SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The NRL instrument aboard the HEAO 1 satellite has detected a faint X-ray source, which has been identified tentatively with the contact binary star (W UMa variable) VW Cephei. Its luminosity is between 3 x 10 to the 30th and 4 x 10 to the 31st ergs/sec (0.1-10 keV), and the results suggest some variation of the X-ray flux with phase.
We succeeded to identify the North Toroidal source by optical observations performed by the SonotaCo Network, which is a TV observation network coordinated by Japanese amateurs. This source has been known only for radar observations until now. The orbits of the optical meteors in the North Toroidal source are relatively large eccentricity and semi-major axis, compared with those of the radar meteors. In this paper, we report the characteristics of this North Toroidal source detected by optical observations, and discuss the possible origin and evolution of this source.
Photoabsorption and fluorescence cross sections of molecules important in the interstellar medium were measured in the 90 to 200 nm region using synchrotron radiation, excimer laser, and condensed discharge lamps as light sources. The quantitative spectroscopic data are currently needed for the modeling of formation and destruction rates of molecules by the interstellar radiation field. Fluorescences from excited photofragments produced by vacuum ultraviolet radiation of molecules are dispersed to identify the emitting species. The fluorescence data are useful for the identification of emission sources in interstellar clouds.
The search for sources of high-energy astrophysical neutrinos can be significantly advanced through a multimessenger approach, which seeks to detect the γ-rays that accompany neutrinos as they are produced at their sources. Multimessenger observations have so far provided the first evidence for a neutrino source, illustrated by the joint detection of the flaring blazar TXS 0506+056 in high-energy (E > 1 GeV) and very-high-energy (VHE; E > 100 GeV) γ-rays in coincidence with the high-energy neutrino IceCube-170922A, identified by IceCube. Imaging atmospheric Cherenkov telescopes (IACTs), namely FACT, H.E.S.S., MAGIC, and VERITAS, continue to conduct extensive neutrino target-of-opportunity follow-up programs. These programs have two components: follow-up observations of single astrophysical neutrino candidate events (such as IceCube-170922A), and observation of known γ-ray sources after the identification of a cluster of neutrino events by IceCube. Here we present a comprehensive analysis of follow-up observations of high-energy neutrino events observed by the four IACTs between 2017 September (after the IceCube-170922A event) and 2021 January. Our study found no associations between γ-ray sources and the observed neutrino events. We provide a detailed overview of each neutrino event and its potential counterparts. Furthermore, a joint analysis of all IACT data is included, yielding combined upper limits on the VHE γ-ray flux.
Four fields containing radio sources with spectral indices greater than unity at low frequencies have been observed over a wide range of radio wavelengths and at X-ray wavelengths. For most of the steep-spectrum sources no optical identifications have been found to the detection limit of the Palomar Schmidt prints, even with the positional precision of about 1 arcsec from VLA maps. This implies that the associated galaxies or clusters lie at a great distance. X-ray emission was not detected from any of the steep-spectrum radio sources. In one case, an extended source appears to be a radio halo in an uncataloged cluster of galaxies at a redshift of 0.125, and it is suggested that this object is a promising candidate for the detection of inverse Compton X-rays, since it is not a high-luminosity source of thermal X-rays.
The International Space Station (ISS) is an on-orbit platform for science utilization in low Earth orbit with multiple sites for external payloads with exposure to the natural and induced environments. Contamination is one of the induced environments that can impact performance, mission success and science utilization on the vehicle. This paper describes the external contamination control requirements and integration process for externally mounted payloads on the ISS. The external contamination control requirements are summarized and a description of the integration and verification process is detailed to guide payload developers in the certification process of attached payloads on the vehicle. A description of the required data certification deliverables covers the characterization of contamination sources. Such characterization includes identification, usage and operational data for each class of contamination source. Classes of external contamination sources covered are vacuum exposed materials, sources of leakage, vacuum venting and thrusters. ISS system level analyses are conducted by the ISS Space Environments Team to certify compliance with external contamination control requirements. This paper also addresses the ISS induced contamination environment at attached payload sites, both at the requirements level as well as measurements made on ISS.
