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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Radiological Anomaly Detection And Identification (RADAI) v1.0

The Radiological Anomaly Detection and Identification (RADAI) software package is a python library for implementing, training, and storing algorithms that detect and identify anomalies in gamma-ray spectra. The library defines a general framework for implementing detection (binary) and identification (classification) algorithms, objects to encapsulate the results of analyses, a variety of temporal filtering tools that can be used in constructing algorithms, and conceptual design that allows easy reading and writing of algorithms (and their time dependent state). In addition to this framework, the library includes implementations of a variety of algorithms from the scientific literature including: gross-counts k-sigma, SPRT, N-SCRAD, Region of Interest, and Censored Energy Window. The implementation of these algorithms within the RADAI package was done to facilitate user-initiated training and configuration to by applied to different gamma-ray detector types. Finally, benchmarked and synthetic datasets will be made available for standardized algorithm characterization with corresponding utilities in the RADAI package for data access and processing.

Joshi, Tenzing↗
DOE OSTI2019

Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation.

42 ENGINEERING↗
NASA NTRS1988

Nature and origin of mineral coatings on volcanic rocks of the Black Mountain, Stonewall Mountain, and Kane Springs Wash volcanic centers, Southern Nevada

Comparative lab spectra and Thematic Mapper imagery investigations at 3 Tertiary calderas in southern Nevada indicate that desert varnish is absorbant relative to underlying host rocks below about 0.7 to 1.3 microns, depending on mafic affinity of the sample, but less absorbant than mafic host rocks at higher wavelengths. Desert varnish occurs chiefly as thin impregnating films. Distribution of significant varnish accumulations is sparse and localized, occurring chiefly in surface recesses. These relationships result in the longer wavelength bands and high 5/2 values over felsic units with extensive desert varnish coatings. These lithologic, petrochemical, and desert varnish controlled spectral responses lead to characteristic TM band relationships which tend to correlate with conventionally mappable geologic formations. The concept of a Rock-Varnish Index (RVI) is introduced to help distinguish rocks with a potentially detectable varnish. Felsic rocks have a high RVI, and those with extensive desert varnish behave differently, spectrally, from those without extensive varnish. The spectrally distinctive volcanic formations at Stonewall Mountain provide excellent statistical class segregation on supervised classification images. A binary decision rule flow-diagram is presented to aid TM imagery analysis over volcanic terrane in semi-arid environments.

Taranik, James V.↗
NASA NTRSDate not supplied

Flood Detection with Synthetic Aperture Radar: A Case Study of Houston, Texas Following Hurricane Harvey (2017) using C- and X-Band Observations

Hurricane Harvey produced record-breaking rainfall of up to 60 inches resulting in extensive flooding in Houston, Texas, in late August and early September of 2017. The slow forward motion of the storm following landfall left much of the area unobservable to optical remote sensing instruments for several days due to cloud cover. The active nature of Synthetic Aperture Radar (SAR) instruments allows for observations through clouds, which can supplement efforts to estimate hurricane-induced flood impacts. A growing fleet of SAR constellations has helped lower the latency of imagery following a hurricane, allowing for more timely detections of flooding to help support emergency response efforts. In this study, we leverage publicly available C-band SAR observations from the European Space Agency’s Sentinel-1B (S1B) satellite, collected on 30 August, and X-band SAR imagery collected on 1 September by the Airbus TanDEM-X (TDX) satellite made available through the NASA Commercial Smallsat Data Acquisition (CSDA) program. For each dataset, one co-polarized, StripMap, Radiometric Terrain Corrected (RTC) image was used to create a binary water/no water classification map by referencing permanent water in the Cropland Data Layer (CDL) dataset to determine thresholds. A validation dataset of randomly distributed “ground truth” points was generated using optical imagery from PlanetScope on 31 August, where the domain had relatively little cloud cover. We found that the S1B- and TDX-derived open water maps achieved overall accuracies of 94.17% and 91.57%, respectively. The variation in performance is attributed to both the penetrative abilities of C- and X-band SAR wavelengths in vegetated areas and the increased spatial resolution of the commercial SAR (~3 m) over Sentinel-1 (30 m). While publicly available Sentinel-1 observations are commonly relied upon in response and recovery efforts, these results suggest that including commercial X-band SAR imagery could be beneficial by providing both increased spatial resolution and more frequent revisits when deriving post-event flood mapping products.

