates the return of accreted species to the gas phase where further gas phase reactions can take place. These different chemical routes towards molecular complexity in molecular clouds and particularly regions of star formation will be discussed.

Tielens, A. G. G. M.↗
NASA NTRS2009

Lightning Pin Injection Testing on MOSFETS

Lightning transients were pin-injected into metal-oxide-semiconductor field-effect transistors (MOSFETs) to induce fault modes. This report documents the test process and results, and provides a basis for subsequent lightning tests. MOSFETs may be present in DC-DC power supplies and electromechanical actuator circuits that may be used on board aircraft. Results show that unprotected MOSFET Gates are susceptible to failure, even when installed in systems in well-shielded and partial-shielded locations. MOSFET Drains and Sources are significantly less susceptible. Device impedance decreased (current increased) after every failure. Such a failure mode may lead to cascading failures, as the damaged MOSFET may allow excessive current to flow through other circuitry. Preliminary assessments on a MOSFET subjected to 20-stroke pin-injection testing demonstrate that Breakdown Voltage, Leakage Current and Threshold Voltage characteristics show damage, while the device continues to meet manufacturer performance specifications. The purpose of this research is to develop validated tools, technologies, and techniques for automated detection, diagnosis and prognosis that enable mitigation of adverse events during flight, such as from lightning transients; and to understand the interplay between lightning-induced surges and aging (i.e. humidity, vibration thermal stress, etc.) on component degradation.

Ely, Jay J.↗
NASA NTRS2010

Mars Science Laboratory Sample Acquisition, Sample Processing and Handling: Subsystem Design and Test Challenges

The Sample Acquisition/Sample Processing and Handling subsystem for the Mars Science Laboratory is a highly-mechanized, Rover-based sampling system that acquires powdered rock and regolith samples from the Martian surface, sorts the samples into fine particles through sieving, and delivers small portions of the powder into two science instruments inside the Rover. SA/SPaH utilizes 17 actuated degrees-of-freedom to perform the functions needed to produce 5 sample pathways in support of the scientific investigation on Mars. Both hardware redundancy and functional redundancy are employed in configuring this sampling system so some functionality is retained even with the loss of a degree-of-freedom. Intentional dynamic environments are created to move sample while vibration isolators attenuate this environment at the sensitive instruments located near the dynamic sources. In addition to the typical flight hardware qualification test program, two additional types of testing are essential for this kind of sampling system: characterization of the intentionally-created dynamic environment and testing of the sample acquisition and processing hardware functions using Mars analog materials in a low pressure environment. The overall subsystem design and configuration are discussed along with some of the challenges, tradeoffs, and lessons learned in the areas of fault tolerance, intentional dynamic environments, and special testing

Jandura, Louise↗
NASA NTRS2009

Designing Struts for the Low-Fidelity Orion Cockpit Mockup

The objective of the project was to design and construct nine struts to be installed in the low-fidelity Orion cockpit mockup (Rev F; located at NASA s Johnson Space Center in Houston, TX) as simplified representations of the existing flight designed struts designed by engineers at Lockheed Martin (the primary contractor of the Orion). The project design included: researching the existing flight designs, brainstorming design upgrades, developing three unrelated three-dimensional (3D) strut designs using Pro/Engineer Wildfire 3.0, choosing the best fit design, locating materials and their sources, implementing the chosen design, and making design modifications. The project resulted in making simple modifications to the existing struts used in the last Orion cockpit mockup. The project is relevant to NASA, because upgrades to the low-fidelity Orion cockpit mockup progresses NASA s goals of developing and testing a new spacecraft, conducting the spacecraft's first crewed mission by 2015, returning to the moon by 2020, and exploring Mars and other planets in the future.

Lucienne, Runa A.↗
NASA NTRSDate not supplied

The Added Value of SMAP Soil Moisture in Crop Yield Forecasting Over Argentina

Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.

Nazmus Shams Sazib↗
NASA NTRS1994

Io's Heat Flow From Infrared Radiometry: 1983-1993

This paper presents results from a decade of infrared radiometry of IO. Results indicated that a new thermophysical model is required to match all the data. The thermal anomalies are modeled with a total of 10 source components at five locations.

Io↗
NASA NTRS2021

Investigations on the Anthropogenic Reversal of the Natural Ozone Gradient Between Northern and Southern Midlatitudes

