Results for “Random ray” — Page 9

NASA NTRS2004

Constraints on the Lithological Variation near the Surface of the HED Planetoid from the Petrology of 91 & 92 Series Antarctic Achondrites

The petrography of a suite of meteorite sections: PCA91006,14; PCA91078,9; PCA91083,6; PCA91159,4; PCA91179,9; PCA91245,9; EET92003,14; EET92004,12; EET92015,4; EET92022,7; EET92026,4; and EET92027,5 is used as an initial sample of the lithological variation on the surface of the HED planetoid (presumably asteroid 4 Vesta). These samples will be combined with much larger arrays of petrographic data for the many Antarctic basaltic achondrites to provide a random sample of the surface of the body. The full variety of the lithologies existing on the parent body is only accessible when the polymict achondrites are considered. The polymict samples contain lithologies that sample multiple provenances as well as those not seen as monolithologic meteorites like eucrites and diogenites. Comparison of lithological variation within individual meteorites provides a subset of variations at the site of last impact. In aggregate, the variations within the achondrites now available may be close to a random sample of the parent body. In combination of microanalytical and imaging techniques now available permit a mass balanced assessment of the distribution and abundance of lithologies to be made. These initial results provide a description of methodology to be tested. All samples were studied and photographed on a polarizing microscope to provide location information. Major mineral phases and randomly selected points (on lines and grids) were analyzed in each thin section. In addition backscattered electron and X-ray imaging of clasts and sections provide the basis for high precision modal analyses of the abundance and distribution of both lithic and mineral clasts. These data provide objective, area based comparisons with other Antarctic samples.

Bjonnes, Emily E.↗

NASA NTRS1997

Accurate Magnetometer/Gyroscope Attitudes Using a Filter with Correlated Sensor Noise

Magnetometers and gyroscopes have been shown to provide very accurate attitudes for a variety of spacecraft. These results have been obtained, however, using a batch-least-squares algorithm and long periods of data. For use in onboard applications, attitudes are best determined using sequential estimators such as the Kalman filter. When a filter is used to determine attitudes using magnetometer and gyroscope data for input, the resulting accuracy is limited by both the sensor accuracies and errors inherent in the Earth magnetic field model. The Kalman filter accounts for the random component by modeling the magnetometer and gyroscope errors as white noise processes. However, even when these tuning parameters are physically realistic, the rate biases (included in the state vector) have been found to show systematic oscillations. These are attributed to the field model errors. If the gyroscope noise is sufficiently small, the tuned filter 'memory' will be long compared to the orbital period. In this case, the variations in the rate bias induced by field model errors are substantially reduced. Mistuning the filter to have a short memory time leads to strongly oscillating rate biases and increased attitude errors. To reduce the effect of the magnetic field model errors, these errors are estimated within the filter and used to correct the reference model. An exponentially-correlated noise model is used to represent the filter estimate of the systematic error. Results from several test cases using in-flight data from the Compton Gamma Ray Observatory are presented. These tests emphasize magnetometer errors, but the method is generally applicable to any sensor subject to a combination of random and systematic noise.

Sedlak, J.↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS1976

Information about accretion flows from X-ray timing of pulsating sources

The response was studied of a rotating neutron star to fluctuating torques and it was found that the observed variations in the pulsation periods of the compact X-ray sources Cen X-3 and Her X-1 could be caused by short time scale fluctuations in the accretion torques acting on the neutron stars. The sizes and rates of the required fluctuations are consistent with current accretion models. Such fluctuations can cause period variations either (a) directly, by causing a random walk of the star's angular velocity or (b) indirectly, by exciting a long-period mode of the neutron star, such as the Tkachenko mode of the rotating neutron superfluid. Phenomena in compact X-ray sources and cataclysmic variables which may be caused by fluctuating mass flow rates are also discussed.

