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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

NuSTAR, SWIFT, and GROND Observations of the Flairing MeV Blazar PMN J0641-0320

MeV blazars are a sub-population of the blazar family, exhibiting larger-than-average jet powers, accretion luminosities, and black hole masses. Because of their extremely hard X-ray continua, these objects are best studied in the X-ray domain. Here, we report on the discovery by the Fermi Large Area Telescope and subsequent follow-up observations with NuSTAR, Swift, and GROND of a new member of the MeV blazar family: PMN J0641-0320. Our optical spectroscopy provides confirmation that this is a flat-spectrum radio quasar located at a redshift of z = 1.196. Its very hard NuSTAR spectrum (power-law photon index of approx.1 up to approx. 80 keV) indicates that the emission is produced via inverse Compton scattering off of photons coming from outside the jet. The overall spectral energy distribution of PMN J0641-0320 is typical of powerful blazars and, using a simple one-zone leptonic emission model, we infer that the emission region is located either inside the broad line region or within the dusty torus.

galaxies: active quasars: general↗
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRS2003

Intelligent machines in the twenty-first century: foundations of inference and inquiry

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have learned and what they are designed to understand.

Review↗
NASA NTRS2019

ALMA Spectral Imaging of Titan Contemporaneous with Cassini's Grand Finale

The Cassini mission performed 127 targeted ybys of Titan during its 13 yr mission to Saturn, culminating in the Grand Finale between 2017 April and September. Here we demonstrate the use of the Atacama Large Millimeter/ submillimeter Array (ALMA) to continue Cassini's legacy for chemical and climatological studies of Titan's atmosphere. Whole-hemisphere, interferometric spectral maps of HCN, HNC, HC3N, CH3CN, C2H3CN, C2H5CN,and C3H8 were obtained using ALMA in 2017 May at moderate (0 2, or 1300 km) spatial resolution, revealing the effects of seasonally variable chemistry and dynamics on the distribution of each species. The ALMA submillimeter observations of HCN and HC3N are consistent with Cassini infrared data on these species, obtained in the same month. Chemical/dynamical lifetimes of a few years are inferred for C2H3CN and C2H5CN, in reasonably close agreement with the latest chemical models incorporating the sticking of C2H5CN to stratospheric aerosol particles. ALMA radial limb ux proles provide column density information as a function of altitude, revealing maximum abundances in the thermosphere (above 600 km) for HCN, HNC, HC3N, and C2H5CN. This constitutes the rst detailed measurement of the spatial distribution of HNC, which is found to be conned predominantly to altitudes above 730 ± 60 km. The HNC emission map shows an east-west hemisphericasymmetry of 13% ± 3%. These results are consistent with very rapid production (and loss) of HNC in Titan's uppermost atmosphere, making this molecule an effective probe of short-timescale (diurnal) ionospheric processes.

Cordiner, M. A.↗
NASA NTRS2011

Micro-Scale Distributions of Major and Trace Elements in Chondrites

The Hayabusa spacecraft has successfully returned to Earth after two touchdowns on the surface of Asteroid 25143 Itokawa. This asteroid is classified as an S-type and inferred to consist of materials similar to ordinary chondrites or primitive achondrites [1]. More than 1500 particles have been identified consisting of olivine, pyroxene, plagioclase, Fe sulfide and Fe metal, with compositions consistent with being of LL origin. While the chondritic components are familiar to us, the level of detail to which the Itokawa samples will be exposed to will be unprecedented given that the samples are reasonably large and accessible to a wide variety of techniques. In many ways, we expect that our knowledge base of the comparator chondrites will be found to be wanting. Chondrites are the building blocks of the solar system. However, these rocks are essentially breccias and they are quite variable in bulk element compositions as well as compositions of the individual components. We have initiated a program of analysis for chondrites focusing on major and trace element distributions between the mineral components and the matrix. The issues to be addressed include the homogeneity of matrix and chondrule components and the representivity of any given sample to the bulk meteorite. This may be particularly important given the limited numbers of Itokawa grains that may be available for a specific analysis. As an initial study, we have taken thin sections of carbonaceous chondrites to study the representivity of the matrix compositions. Spot locations were constrained to limited regions of the sections so as to assess the variability of a local scale. Further work will be required to assess variability over a centimeter scale.