A compact X-ray source near the North Galactic Pole has been detected with the low-energy X-ray telescope aboard the SAS-3 satellite. The spectral temperature of the source is less than 1 million K, and the energy flux at the earth is 3 by 10 to the -10th power erg/sq cm/sec above 0.1 keV. The flux appeared to be constant both on short time scales (10-1000 s), and over the interval from the first SAS-3 observation (June 12, 1975) to the latest (July 15, 1975). The possible identification of this source with the hot white dwarf HZ 43 is discussed.
Variations in the scalar magnetic field (delta B) from the polar orbiting OGO 2, 4, and 6 spacecraft, with supporting vector magnetic field data from surface observatories, were analyzed at dipole latitudes above 55 deg. Although individual satellite passes, at low altitudes, confirm the existence of electrojet currents, neither individual satellite passes nor contours of average delta B are consistent with latitudinally narrow electrojet currents as the principal source of delta B at the satellite. The ratio of delta B magnitudes in positive and negative regions is variable. The characteristics of the negative delta B region indicate a latitudinally broad ionospheric source. Comparison of the surface magnetic disturbance caused by the equivalent current with the measured average surface disturbance shows good agreement except in localized details. Because delta B decreases very slowly with altitude in the positive delta B region, it is not possible to account for this disturbance in terms of ionospheric currents. The contribution to the satellite delta B due to a model electrojet, which reproduced the measured average surface horizontal disturbance, was computed. Definitive identification of all sources of positive delta B is not possible at this time.
A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.
The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.
Preliminary results of an infrared survey of the equatorial sky zone (declinations 10 S to 10 N) with U.S. Air Force satellite sensors show that positions of previously unknown infrared sources are measured with an rms accuracy of 4 arc seconds, which is six times better than the best previous infrared survey. The search area per source for further study is thus 36 times smaller, so that identification of the infrared sources with optical objects in catalogs and sky photographs is facilitated, as is reacquisition of the sources with ground-based infrared telescopes. The survey extends the content of near-infrared source catalogs to lower flux densities and adds information at a wavelength not observable from the ground. Objects found in the survey include cool giants and supergiants, long-period and semi-regular variable stars, and sources identified with faint red stars visible on the Palomar Observatory Sky Survey.
During a pointed ROSAT observation in the direction of the Coma cluster of galaxies an exceptionally bright EUV source, RE J1255+266, was detected serendipitously. The source is located close to the Galactic North Pole, at b(sub II) is approximately or equal to 89 deg. Its observed EUV flux (62 - 110 eV) at the time of the detection was of order 7 x 10(exp -9) ergs/sq cm, making RE J1255+266 temporarily one of the brightest EUV sources on the sky. The EUV flare of RE J1255+266 has a light curve with a decay time of about 0.86 days. With respect to earlier non-detections, the source brightened by a factor of approximately 7000. Such a behavior has not been observed before. Thus, it is unclear what type of source RE J1255+266 might be. Up to now no positive identification with any known source could be obtained. Emission at the position of the source was previously only detected in the 1987 Green Bank radio continuum survey. Simultaneous observations with Compton Gamma Ray Observatory/Burst and Transient Source Experiment (CGRO/BATSE) resulted in non-detections of the source in the 8 - 50 keV energy range.