Alexander M Melancon↗
DOE OSTI2020

Genetic Algorithm for Hyperparameter Optimization in Gaussian Process Modeling

A genetic algorithm is developed and applied to optimize hyperparameters of convolutional recursively determined dual neural network-Gaussian process (NNGP) kernels. As a specific application of the combined GPNN-GA algorithm, it is applied to image classification in publicly available data of Hyper Suprime-Cam Subaru Strategic Program. Matthews correlation coefficient is calculated based on results of binary star-galaxy classification and used as a fitting function of the GA module of the algorithm. The simulation results confirm significant improvement of the classification accuracy with optimized hyperparameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

THE CLASSIFICATION OF KEPLER B-STAR VARIABLES

The light curves of 252 B-star candidates in the Kepler database are analyzed in a similar fashion to that done by Balona et al. to further characterize B-star variability, increase the sample of variable B stars for future study, and to identify stars whose power spectra include particularly interesting features such as frequency groupings. Stars are classified as either constant light emitters, {beta} Cep stars, slowly pulsating B stars (SPBs), hybrid pulsators, binaries or stars whose light curves are dominated by rotation (Bin/Rot), hot subdwarfs, or white dwarfs. One-hundred stars in our sample were found to be either light constants or to be variable at a level of less than 0.02 mmag. We increase the number of candidate B-star variables found in the Kepler database by Balona et al. in the following fashion: {beta} Cep stars from 0 to 10, SPBs from eight to 54, hybrid pulsators from seven to 21, and Bin/Rot stars from 23 to 82. For comparison purposes, approximately 51 SPBs and six hybrids had been known prior to 2007. The number of {beta} Cep stars known prior to 2004 was 93. A secondary result of this study is the identification of an additional 11 pulsating white dwarf candidates, four of which possess frequency groupings.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2010

LOW-AMPLITUDE VARIABLES: DISTINGUISHING RR LYRAE STARS FROM ECLIPSING BINARIES

It is not easy to identify and classify low-amplitude variables, but it is important that the classification is done correctly. We use photometry and spectroscopy to classify low-amplitude variables in a 246 deg{sup 2} part of the Akerlof et al. field. Akerlof and Collaborators found that 38% of the RR Lyrae stars in their 2000 deg{sup 2} test field were RR1 (type c). This suggests that these RR Lyrae stars belong to an Oosterhoff Type II population, while their period distribution is primarily Oosterhoff Type I. Our observations support their RR0 (type ab) classifications; however, six of the seven stars that they classified as RR1 (type c) are eclipsing binaries. Our classifications are supported by spectroscopic metallicities, line-broadening, and Galactic rotation measurements. Our 246 deg{sup 2} field contains 16 RR Lyrae stars that are brighter than m{sub R} = 14.5; only four of these are RR1 (type c). This corresponds to an Oosterhoff Type I population in agreement with the period distribution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

PHOTOMETRIC TYPE Ia SUPERNOVA CANDIDATES FROM THE THREE-YEAR SDSS-II SN SURVEY DATA

We analyze the three-year Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey data and identify a sample of 1070 photometric Type Ia supernova (SN Ia) candidates based on their multiband light curve data. This sample consists of SN candidates with no spectroscopic confirmation, with a subset of 210 candidates having spectroscopic redshifts of their host galaxies measured while the remaining 860 candidates are purely photometric in their identification. We describe a method for estimating the efficiency and purity of photometric SN Ia classification when spectroscopic confirmation of only a limited sample is available, and demonstrate that SN Ia candidates from SDSS-II can be identified photometrically with {approx}91% efficiency and with a contamination of {approx}6%. Although this is the largest uniform sample of SN candidates to date for studying photometric identification, we find that a larger spectroscopic sample of contaminating sources is required to obtain a better characterization of the background events. A Hubble diagram using SN candidates with no spectroscopic confirmation, but with host galaxy spectroscopic redshifts, yields a distance modulus dispersion that is only {approx}20%-40% larger than that of the spectroscopically confirmed SN Ia sample alone with no significant bias. A Hubble diagram with purely photometric classification and redshift-distance measurements, however, exhibits biases that require further investigation for precision cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2010