Our quantitative understanding of natural tropospheric ozone concentrations is limited by the paucity of reliable measurements before the 1980s. We utilize the existing measurements to compare the long-term ozone changes that occurred within the marine boundary layer at northern and southern midlatitudes. Since 1950 ozone concentrations have increased by a factor of 2.1 ± 0.2 in the Northern Hemisphere (NH) and are presently larger than in the Southern Hemisphere (SH), where only a much smaller increase has occurred. These changes are attributed to increased ozone production driven by anthropogenic emissions of photochemical ozone precursors that increased with industrial development. The greater ozone concentrations and increases in the NH are consistent with the predominant location of anthropogenic emission sources in that hemisphere. The available measurements indicate that this interhemispheric gradient was much smaller and was likely reversed in the pre-industrial troposphere with higher concentrations in the SH. Six Earth system model (ESM) simulations indicate similar total NH increases (1.9 with a standard deviation of 0.3), but they occurred more slowly over a longer time period, and the ESMs do not find higher pre-industrial ozone in the SH. Several uncertainties in the ESMs may cause these model–measurement disagreements: the assumed natural nitrogen oxide emissions may be too large, the relatively greater fraction of ozone injected by stratosphere–troposphere exchange to the NH may be overestimated, ozone surface deposition to ocean and land surfaces may not be accurately simulated, and model treatment of emissions of biogenic hydrocarbons and their photochemistry may not be adequate.

natural tropospheric ozone concentrations↗
NASA NTRS2021

Simulation of Radio Frequency Power Received by a UAV Along Its Flight Path

A ray-tracing electromagnetic simulation using ALTAIR WinProp software was performed to calculate the 2.4 GHz power received by a UAV in flight, both from the intended controller transmitter and from a fictitious interference source of equal power located near the ground. The signal-to-interference ratio was then calculated. For the chosen example, the received power from the controller varied from -88.3 to -70.1 dBm, while the signal-to-interference ratio varied from -10.9 to +11.4 db. This work was done in support of the System-Wide Safety Project Technical Challenge 2 - Emerging Operations at NASA Langley, which is studying methods to avoid interference of UAV control and data relay signals.

Electromagnetic Simulation↗
NASA NTRSDate not supplied

Effects of Porous Gap Fillers on 30P30N Leading-Edge SlatNoise. Part I: Surface Pressure and Acoustics

The leading edge slat of a high-lift system is one of the main contributors to airframe noise during approach. In a previous experimental study, we assessed the performance of an impermeable slat gap filler as a passive flow control device to reduce the slat noise associated with a two-dimensional, three-element high-lift airfoil. The present paper, the first of two parts,represents a follow-on investigation to assess the relative efficacy of permeable gap fillers thatallow successively higher amounts of flow to pass through the gap. To evaluate the influence of this passive flow control device on the acoustics generated by the unsteady flow near the slat, experiments are conducted in an anechoic wind tunnel by mounting a gap filler to the slat element of the two-dimensional 30P30N high-lift configuration. Measurements are performed at a single geometric angle of attack (𝛼𝑘=8◦) and three different flow speeds that correspond to Reynolds numbers of𝑅𝑒𝑐=1.2𝑒6,1.5𝑒6, and1.71𝑒6, respectively. Steady surface pressure measurements are used to gauge the influence of the permeable gap filler treatments on the overall lift. The effect of each treatment on the radiated noise is analyzed via acoustic array measurements, followed by delay-and-sum beamforming to locate the slat noise sources and to provide the integrated acoustic spectra. The porous gap fillers are found to eliminate the narrowband peaks in the acoustic spectra and, also, to yield a 10 dB reduction in the broadband noise in comparison with the baseline case with no gap filler. The porous gap filler with thelowest permeability acts similar to the impermeable gap filler examined previously. However, the aerodynamics and noise reduction both degrade with increasing permeability. An accompanying abstract describes the particle image velocimetry measurements of the flowfield within the slat cove and on either side of the permeable gap f

slat noise↗
DOE OSTI2024

PCOR Partnership Atlas 6th Edition, Revised

The Plains CO 2 Reduction (PCOR) Partnership Atlas provides a profile of CO 2 sources and potential storage locations across the PCOR Partnership region. The revised sixth edition of the Atlas provides an up-to-date look at PCOR Partnership initiative activities, including additional regional characterization and updates on commercial projects. Additional background information on carbon capture, utilization, and storage (CCUS) is included to give a better understanding of how CCUS addresses concerns about climate change while allowing future energy needs to be met.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Quantitative Characterization of Hyper-Local Atmospheric Greenhouse Gas Sources

Atmospheric greenhouse gas (GHG) emissions are often characterized using stationary, tower-based sensors. Ground based sensors reside in the turbulent boundary layer and are subject to intense concentration impulses from hyper-local (<100m) point sources of emissions. These high frequency spikes are often filtered out in broader emission flux studies, losing valuable information about how hyper-local sources influence receptors. In this study, we investigated how empirical atmospheric data can be used to locate and quantify a concurrently measured hyper-local point source in a dense urban setting. An eddy covariance style tower and a low-cost sensor tower were deployed in various locations around an urban, hyper-local CO2/CH4 emissions source (a continuously measured restaurant exhaust vent). A model using different processing and statistical techniques was built to examine the most effective procedures for source isolation, directional location, and emission quantification. Using excess concentrations above a minimum baseline, we identify the source using bivariate polar plots and quantify the relationship between source size, receptor distance, and statistical proxies. Furthermore, we find that varying statistical thresholds allows for identification of less influential sources which are drowned out by larger or closer sources. Finally, we show that large sources can be effectively characterized using low-cost sensors, a valuable outcome informing how networks for monitoring larger areas could be implemented. This work may provide a basis for source identification and monitoring protocols for networks that feature sensors influenced by hyper-local point sources, subject to site-specific assumptions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2026