Since 2002 the National Aeronautics and Space Administration (NASA) has carried out an optical survey of the debris environment in the geosynchronous Earth-orbit (GEO) region with the Michigan Orbital Debris Survey Telescope (MODEST) in Chile. The survey coverage has been similar for 4 of the 5 years allowing us to follow the orbital evolution of Correlated Targets (CTs), both controlled and un-controlled objects, and Un-Correlated Targets (UCTs). Under gravitational perturbations the distributions of uncontrolled objects, both CTs and UCTs, in GEO orbits will evolve in predictable patterns, particularly evident in the inclination and right ascension of the ascending node (RAAN) distributions. There are several clusters (others have used a "cloud" nomenclature) in observed distributions that show evolution from year to year in their inclination and ascending node elements. However, when MODEST is in survey mode (field-of-view approx.1.3deg) it provides only short 5-8 minute orbital arcs which can only be fit under the assumption of a circular orbit approximation (ACO) to determine the orbital parameters. These ACO elements are useful only in a statistical sense as dedicated observing runs would be required to obtain sufficient orbital coverage to determine a set of accurate orbital elements and then to follow their evolution. Identification of the source(s) for these "clusters of UCTs" would be advantageous to the overall definition of the GEO orbital debris environment. This paper will set out to determine if the ACO elements can be used to in a statistical sense to identify the source of the "clustering of UCTs" roughly centered on an inclination of 12deg and a RAAN of 345deg. The breakup of the Titan 3C-4 transtage on February 21, 1992 has been modeled using NASA s LEGEND (LEO-to-GEO Environment Debris) code to generate a GEO debris cloud. Breakup fragments are created based on the NASA Standard Breakup Model (including fragment size, area-to-mass (A/M), and delta-V distributions). Once fragments are created, they are propagated forward in time with a subroutine GEOPROP. Perturbations included in GEOPROP are those due to solar/lunar gravity, radiation pressure, and major geopotential terms. The question to be addressed: are the UCTs detected by MODEST in this inclination/RAAN region related to the Titan 3C-4 breakup? Discussion will include the observational biases in attempting to detect a specific, uncontrolled target during given observing session. These restrictions include: (1) the length of the observing session which is 8 hours or less at any given date or declination; (2) the assumption of ACO elements for detected object when the breakup model predicts debris with non-zero eccentricities; (3) the size and illumination or brightness of the debris predicted by the model and the telescope/sky limiting magnitude.
Chemical characterization of atmospheric organic aerosols using gas chromatography with 70 eV electron ionization mass spectrometry (GC/EI-MS) has been used for decades in advancing molecular marker detection and identification, though primarily through suspect screening and/or targeted analyses. To advance non-targeted analyses of environmental samples, we have catalogued approximately 27 000 mass spectra (MS) of the trimethylsilyl derivatives of semi-volatile organic aerosol (OA) analytes in the open-access University of California Berkeley Goldstein Library of Organic Biogenic Environmental Spectra (UCB-GLOBES). Analytes were observed in ambient samples from the U.S. and the Central Amazon and/or laboratory simulations of secondary OA (SOA) formation. These samples are representative of OA under urban and biomass burning influences as well as SOA derived from biogenic precursors (e.g., isoprene, monoterpenes, sesquiterpenes) and biomass burning intermediates. MS are documented in UCB-GLOBES without regard to known chemical identity, annotated with extensive metadata such as sample source/experimental conditions, any structural information gained from MS analyses, and predicted chemical properties such as average carbon oxidation state and carbon number. UCB-GLOBES MS are compatible for importing into the NIST MS Search program, and we have also provided a Jupyter Notebook for MS visualization and comparisons. We demonstrate the utility of UCB-GLOBES through MS reanalyses of prior analytes observed in ambient data, finding a 20 % reduction in the number of analytes assigned to OA source categories reliant solely on time series correlation and an overall 11 % increase in new MS-based OA source categorization for the Southeast U.S. For 1513 analytes observed previously in the Central Amazon, we found 375 MS matches using UCB-GLOBES vs. 136 MS matches during prior analyses, representing a 14 % gain in newly confirmed or newly categorized OA species. While OA from laboratory oxidation experiments in UCB-GLOBES are highly diverse chemically, on average only 29 % of UCB-GLOBES MS have a mass spectral match to another MS entry in UCB-GLOBES and/or in databases of known compounds (i.e. NIST MS Database, Adams Essential Oil, MANE Flavor and Fragrance Company). This indicates that roughly 70 % of UCB-GLOBES MS are unique thus far, not observed more than once among the laboratory oxidation samples and ambient data in UCB-GLOBES MS. Further, only 18 % can be positively identified using these databases or known authentic standards. This points to a large gap between these laboratory simulations and ambient OA. Overall, the UCB-GLOBES database can be utilized for improving confidence in OA source categorization and/or identification, novel chemical marker discovery, tracking chemical diversity, de novo structure and properties prediction, and improving MS search and matching algorithms. This can ultimately inform future research priorities for the chemical characterization of atmospheric organic samples.
