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All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Data analysis using scale-space filtering and Bayesian probabilistic reasoning

This paper describes a program for analysis of output curves from Differential Thermal Analyzer (DTA). The program first extracts probabilistic qualitative features from a DTA curve of a soil sample, and then uses Bayesian probabilistic reasoning to infer the mineral in the soil. The qualifier module employs a simple and efficient extension of scale-space filtering suitable for handling DTA data. We have observed that points can vanish from contours in the scale-space image when filtering operations are not highly accurate. To handle the problem of vanishing points, perceptual organizations heuristics are used to group the points into lines. Next, these lines are grouped into contours by using additional heuristics. Probabilities are associated with these contours using domain-specific correlations. A Bayes tree classifier processes probabilistic features to infer the presence of different minerals in the soil. Experiments show that the algorithm that uses domain-specific correlation to infer qualitative features outperforms a domain-independent algorithm that does not.

Kulkarni, Deepak↗
DOE OSTI2024

Effect of detachment on Magnum-PSI ELM-like pulses: direct observations and qualitative results

Abstract Conditions similar to those at the end of the divertor leg in a tokamak were replicated in the linear plasma machine Magnum-PSI. The neutral pressure in the target chamber is then increased to cause the target to transition from an attached to a detached state. Superimposed to this steady state regime, edge localised mode (ELM)-like pulses are reproduced, resulting in a sudden increase in plasma temperature and density, such that the heat flux increases transiently by half an order of magnitude. Visible light emission, target thermography, and Thomson scattering are used to demonstrate that the higher the neutral pressure the more energy is removed from the ELM-like pulse in the volume. If the neutral pressure is sufficiently high, the ELM-like pulse can be prevented from affecting the target and the plasma energy is fully dissipated in the volume instead (ID 4 in table 1). The visible light images allow the division of the ELM-plasma interaction process of ELM energy dissipation into 3 ‘stages’ ranging from no dissipation to full dissipation (the target plasma is detached). In the second publication related to this study, spectroscopic data is analysed with a Bayesian approach, to acquire insights into the significance of molecular processes in dissipating the plasma energy and particles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2004

The Goddard Profiling Algorithm (GPROF): Description and Current Applications

Atmospheric scientists use different methods for interpreting satellite data. In the early days of satellite meteorology, the analysis of cloud pictures from satellites was primarily subjective. As computer technology improved, satellite pictures could be processed digitally, and mathematical algorithms were developed and applied to the digital images in different wavelength bands to extract information about the atmosphere in an objective way. The kind of mathematical algorithm one applies to satellite data may depend on the complexity of the physical processes that lead to the observed image, and how much information is contained in the satellite images both spatially and at different wavelengths. Imagery from satellite-borne passive microwave radiometers has limited horizontal resolution, and the observed microwave radiances are the result of complex physical processes that are not easily modeled. For this reason, a type of algorithm called a Bayesian estimation method is utilized to interpret passive microwave imagery in an objective, yet computationally efficient manner.

Olson, William S.↗
NASA NTRS2009

A Method for Retrieving Ground Flash Fraction from Satellite Lightning Imager Data

A general theory for retrieving the fraction of ground flashes in N lightning observed by a satellite-based lightning imager is provided. An "exponential model" is applied as a physically reasonable constraint to describe the measured optical parameter distributions, and population statistics (i.e., mean, variance) are invoked to add additional constraints to the retrieval process. The retrieval itself is expressed in terms of a Bayesian inference, and the Maximum A Posteriori (MAP) solution is obtained. The approach is tested by performing simulated retrievals, and retrieval error statistics are provided. The ability to retrieve ground flash fraction has important benefits to the atmospheric chemistry community. For example, using the method to partition the existing satellite global lightning climatology into separate ground and cloud flash climatologies will improve estimates of lightning nitrogen oxides (NOx) production; this in turn will improve both regional air quality and global chemistry/climate model predictions.