Ireland, T. R.↗
NASA NTRS1975

Interstellar clouds containing optically thin H2

The theory of Black and Delgarno that the relative populations of the excited rotational levels of H2 can be understood in terms of cascading following absorption in the Lyman and Werner bands is employed to infer the gas densities and radiation fields within diffuse interstellar clouds containing H2 that is optically thin in those bands. The procedure is described for computing the populations of the different rotation levels, the relative distribution among the different rotation levels of newly formed H2 is determined on the basis of five simplified models, and the rate of H2 formation is estimated. The results are applied to delta Ori, two components of iota Ori, the second components of rho Leo and zeta Ori, tau Sco, gamma Vel, and zeta Pup. The inferred parameters are summarized for each cloud.

Jura, M.↗
DOE OSTI2026

Artificial Intelligence-Driven Management of Sustainable Energy Resources: Visibility, Operation, and Control

The rapid global transition toward sustainable energy resources (SERs) is reshaping how modern power systems are observed, optimized, and controlled. While SERs have significantly advanced decarbonization, their weather dependence, variability, and inverter-dominated characteristics challenge traditional, centralized, and deterministic grid operation. At the same time, the proliferation of high-resolution data from inverters, smart meters, and sensors offers unprecedented visibility into system dynamics. Yet, it also exceeds the analytical capability of conventional model-based approaches. Artificial intelligence (AI) provides a new foundation for addressing these challenges by bridging physical laws with data-driven learning, enabling accurate state awareness, adaptive operation, and coordinated control across distributed assets. This article examines how AI transforms the management of SER-rich power systems along three critical dimensions: 1) enhancing visibility by inferring behind-the-meter (BTM) activities, assessing SER flexibility, and reconstructing system states from sparse or noisy measurements; 2) improving operation through AI-enhanced SER service provision, volt/var control (VVC), and dynamic operating envelopes (DOE) for efficiency and security; and 3) advancing control by embedding learning-based intelligence into inverter coordination, voltage and frequency regulation, and long-term dispatch. Together, these developments reveal how AI can convert the variability of SERs from an operational challenge into a source of flexibility, resilience, and intelligence, paving the way toward sustainable, adaptive, and self-optimizing power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Slip band characteristics in the presence of grain boundaries in nickel-based superalloy

Shear strain profiles along slip bands in a modified Rolls-Royce nickel superalloy (RR1000) were analyzed for a tensile sample deformed by 2%. The strain increased with distance away from a grain boundary (GB), with maximum shear strain towards the center of the grain, indicating that dislocation nucleation generally occurred in the grain interior. The strain gradients in the neighborhood of the GBs were quantified and generally correlated with rotation about the active slip system line direction. This leads to an ability to determine the active slip system in these regions. The dislocation spacing and pileup stresses were inferred. The dislocation spacing closely follows an Eshelby analytical solution for a single ended pileup of dislocations under an applied stress. The distribution of pileup stress values for GBs of a given misorientation angle follows a log-normal distribution, with no correlation between the pileup stress and the GB misorientation angle. Furthermore, there is no observed correlation between various transmissivity factors and slip band pileup stress. Hence it appears that the obstacle strength of any of the observed GBs is adequate to facilitate the dislocation pileups present in the slip bands. However, slip band transmission does correlate with transmissivity factors, with the current study focusing on the Luster and Morris m’-factor. Observation of strain profiles of transmitted bands indicate dislocation nucleation locations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

popclass

popclass is a lightweight python package that allows fast, probabilistic classification of the lens of a microlensing event given the event's posterior distribution and a model of the Galaxy. popclass provides the bridge between Galactic simulation and lens classification, an interface to common Bayesian inference libraries, and the ability for users to flexibly specify their own Galactic model and classification parameters.

Mcgill, Peter↗
DOE OSTI2022

The Hubble Constant from Strongly Lensed Supernovae with Standardizable Magnifications

The dominant uncertainty in the current measurement of the Hubble constant (H 0 ) with strong gravitational lensing time delays is attributed to uncertainties in the mass profiles of the main deflector galaxies. Strongly lensed supernovae (glSNe) can provide, in addition to measurable time delays, lensing magnification constraints when knowledge about the unlensed apparent brightness of the explosion is imposed. We present a hierarchical Bayesian framework to combine a data set of SNe that are not strongly lensed and a data set of strongly lensed SNe with measured time delays. We jointly constrain (i) H 0 using the time delays as an absolute distance indicator, (ii) the lens model profiles using the magnification ratio of lensed and unlensed fluxes on the population level, and (iii) the unlensed apparent magnitude distribution of the SN population and the redshift–luminosity relation of the relative expansion history of the universe. We apply our joint inference framework on a future expected data set of glSNe and forecast that a sample of 144 glSNe of Type Ia with well-measured time series and imaging data will measure H 0 to 1.5%. We discuss strategies to mitigate systematics associated with using absolute flux measurements of glSNe to constrain the mass density profiles. Using the magnification of SN images is a promising and complementary alternative to using stellar kinematics. Future surveys, such as the Rubin and Roman observatories, will be able to discover the necessary number of glSNe, and with additional follow-up observations, this methodology will provide precise constraints on mass profiles and H 0 .