The results of a program of optical identification of the X-ray point sources in the direction of the Large Magellanic Cloud (LMC) are presented. Observational results are combined with accurate positions of about 3-arcsec obtained with the Einstein Observatory Imaging Proportional Counter (IPC). The sources include 13 foreground stars, three background active galaxies, and six LMC members which are known or suspected to be binaries. The 24 other sources in the Einstein LMC survey have only 30-arcsec positions and on average are less luminous and in more crowded regions of the LMC. On the basis of the new identifications, comparisons are made between the overall stellar X-ray population in the LMC and in the Galaxy. Although the mean X-ray luminosity of the LMC sources is significantly higher than that of the Galaxy, the total number of stellar X-ray systems per unit mass is similar in the LMC and the Galaxy.
Time-lapse electrical resistivity tomography (ERT) measurements provide indirect observations of hydrological processes in the Earth's shallow subsurface at high spatial and temporal resolution. ERT has been used in the past decades to detect leaks and monitor the evolution of associated contaminant plumes. Specifically, inverted resistivity images allow visualization of the dynamic changes in the structure of the plume. However, existing methods do not allow the direct estimation of leak parameters (e.g. leak rate, location, etc.) and their uncertainties. We propose an ensemble-based data assimilation framework that evaluates proposed hydrological models against observed time-lapse ERT measurements without directly inverting for the resistivities. Each proposed hydrological model is run through the parallel coupled hydro-geophysical simulation code PFLOTRAN-E4D to obtain simulated ERT measurements. The ensemble of model proposals is then updated using an iterative ensemble smoother. In this paper, we demonstrate the proposed framework on synthetic and field ERT data from controlled tracer injection experiments. Our results show that the approach allows joint identification of contaminant source location, initial release time, and solute loading from the cross-borehole time-lapse ERT data, alongside with an assessment of uncertainties in these estimates. We demonstrate a reduction in site-wide uncertainty by comparing the prior and posterior plume mass discharges at a selected image plane. This framework is particularly attractive to sites that have previously undergone extensive geological investigation (e.g., nuclear sites). It is well suited to complement ERT imaging and we discuss practical issues in its application to field problems.
Rapid economic growth and burgeoning population have contributed to enhanced levels of PM 2.5 concentrations in urban regions of India. Evaluation of ambient air quality facilitates the assessment of effectiveness of emission control measures and early identification of new sources. This study provides a comprehensive statistical analysis of PM 2.5 concentrations in key urban areas across India, including Delhi, Kolkata, Mumbai, Chennai, Hyderabad, and several regional centers. Data from 2017 to 2023 was analyzed using trend analysis, cluster analysis, principal component analysis, and geostatistical interpolation to understand spatiotemporal variations and sources. The analysis reveals significant differences in spatial distribution of PM 2.5 concentrations with high annual averages in urban regions in Indo-Gangetic plain (82–123 μg m −3 ) and relatively lower concentrations (29–46 μg m −3 ) in southern urban areas of Kerala, Tamil Nadu and Andhra Pradesh. Delhi state had the highest 24-averaged PM 2.5 concentrations (112 μg m −3 ) followed by urban regions in Uttar Pradesh, Bihar and West Bengal (94 μg m −3 ). Trend analysis from 2017 to 2023 revealed an overall 2.5% decline in site-wide PM2.5 concentrations, with the exception of Ludhiana, which exhibited a consistent annual increase of 10%. Principal component analysis (PCA) attributes 30% of the variance to wintertime emissions, 13% to biomass burning, and 18% to the regional haze in the northern Indo-Gangetic Plain. Different analyses clearly demonstrates the contribution of biomass burning to pollution in Delhi and surrounding cities. Transboundary pollution to Kolkata is likely from the highly polluted region in Indo-Gangetic Plain. Coastal cities of Mumbai and Chennai has relatively lower pollution attributed to the influence of sea breeze dilution, with mostly local contribution and some potential transport from upwind industry clusters. Hyderabad also has local contribution due to high density of vehicular traffic and local small industries. This study shows that mitigation efforts targeting clusters of regions should be undertaken to curb the high PM2.5 pollution. Policy measures should be implemented both at local and the intra-state level to address shared sources and transport of pollution.
Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. Here, as a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.