Koshak, William J.↗
NASA NTRS2004

Support vector machines

Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

multiangle↗
NASA NTRS2004

Putting Priors in Mixture Density Mercer Kernels

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

Srivastava, Ashok N.↗
NASA NTRS2005

An Ensemble Approach to Building Mercer Kernels with Prior Information

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Srivastava, Ashok N.↗
NASA NTRS2012

An Icy Kuiper Belt Around the Young Solar-type Star HD 181327

Context. HD 181327 is a young main sequence F5/F6 V star belonging to the Beta Pictoris moving group (age approx.. 12 Myr). It harbors an optically thin belt of circumstellar material at radius approx.. 90 AU, presumed to result from collisions in a population of unseen planetesimals. Aims. We aim to study the dust properties in the belt in details, and to constrain the gas-to-dust ratio. Methods. We obtained far-infrared photometric observations of HD 181327 with the PACS instrument onboard the Herschel Space Observatory, complemented by new 3.2 mm observations carried with the ATCA array. The geometry of the belt is constrained with newly reduced HST/NICMOS scattered light images that allow the degeneracy between the disk geometry and the dust properties to be broken. We then use the radiative transfer code GRaTeR to compute a large grid of models, and we identify the grain models that best reproduce the spectral energy distribution (SED) through a Bayesian analysis. We attempt to detect the oxygen and ionized carbon fine-structure lines with Herschel/PACS spectroscopy, providing observables to our photochemical code ProDiMo. Results. The HST observations confirm that the dust is confined in a narrow belt. The continuum is detected with Herschel/PACS completing nicely the SED in the far-infrared. The disk is marginally resolved with both PACS and ATCA. A medium integration of the gas spectral lines only provides upper limits on the [OI] and [CII] line fluxes.We show that the HD 181327 dust disk consists of micron-sized grains of porous amorphous silicates and carbonaceous material surrounded by an important layer of ice, for a total dust mass of approx.. 0.05 Solar Mass (in grains up to 1 mm). We discuss evidences that the grains consists of fluffy aggregates. The upper limits on the gas atomic lines do not provide unambiguous constraints: only if the PAH abundance is high, the gas mass must be lower than approx. 17 Solar Mass. Conclusions. Despite the weak constraints on the gas disk, the age of HD 181327 and the properties of the dust disk suggest that it has passed the stage of gaseous planets formation. The dust reveals a population of icy planetesimals, similar to the primitive Edgeworth-Kuiper belt, that may be a source for the future delivery of water and volatiles onto forming terrestrial planets.

HD 181327↗
NASA NTRS2022

Updated Orbital Monitoring and Dynamical Masses for Nearby M-dwarf Binaries

Young M-type binaries are particularly useful for precise isochronal dating by taking advantage of their extended pre-main sequence evolution. Orbital monitoring of these low-mass objects becomes essential in constraining their fundamental properties, as dynamical masses can be extracted from their Keplerian motion. Here, we present the combined efforts of the AstraLux Large Multiplicity Survey, together with a filler sub-programme from the SpHere INfrared Exoplanet (SHINE) project and previously unpublished data from the FastCam lucky imaging camera at the Nordical Optical Telescope (NOT) and the NaCo instrument at the Very Large Telescope (VLT). Building on previous work, we use archival and new astrometric data to constrain orbital parameters for 20 M-type binaries. We identify that eight of the binaries have strong Bayesian probabilities and belong to known young moving groups (YMGs). We provide a first attempt at constraining orbital parameters for 14 of the binaries in our sample, with the remaining six having previously fitted orbits for which we provide additional astrometric data and updated Gaia parallaxes. The substantial orbital information built up here for four of the binaries allows for direct comparison between individual dynamical masses and theoretical masses from stellar evolutionary model isochrones, with an additional three binary systems with tentative individual dynamical mass estimates likely to be improved in the near future. We attained an overall agreement between the dynamical masses and the theoretical masses from the isochrones based on the assumed YMG age of the respective binary pair. The two systems with the best orbital constrains for which we obtained individual dynamical masses, J0728 and J2317, display higher dynamical masses than predicted by evolutionary models.