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1983

FUNSTAT and statistical image representations

General ideas of functional statistical inference analysis of one sample and two samples, univariate and bivariate are outlined. ONESAM program is applied to analyze the univariate probability distributions of multi-spectral image data.

Parzen, E.↗
NASA NTRS1990

Dynamics and configurations of galaxy triplets

The purpose is to infer the probable dynamical states of galaxy triplets by the observed data on their configurations. Two methods are proposed for describing the distributions of the triplet configuration parameters characterizing a tendency to alignment and hierarchy: (1) obtaining a representative sample of configurations and determining its statistical parameters (moments and percentages); and (2) dividing the region of possible configurations of triple systems (Agekian and Anosova, 1967) into a set of segments and finding the probabilities for the configurations to find themselves in each of them. Both these methods allow representation of the data by numerical simulations as well as observations. The effect of projection was studied. It rather overestimates the alignment and hierarchy of the triple systems. Among the parameters of interest there are found some parameters that are least sensitive to projection effects. The samples consist of simulated galaxy triplets (with hidden mass) as well as of 46 probably physical triple galaxies (Karachentseva et al., 1979). The observed triples as well as numerical models show a tendency to alignment. The triple galaxies do not show any tendency to hierarchy (formation of the temporary binaries), but this tendency may be present for simulated triplets without significant dark matter. The significant hidden mass (of order ten times the total mass of a triplet) decreases the probability of forming a binary and so weakens the hierarchy. Small galaxy groups consisting of 3 to 7 members are probably the most prevalent types of galaxy aggregate (Gorbatsky, 1987). Galaxy triplets are the simplest groups, but dynamically nontrivial ones.

Anosova, Joanna P.↗
NASA NTRS1994

The multifrequency spectral evolution of blazar 3C 345

The blazar 3C 345 underwent a 2.5 mag optical outburst between 1990 November and 1991 May. We have obtained 10 nearly simultaneous multifrequency spectra during the course of the outburst in order to study the multifrequency spectral variations of 3C 345 as a function of time. Although our observations were not sampled frequently enough to completely resolve the variations in every frequency band, the general rise and decline of the outburst were seen in the UV through radio with differing rise times. Simulations of an electron distribution injected into a tangled magnetic field show a relationship between frequency and characteristic timescale that was also observed in the radio variations of 3C 345. The two X-ray observations made during the monitoring period showed no evidence of variability. The multifrequency spectrum was modeled with two major components: a relativistic jet and a relativistic thermal accretion disk. Models calculated for each spectrum indicate that the outburst can be explained in terms of these models by varying the high-energy cutoff of the injected electron distribution in the jet model, while also varying the mass accretion rate in the disk model. There is marginal evidence that the inferred accretion rate varies with the jet luminosity.

Webb, J. R.↗
NASA NTRS1998

Evidence for Solar-Cycle Forcing and Secular Variation in the Armagh Observatory Temperature Record

A prominent feature of previous long-term temperature studies has been the appearance of warming since the 1880s, this often being taken as evidence for anthropogenic-induced global warming. In this investigation, the long-term, annual, mean temperature record (1844-1992) of the Armagh Observatory (Armagh, North Ireland), a set of temperature data based on maximum and minimum thermometers that predates the 1880s and correlates well with northern hemispheric and global standards, is examined for evidence of systematic variation, in particular, as related to solar-cycle forcing and secular variation. Indeed, both appear to be embedded within the Armagh data. Removal of these effects, each contributing about 8% to the overall reduction in variance, yields residuals that are randomly distributed. Application of the 10-year moving average to the residuals, furthermore, strongly suggests that the behavior of the residuals is episodic, inferring that (for extended periods of time) temperatures at Armagh sometimes were warmer or cooler (than expected), while at other times they were stable. Comparison of cyclic averages of annual mean temperatures against the lengths of the associated Hale cycles (i.e., the length of two, sequentially numbered, even-odd sunspot cycle pairs) strongly suggests that the temperatures correlate inversely (r = -0.886 at less than 2% level of significance) against the length of the associated Hale cycle. Because sunspot cycle 22 ended in 1996, the present Hale cycle probably will be shorter than average, implying that temperatures at Armagh over this Hale cycle will be warmer (about 9.31 q 0.23 C at the 90% confidence level) than average (= 9.00 C).