Lamb, F. K.↗

NASA NTRSDate not supplied

Nanoflare heating in coronal X-ray Bright Points

Understanding the heating of the non-flaring solar corona is an active topic of research in heliophysics. It is well accepted that magnetic fields are mainly responsible for coronal heating. The photospheric driver randomly moves the foot-points of the magnetic field lines, and either generates waves or the quasi-static buildup of magnetic energy, depending on the timescale of motion. Heating by the dissipation of the magnetic energy is termed as DC heating while the dissipation of wave is known as AC heating mechanism. Both the AC and DC heating mechanisms can lead to impulsive heating events, termed nanoflares. The magnitude and frequency of these nanoflares determine whether they can adequately satisfy the coronal heating budget. Thus it is of great importance to study the nanoflares properties to validate their role in coronal heating. Individual nanoflares are difficult to detect with the present generation instruments, however their presence can be inferred through indirect observational techniques combined with simulated nanoflare-heated plasma emissions. We combined the observed emission properties of coronal X-ray bright points (XBP) in EUV and X-ray wavelengths with the simulated nanoflare heated plasma to understand nanoflare properties and their contribution in coronal heating. Our results indicate that the heating of coronal XBPs could be explained by frequent nanoflares. Further we demonstrate that the sensitive spectroscopic observations in X-ray wavelength are crucial to diagnose the nanoflare properties.

Biswajit Mondal↗

NASA NTRS1977

Evolution of the high-temperature plasma in the 15 June 1973 flare

The evolution of the high-temperature plasma in the solar flare of June 15, 1973, is analyzed using XUV spectroheliograms obtained from Skylab. Preflare enhancement of loops in the active region is discussed, and the temperature and density of the hot plasma are determined as a function of time. The turbulent velocity in the high-temperature plasma is deduced from a comparison of line widths of the Fe XXIV doublet at 192 and 255 A. The results indicate that: (1) the flare occurred in a low-lying loop or loops; (2) the large-scale magnetic-field configuration of the active region did not change significantly; (3) the hot plasma observed in Fe XXIII-XXIV showed considerable dynamic activity, including an initial upward motion of the whole plasma with a velocity of about 80 km/s; (4) large turbulent mass motions took place in the Fe XXIII-XXIV plasma with random velocities of 100 to 160 km/s; (5) the temperature of the hot plasma was 14 million K at peak X-ray intensity, decreased slightly, and then remained at 12.6 million K before dropping to below 10 million K; and (6) there was a temperature gradient along the length of the loop, with the 14 million-K plasma near the top and progressively lower-temperature plasmas on either side.

Cheng, C.-C.↗

NASA NTRS2012

Staring at 4U 1909+07 with Suzaku (Research Note)

We present an analysis of the neutron star High Mass X-ray Binary (HMXB) 4U 1909+07 mainly based on Suzaku data. We extend the pulse period evolution, which behaves in a random-walk like manner, indicative of direct wind accretion. Studying the spectral properties of 4U 1909+07 between 0.5 to 90keV we find that a power-law with an exponential cutoff can describe the data well, when additionally allowing for a blackbody or a partially covering absorber at low energies. We find no evidence for a cyclotron resonant scattering feature (CRSF), a feature seen in many other neutron star HMXBs sources. By performing pulse phase resolved spectroscopy we investigate the origin of the strong energy dependence of the pulse profile, which evolves from a broad two-peak profile at low energies to a profile with a single, narrow peak at energies above 20keV. Our data show that it is very likely that a higher folding energy in the high energy peak is responsible for this behavior. This in turn leads to the assumption that we observe the two magnetic poles and their respective accretion columns at different phases, and that these accretions column have slightly different physical conditions.

Fuerst, F.↗

NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit(CPU)on the order of ~30 s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗

NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit (CPU) on the order of ~30s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗

NASA NTRS2008

Assessment of Proper Bonding Methods and Mechanical Characterization FPGA CQFPs

This presentation discusses fractured leads on field-programmable gate array (FPGA) during flight vibration. Actions taken to determine root cause and resolution of the failure include finite element analysis (FEA) and vibration testing and scanning electron microscopy (with X-ray microanalysis) and energy dispersive spectrometry (SEM/EDS) failure assessment. Bonding methods for surface mount parts is assessed, including critical analysis and assessment of random fatigue damage. Regarding ceramic quad flat pack (CQFP) lead fracture, after disassembling the attitude control electronics (ACE) configuration, photographs showed six leads cracked on FPGA RTSX72SU-1 CQ208B package located on the RWIC card. An identical package (FPGA RTSX32SU-1 CQ208B) mounted on the RWIC did not results in cracked pins due to vibration. FPGA lead failure theories include workmanship issues in the lead-forming, material defect in the leads of the FPGA packages, and the insecure mounting of the board in the card guides, among other theories. Studies were conducted using simple calculations to determine the response and fatigue life of the package. Shorter packages exhibited more response when loaded by out-of-plane displacement of PCB while taller packages exhibit more response when loaded by in-plane acceleration of PCB. Additionally, under-fill did not contribute to reducing stress in leads due to out-of-plane PCB loading or from component twisting, as much as corner bonding. The combination of corner bond and under-fill is best to address mechanical and thermal S/C environment. Test results of bonded parts showed reduced (dampened) amplitude and slightly shifted peaks at the un-bonded natural frequency and an additional response at the bonded frequency. Stress due to PCBB out-of-plane loading was decreased on in the corners when only a corner bond was used. Future work may address CQFP fatigue assessment, including the investigation of discrepancy in predicted fatigue damage, as well as comparing fatigue life and fatigue damage cycle ration computed using FEA and Miner's rule to results from a fatigue assessment software program.

Davis, Milton C.↗

NASA NTRS2024

The Radio Fundamental Catalogue. I. Astrometry

We present the all-sky catalogue of absolute positions and estimates of correlated flux density of 21,942 compact radio sources determined from processing interferometric visibility data of virtually all VLBI observing sessions at 2 to 23~GHz from 72 programs suitable for absolute astrometry collected for 30 years. We used a novel technique of generation of a dataset of fused observables that allowed us to incorporate all available data in our analysis. The catalogue is the most complete and most precise to date. It forms the foundation and reference for positional astronomy, space geodesy, space navigation, population analysis of active galactic nuclei (AGNs), and provides calibrators for phase-referencing for differential astrometry and VLBI astrophysical observations. Its accuracy was evaluated through a detailed accounting systematic errors, rigorous decimation tests, comparison of different datasets, and comparison with other catalogues. The catalogue samples preferentially the AGNs with a strong contemporary parsec-scale synchrotron emission. Its milliarcsecond level positional accuracy allows association of these AGNs with detections in a wide range of the electromagnetic spectrum from low-frequency radio to gamma-rays and high energy neutrinos. We describe the innovative data processing and calibration technique in full detail, report the in depth analysis of random and systematic positional errors, and provide the list of associations with large surveys at different wavelengths.

Astronomy↗

NASA NTRS1977

Computer simulation of the velocity diffusion of cosmic rays

Monte Carlo simulation experiments were performed in order to study the velocity diffusion of charged particles in a static turbulent magnetic field. By following orbits of particles moving in a large ensemble of random magnetic field realizations with suitable chosen statistical properties, a pitch-angle diffusion coefficient is derived. Results are presented for a variety of particle rigidities and rms random field strengths and compared with the predictions of standard quasi-linear theory and the nonlinear partially averaged field theory.

Kaiser, T. B.↗

NASA NTRS1991

A measurement of the value of the Hubble constant from the X-ray properties and the Sunyaev-Zel'dovich effect of Abell 665

A comparison of the X-ray properties and the Sunyaev-Zel'dovich (SZ) effect for a cluster of galaxies can be used to measure the distance of the cluster and hence the Hubble constant. This method was applied to the rich cluster Abell 665. The relative normalization of X-ray and SZ-effect data leads to an estimated Hubble constant of H sub 0 = 40 + or - 9 km/s Mpc if only the random errors are included. When the possible systematic errors are added in quadrature, the range of possible values of the Hubble constants expands to (40 to 50) + or - 12 km/s Mpc.