Astrometry / binaries↗
DOE OSTI2026

Data‐Driven Engineering of Thermostable Collagen‐Mimetic Peptoid Triple Helices

Collagen-mimetic peptides (CMPs) are engineered molecules designed to replicate the triple-helical structure of natural collagen. A repeating x–y-Gly sequence is the defining motif of CMPs and is critical to their triple-helical structure and stability. Substitutions to the residues occupying the x and y positions present a means to modulate the CMP structure and properties. Peptoid residues—N-substituted glycine derivatives—present an attractive potential substitution due to their thermal stability, proteolytic resistance, biocompatibility, and diverse palette of non-natural side chains, but also tend to introduce a high degree of backbone flexibility that can diminish the stability of the triple helix. In this work, we report a computational active learning cycle comprising molecular dynamics simulation, Gaussian process regression, and Bayesian optimization to computationally identify a number of promising peptoid substitutions predicted to stabilize the desired quaternary structure through side chain interactions and produce stable peptoid-based collagen-like triple helices. To experimentally test the computational predictions, a top candidate identified by the screen was synthesized and imaged using scanning electron microscopy to resolve fibril-like bundles consistent with collagen-like triple helices. This work predicts a number of CMP peptoid substitutions capable of forming stable triple-helical structures, presents a generalizable design strategy for engineering desired peptoid structures, and opens new avenues for the design of peptoid-based biomimetic materials.

active learning↗
NASA NTRS2000

Limits on Arcminute Scale Cosmic Microwave Background Anisotropy with the BIMA Array

We have used the Berkeley-Illinois-Maryland-Association (BIMA) millimeter array outfitted with sensitive cm-wave receivers to search for Cosmic Microwave Background (CMB) anisotropies on arcminute scales. The interferometer was placed in a compact configuration which produces high brightness sensitivity, while providing discrimination against point sources. Operating at a frequency of 28.5 GHz, the FWHM primary beam of the instrument is 6.6 arcminutes. We have made sensitive images of seven fields, five of which where chosen specifically to have low IR dust contrast and be free of bright radio sources. Additional observations with the Owens Valley Radio Observatory (OVRO) millimeter array were used to assist in the location and removal of radio point sources. Applying a Bayesian analysis to the raw visibility data, we place limits on CMB anisotropy flat-band power Q_flat = 5.6 (+3.0, -5.6) uK and Q_flat < 14.1 uK at 68% and 95% confidence. The sensitivity of this experiment to flat band power peaks at a multipole of l = 5470, which corresponds to an angular scale of approximately 2 arcminutes The most likely value of Q_flat is similar to the level of the expected secondary anisotropies.

Holzapfel, W. L.↗
NASA NTRS2000

Limits on Arcminute-Scale Cosmic Microwave Background Anisotropy at 28.5 GHz

We have used the Berkeley-Illinois-Maryland Association (BIMA) millimeter array outfitted with sensitive centimeter-wave receivers to search for cosmic microwave background (CMB) anisotropies on arcminute scales. The interferometer was placed in a compact configuration that produces high brightness sensitivity, while providing discrimination against point sources. Operating at a frequency of 28.5 GHz, the FWHM primary beam of the instrument is approximately 6'.6. We have made sensitive images of seven fields, four of which where chosen specifically to have low infrared dust contrast and to be free of bright radio sources. Additional observations with the Owens Valley Radio Observatory (OVRO) millimeter array were used to assist in the location and removal of radio point sources. Applying a Bayesian analysis to the raw visibility data, we place limits on CMB anisotropy flat-band power of Q(sub flat) = 5.6(sub -5.6)(exp 3.0) microK and Q(sub flat) < 14.1 microK at 68% and 95% confidence, respectively. The sensitivity of this experiment to flat-band power peaks at a multipole of I = 5470, which corresponds to an angular scale of approximately 2'. The most likely value of Q(sub flat) is similar to the level of the expected secondary anisotropies.