Wilson, Robert M.↗
NASA NTRS1998

Perspectives on the Future Search for Life on Mars and Beyond

One can view the search for life on Mars in two ways: first, as the initial step in the search for life elsewhere, and second, as the one place where in situ methods for life detection can be tested and proved via sample return. After Mars, most of the life detection will he done via in situ studies with data return. Mars offers us the opportunity to fine tune our methods - perhaps for a long time to come. Our group is involved in the development of methods for life detection that are independent of specific signals used for detection of life on Earth. These approaches include general indicators of metabolic activity and organismal structure and composition. Using such approaches, we hope to detect the signals of life (biosignatures) that are independent of preconceived notions and yet are convincing and unambiguous. The approaches we are focusing on include stable isotopic analyses of metals, mineral formation and disolution, and elemental analysis. These methods allow us to examine samples at a variety of scales, looking for nonequilibrium distribution of elements that serve as biosignatures. For futures studies of Mars and beyond, they, or some variation of them, should allow inference or proof of life in non-Earth locations.

Nealson, K. H.↗
NASA NTRS2002

Passive and Active Microwave Remote Sensing of Precipitation and Latent Heating Distributions in the Tropics from TRMM

Passive and active microwave remote sensing data are analyzed to identify signatures of precipitation and vertical motion in tropical convection. A database of cloud/radiative model simulations is used to quantify surface rain rates and latent heating profiles that are consistent with these signatures. At satellite footprint-scale (approximately 10 km), rain rate and latent heating estimates are subject to significant random errors, but by averaging the estimates in space and time, random errors are substantially reduced, Bias errors have been minimized by improving the microphysics in the supporting cloud/radiative model simulations, and by imposing a consistent definition of remotely-sensed and model-simulated convective/stratiform rain coverage. Remotely-sensed precipitation and latent heating distributions in the tropics are derived from Tropical Rainfall Measuring Mission (TRMM) and Special Sensor Microwave/ Imager (SSM/ I) sensor data. The prototype Version 6 TRMM passive microwave algorithm typically yields average heating profiles with maxima between 6 and 7 km altitude for organized mesoscale convective systems. Retrieved heating profiles for individual convective systems are compared to coincident estimates based upon a combination of dual-Doppler radar and rawinsonde data. Also, large-scale latent heating distributions are compared to estimates derived from a simpler technique that utilizes observations of surface rain rate and stratiform rain proportion to infer vertical heating structure. Results of these tests will be presented at the conference.

Olson, William S.↗
NASA NTRS2014

High-Energy Pulsar Models: Developments and New Questions

The past few years have seen a major advance in observational knowledge of high-energy (HE) pulsars. The Fermi Large Area Telescope (LAT) and AGILE have increased the number of known gamma-ray pulsars by an order of magnitude, its members being divided roughly equally among millisecond pulsars (MSPs), young radio-loud pulsars, and young radio-quiet pulsars. Many new and diverse emission characteristics are being measured, while radio and X-ray follow-up observations increase the pulsar detection rate and enrich our multiwavelength picture of these extreme sources. The wealth of new data has provided impetus for further development and improvement of existing theoretical pulsar models. Geometric light curve (LC) modelling has uncovered three broad classes into which HE pulsars fall: those where the radio profile leads, is aligned with, or lags the gamma-ray profile. For example, the original MSP and original black widow system are members of the second class, requiring co-located emission regions and thereby breaking with traditional notions of radio emission origin. These models imply narrow accelerator gaps in the outer magnetosphere, indicating copious pair production even in MSP magnetospheres that were previously thought to be pair-starved. The increased quality and variety of the LCs necessitate construction of ever more sophisticated models. We will review progress in global magnetosphere solutions which specify a finite conductivity on field lines above the stellar surface, filling the gap between the standard vacuum and force-free (FF; plasma-filled) models. The possibility of deriving phase-resolved spectra for the brightest pulsars, coupled with the fact that the HE pulsar population is sizable enough to allow sampling of various pulsar geometries, will enable much more stringent testing of future radiation models. Reproduction of the observed phase-resolved behavior of this disparate group will be one of the next frontiers in pulsar science, impacting on our understanding of particle acceleration, emission, and magnetosphere geometry. One may now also study evolutionary trends of the measured or inferred quantities, and probe pulsar visibility and population properties such as radiation beam sizes of different pulsar classes, as well as the distribution of spin-down power, gamma-ray luminosity, conversion efficiency, spectral index, and cutoff energy across the population. Lastly, the recent detection of very-high-energy (VHE) pulsations from the Crab pulsar generated quite a few ideas to explain this emission, leading to an extension of standard models and possibly even a bridge between the physics of pulsars and pulsar wind nebulae (PWNe).

gamma rays:↗