Birkinshaw, M.↗

NASA NTRS1994

Very high energy gamma rays from active galactic nuclei: Cascading on the cosmic background radiation fields and the formation of pair halos

Recent high-energy gamma-ray observations (E(sub gamma) greater than 100 MeV) of blazar Active Galactic Nuclei (AGNs) show emission spectra with no clear upper energy cutoff. AGNs, considered to be possible sources for the observed flux of cosmic rays beyond 10(exp 19) eV, may well have emission extending into the very high energy (VHE), (E(sub gamma) greater than 100 GeV) domain. Because VHE gamma-rays are absorbed by pair production on the intergalactic background radiation fields, much of this emission may not be directly visible. The electromagnetic cascades initiated by absorbed VHE gamma-rays, however, may be observable. Since, most probably, the velocities of (e(+), e(-)) pairs produced in a cascade are quickly isotropized by an ambient random magnetic field, extended 'halos' (R greater than 1 Mpc) of pairs will be formed around AGNs with VHE emission. The cascade radiation from these pair halos is emitted isotropically and should be observable at energies below a few TeV. The halo radiation can be distinguished by its characteristic variation in spectrum and intensity with angular distance from the central source. This variation depends weakly on the details of the central source model, e.g., the orientation and beaming/opening angle of an emitting jet. Limiting or determining the intensity of the pair halo can thus serve as a model-independent bound on or measure of the VHE power of AGNs. Next-generation Cherenkov telescopes may be able to image a pair halo.

Aharonian, F. A.↗

NASA NTRS1978

Computer simulation of the velocity diffusion of cosmic rays

Monte Carlo simulation experiments have been performed in order to study the velocity diffusion of charged particles in a static turbulent magnetic field. By following orbits of particles moving in a large ensemble of random magnetic field realizations with suitably chosen statistical properties, a pitch-angle diffusion coefficient is derived. Results are presented for a variety of particle rigidities and rms random field strengths and compared with the predictions of standard quasi-linear theory and the nonlinear partially averaged field theory.

Kaiser, T. B.↗

NASA NTRS1993

Imaging the sun in hard x rays using Fourier telescopes

For several years, solar flares have been observed with a variety of instruments confirming that tremendous amounts of energy are locally stored in the solar magnetic field and then rapidly released during the life of the flare. In concert with observations, theorists have attempted to describe the means by which these energetic events occur and evolve. Two competing theories have emerged and have stood the test of time. One theory describes the flare in terms of nonthermal, electron beam injection into a thick target while the other uses a thermal approach. Both theories provide results which are reasonably consistent with current observations; but to date, none have been able to provide conclusive evidence as to the validity of either model. Imaging on short time scales (1 s) and/or small size scales (1 arc s) should give definitive answers to these questions. In order to test whether a realistic telescope can indeed discriminate between models, we construct model sources based upon the thermal and the nonthermal models and calculate the emission as a function of time and energy in the range from 10 to 100 keV. In addition, we construct model telescopes representing both the spatial modulation collimator (SMC) and the rotating modulation collimator (RMC) techniques of observation using random photon counting statistics. With these two types of telescopes we numerically simulate the instrument response to the above two model flares to see if there are distinct x-ray signatures which may be discernable. We find that theoretical descriptions of the primary models of solar flares do indeed predict different hard x-ray signatures for 1 sec time scales and at 1-5 arc sec spatial resolution. However, these distinguishing signatures can best be observed early in the impulsive phase and from a position perpendicular to the plane of the loop. Furthermore, we find that Fourier telescopes with reasonable and currently attainable design characteristics can image these signatures and that the same sensitive areas and short temporal integration times relative to source evolution (i.e., 1 s), the RMC and the SMC will both provide about the same performance.

Campbell, J. W.↗

NASA NTRS1974

Long-term observations of Cygnus X-2 from OSO-7

Results of observations of Cygnus X-2 are reported that were made with an X-ray telescope on board the OSO-7 satellite during the three intervals of January 9-16 and 21-26, 1972, and July 8-11, 1972. Random factor-of-two variations were found in the data. No statistically significant periodic variations were found in the range from 10 hours to 2 days.

Ulmer, M. P.↗