Holzapfel, W. L.↗
DOE OSTI2023

Image-based solar estimates

An example device is configured to determine, based on a sky image of a portion of sky over a power distribution network and using a convolutional neural network (CNN)-based image regression model, an estimated global horizontal irradiance (GHI) value and manage or control the power distribution network using the estimated GHI value. The device may also be configured to determine, based on GHI values and aggregate load values for at least a portion of the power distribution network, using a Bayesian Structural Time Series model, an estimated photovoltaic power output value for the at least a portion of the power distribution network. The device may manage or control the power distribution network using the estimated photovoltaic power output value.

Bernstein, Andrey↗
DOE OSTI2022

The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation

An accurate estimation of spatially and temporally continuous global terrestrial evapotranspiration (ET) is essential in the assessment of surface energy, water and carbon cycles. The Global LAnd Surface Satellite (GLASS) ET product Version 4.0 (v4.0) based on the Bayesian model averaging (BMA) method was generated to estimate global terrestrial ET. However, certain uncertainty for the GLASS ET product v4.0 limits its application. In this study, we introduced the deep neural networks (DNN) merging framework to improve terrestrial ET estimation for GLASS ET product Version 5.0 (v5.0) generation by integrating five satellite-derived ET products [Moderate Resolution Imaging Spectroradiometer (MODIS) ET product (MOD16), Shuttleworth–Wallace dual-source ET product (SW), Priestley–Taylor-based ET product (PT-JPL), modified satellite-based Priestley–Taylor ET product (MS-PT) and simple hybrid ET product (SIM)]. We compared the performance of DNN method against other merging methods, including GLASS ET algorithm v4.0 (BMA), the gradient boosting regression tree (GBRT) method and the random forest (RF) method, based on 195 global eddy covariance (EC) flux towers covering observations from 2000 through 2015. Validations indicated that the DNN had the highest accuracy among four merging methods across different land cover types, yielding the highest average determination coefficients (R 2 , 0.62), root-mean-squared-error (RMSE, 24.1 W/m 2 ) and Kling–Gupta efficiency (KGE, 0.77) with a of 99% confidence interval. Compared with GLASS ET algorithm v4.0, the DNN improved on the R 2 by approximately 7% (p < 0.01) and the KGE by 10%. Based on the DNN, we then generated 8-day GLASS ET product v5.0 globally with a 1 km spatial resolution from 2001 to 2015 driven by GLASS vegetation and surface net radiation (R n ) datasets and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) datasets. Finally, this global terrestrial ET product provides a valuable dataset for monitoring regional and global water resources and environmental changes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

Bayesian Estimation of Precipitation from Satellite Passive Microwave Observations Using Combined Radar-Radiometer Retrievals

Precipitation estimation from satellite passive microwave radiometer observations is a problem that does not have a unique solution that is insensitive to errors in the input data. Traditionally, to make this problem well posed, a priori information derived from physical models or independent, high-quality observations is incorporated into the solution. In the present study, a database of precipitation profiles and associated brightness temperatures is constructed to serve as a priori information in a passive microwave radiometer algorithm. The precipitation profiles are derived from a Tropical Rainfall Measuring Mission (TRMM) combined radar radiometer algorithm, and the brightness temperatures are TRMM Microwave Imager (TMI) observed. Because the observed brightness temperatures are consistent with those derived from a radiative transfer model embedded in the combined algorithm, the precipitation brightness temperature database is considered to be physically consistent. The database examined here is derived from the analysis of a month-long record of TRMM data that yields more than a million profiles of precipitation and associated brightness temperatures. These profiles are clustered into a tractable number of classes based on the local sea surface temperature, a radiometer-based estimate of the echo-top height (the height beyond which the reflectivity drops below 17 dBZ), and brightness temperature principal components. For each class, the mean precipitation profile, brightness temperature principal components, and probability of occurrence are determined. The precipitation brightness temperature database supports a radiometer-only algorithm that incorporates a Bayesian estimation methodology. In the Bayesian framework, precipitation estimates are weighted averages of the mean precipitation values corresponding to the classes in the database, with the weights being determined according to the similarity between the observed brightness temperature principal components and the brightness temperature principal components of the classes. Because the classes are stratified by the sea surface temperature and the echo-top-height estimator, the number of classes that are considered for retrieval is significantly smaller than the total number of classes, making the algorithm computationally efficient. The radiometer-only algorithm is applied to TMI observations, and precipitation estimates are compared with combined TRMM precipitation radar (PR) TMI reference estimates. The TMI-only algorithm, supported by the empirically derived database, produces estimates that are more consistent with the reference values than the precipitation estimates from the version-6 TRMM facility TMI algorithm. Cloud-resolving model simulations are used to assign a latent heating profile to each precipitation profile in the empirically derived database, making it possible to estimate latent heating using the radiometer-only algorithm. Although the evaluation of latent heating estimates in this study is preliminary, because realistic conditional probability distribution functions are attached to latent heating structures in the algorithm s database, a generally positive impact on latent heating estimation from passive microwave observations is expected.