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Data items will occasionally be removed from OSM if they are misidentified, if they no longer exist, if they are duplicates of another item, or similar. For that reason, updated versions of this database may not contain all data center locations included in previous versions. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
NASA NTRS1987

Bearing detection in the presence of two sources of varying coherence using the complex cepstrum

The effect of the presence of two acoustic sources (one, the primary, whose location is to be detected) of varying coherence on a cepstral bearing finding procedure is experimentally studied. The coherence between the acoustic sources was altered by adding random noise of various SNR (signal-to-noise ratio) to the input signal of the primary source; the same base signal being fed to both sources. The results demonstrate that, when block liftering is used, the primary source bearing is reliably estimated for coherences as low as gamma sup 2 greater than or approx equal to 0.5. The results also imply that background noise (unreflected) of SNR greater than or approx equal to 10 dB will not markedly affect the accuracy of the bearing estimation algorithm.

Fuller, C. R.↗
NASA NTRS1950

Analysis of Multiengine Transport Airplane Fire Records

An analysis has been made of Civil Aeronautics Administration and Civil Aeronautics Board commercial airplane fire records collected during the 10-year period ending July 1, 1948. The results of the analysis show that: 1. Gasoline was most frequently the initial combustible ignited in flight and ground fires and is considered to be the most hazardous of the combustibles carried. 2. Although electrical-ignition sources are the most frequent flight-fire ignition source by a small margin, the exhaust system is concluded to be the most hazardous ignition source because it is necessarily located near the lubricating-oil and gasoline-plumbing systems and the resulting fires are relatively severe. The electrical-ignition sources usually involve only the electrical insulation and result in small-volume fires. The exhaust system was found to be the most frequent ground-fire ignition source. 3. Engine failures were the most frequent cause of the union of combustible and ignition source that resulted in flight fires. 4. Fuel-plumbing-system failures were the most frequent cause of fires occurring during ground operation. 5. The evidence concerning crash fires was not sufficiently extensive to provide information concerning the factors that affect the start and the spread of fire. In order that future records may be more useful, all crash accidents should be studied to determine why fire does or does not occur and to establish data that relate the occurrence and the spread of fire to airplane design and operation.

Pesman, Gerard J.↗
NASA NTRS2003

Chandra X-Ray Observations of the Spiral Galaxy M81

A Chandra X-Ray Observatory ACIS-S imaging observation is used to study the population of X-ray sources in the nearby Sab galaxy M81 (NGC 3031). A total of 177 sources are detected with 124 located within the D(sub 25) isophote to a limiting X-ray luminosity of approx. 3 x 10(exp 36) ergs/s. Source positions, count rates, luminosities in the 0.3-8.0 keV band, limiting optical magnitudes, and potential counterpart identifications are tabulated. Spectral and timing analysis of the 36 brightest sources are reported including the low luminosity active galactic nucleus, SN 1993J, and the Einstein-discovered ultraluminous X-ray source X6. The nucleus accounts for approx. 86%, or 5 x 10(exp 40) ergs/s, of the total X-ray emission from M81. Its spectrum is well fitted by an absorbed power law with photon index 1.98 +/- 0.08, consistent with previous observations (average index 1.9). SN 1993J has softened and faded since its discovery. At an age of 2594 days, SN 1993J displayed a complex thermal spectrum from a reverse shock rich in Fe L and highly ionized Mg, Si, and S but lacking O. A hard X-ray component, emitted by a forward shock, is also present. X6 is spatially coincident with a stellar object with optical brightness and colors consistent with an O9-B1 main-sequence star. It is also coincident with a weak radio source with a flux density of approx. 95 microJy at lambda = 3.6 cm. The continuum-dominated X-ray spectrum of X6 is most closely reproduced by a blackbody disk model suggesting the X-ray source is an approx. 18 solar mass object accreting at nearly its Eddington limit.

Swartz, Douglas A.↗
NASA NTRS1977

A rocket-borne airglow photometer

The design of a rocket-borne photometer to measure the airglow emission of ionized molecular nitrogen in the 391.4 nm band is presented. This airglow is a well known and often observed phenomenon of auroras, where the principal source of ionization is energetic electrons. It is believed that at some midlatitude locations energetic electrons are also a source of nighttime ionization in the E region of the ionosphere. If this is so, then significant levels of 391.4 nm airglow should be present. The intensity of this airglow will be measured in a rocket payload which also contains instrumentation to measured in a rocket payload which also contains instrumentation to measure energetic electron differential flux and the ambient electron density. An intercomparison of the 3 experiments in a nightime launch will allow a test of the importance of energetic electrons as a nighttime source of ionization in the upper E region.

Paarmann, L. D.↗