Grecu, Mircea↗
NASA NTRS2016

Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data

Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.

Babcock, Chad↗
NASA NTRS2024

XLSSC 122 Caught in the Act of Growing Up: Spatially Resolved SZ Observations of a z=1.98 Galaxy Cluster

Context. How protoclusters evolved from sparse galaxy overdensities to mature galaxy clusters is still not well understood. In this context, detecting and characterizing the hot intracluster medium (ICM) at high redshifts (z ∼ 2) is key to understanding how the continuous accretion from the filamentary large-scale structure and the mergers along it impact the first phases of cluster formation. Aims. We study the dynamical state and morphology of the z = 1:98 galaxy cluster XLSSC 122 with high-resolution observations (≈5″) of the ICM through the Sunyaev–Zeldovich (SZ) effect. XLSSC 122 is the highest redshift optically confirmed galaxy cluster found in an unbiased, widefield survey. Methods. Via Bayesian forward modeling, we mapped the ICM on scales from the virial radius down to the core of the cluster. To constrain such a broad range of spatial scales, we employed a new technique that jointly forward-models parametric descriptions of the pressure distribution to interferometric ACA and ALMA observations and multiband imaging data from ACT. Results. We detect the SZ effect with 11σ significance in the ALMA+ACA observations and find a flattened inner pressure profile that is consistent with a noncool core classification with a significance of ≥3σ. In contrast to the previous works, we find better agreement between the SZ effect signal and the X-ray emission as well as the cluster member distribution. Further, XLSSC 122 exhibits an excess of SZ flux in the south of the cluster where no X-ray emission is detected. By reconstructing the interferometric observations and modeling in the uv-plane, we obtain a tentative detection of an infalling group or filamentary-like structure in the southeast that is believed to boost and heat up the ICM while the density of the gas is still low. In addition to characterizing the dynamical state of the cluster, we provide an improved SZ mass estimate M500,c = 1.66−0.20+0.23 × 1014 M⊙ . Conclusions. Altogether, the observations indicate that we see XLSSC 122 in a dynamic phase of cluster formation while a large reservoir of gas is already thermalized.

large-scale structure of Universe↗
NASA NTRS2021

KMT-2019-BLG-1715: Planetary Microlensing Event with Three Lens Masses and Two Source Stars

We investigate the gravitational microlensing event KMT-2019-BLG-1715, the light curve of which shows two short-term anomalies from a caustic-crossing binary-lensing light curve: one with a large deviation and the other with a small deviation. We identify five pairs of solutions, in which the anomalies are explained by adding an extra lens or source component in addition to the base binary-lens model. We resolve the degeneracies by applying a method in which the measured flux ratio between the first and second source stars is compared with the flux ratio deduced from the ratio of the source radii. Applying this method leaves a single pair of viable solutions, in both of which the major anomaly is generated by a planetary-mass third body of the lens, and the minor anomaly is generated by a faint second source. A Bayesian analysis indicates that the lens comprises three masses: a planet-mass object with ∼2.6 MJ and binary stars of K and M dwarfs lying in the galactic disk. We point out the possibility that the lens is the blend, and this can be verified by conducting high-resolution follow-up imaging for the resolution of the lens from the source.

Cheongho Han↗