REVISED SUPERNOVA RATES FROM THE IfA DEEP SURVEY

The IfA Deep Survey uncovered {approx} 130 thermonuclear supernova (TN SN, i.e., Type Ia) candidates at redshifts from z = 0.1 out to beyond z = 1. The TN SN explosion rates derived from these data have been controversial, conflicting with evidence emerging from other surveys. This work revisits the IfA Deep Survey to re-evaluate the photometric evidence. Applying the SOFT program to the light curves of all SN candidates, we derive new classification grades and redshift estimates. We find a volumetric rate for z {approx} 0.5 that is substantially smaller than the originally published values, bringing the revised IfA Deep rate into good agreement with other surveys. With our improved photometric analysis techniques, we are able to confidently extend the rate measurements to higher redshifts and we find a steadily increasing TN SN rate, with no indication of a peak out to z = 1.05.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2010

THE DUSTY NOVA V1065 CENTAURI (NOVA CEN 2007): A SPECTROSCOPIC ANALYSIS OF ABUNDANCES AND DUST PROPERTIES

We examine the ejecta evolution of the classical nova V1065 Centauri, constructing a detailed picture of the system based on spectrophotometric observations obtained from 9 to approximately 900 days post-outburst with extensive coverage from optical to mid-infrared wavelengths. We estimate a reddening toward the system of E(B-V) = 0.5 {+-} 0.1, based upon the B - V color and analysis of the Balmer decrement, and derive a distance estimate of 8.7{sup +2.8}{sub -2.1} kpc. The optical spectral evolution is classified as P {sup o}{sub fe} N{sub ne} A{sub o} according to the CTIO Nova Classification system of Williams et al. Photoionization modeling yields absolute abundance values by number, relative to solar of He/H = 1.6 {+-} 0.3, N/H = 144 {+-} 34, O/H = 58 {+-} 18, and Ne/H = 316 {+-} 58 for the ejecta. We derive an ejected gas mass of M{sub g} = (1.6 {+-} 0.2) x 10{sup -4} M{sub sun}. The infrared excess at late epochs in the evolution of the nova arises from dust condensed in the ejecta composed primarily of silicate grains. We estimate a total dust mass, M{sub d} , of order (0.2-3.7) x 10{sup -7} M{sub sun}, inferred from modeling the spectral energy distribution observed with the Spitzer IRS and Gemini-South GNIRS spectrometers. Based on the speed class, neon abundance, and the predominance of silicate dust, we classify V1065 Cen as an ONe-type classical nova.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Accelerating Random Forest Classification on GPU and FPGA

Random Forests (RFs) are a commonly used machine learning method for classification and regression tasks spanning a variety of application domains, including bioinformatics, business analytics, and software optimization. While prior work has focused primarily on improving performance of the training of RFs, many applications, such as malware identification, cancer prediction, and banking fraud detection, require fast RF classification. In this work, we accelerate RF classification on GPU and FPGA. In order to provide efficient support for large datasets, we propose a hierarchical memory layout suitable to the GPU/FPGA memory hierarchy. We design three RF classification code variants based on that layout, and we investigate GPU- and FPGA-specific considerations for these kernels. Our experimental evaluation, performed on an Nvidia Xp GPU and on a Xilinx Alveo U250 FPGA accelerator card using publicly available datasets on the scale of millions of samples and tens of features, covers various aspects. First, we evaluate the performance benefits of our hierarchical data structure over the standard compressed sparse row (CSR) format. Second, we compare our GPU implementation with cuML, a machine learning library targeting Nvidia GPUs. Third, we explore the performance/accuracy tradeoff resulting from the use of different tree depths in the RF. Finally, we perform a comparative performance analysis of our GPU and FPGA implementations. Our evaluation shows that for high accuracy targets, our GPU implementation yields 5-9x speedup over CSR, and up to a 2x speedup over cuML.

FPGA, Xilinx FPGA, GPU, Random Forest classificati↗
DOE OSTI2015

Discrimination of Mild Cognitive Impairment and Alzheimer′s Disease Using Transfer Entropy Measures of Scalp EEG

Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer′s disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age‐matched participants (mean age 75.7 years)—15 normal controls, 16 MCI, and 17 early AD—are examined. The mean temporal delays corresponding to peaks in inter‐regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three‐way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7— 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.

McBride, Joseph↗
DOE OSTI2017

The Galah Survey: Classification and Diagnostics with t-SNE Reduction of Spectral Information

Galah is an ongoing high-resolution spectroscopic survey with the goal of disentangling the formation history of the Milky Way using the fossil remnants of disrupted star formation sites that are now dispersed around the Galaxy. It is targeting a randomly selected magnitude-limited ( V ≤ 14) sample of stars, with the goal of observing one million objects. To date, 300,000 spectra have been obtained. Not all of them are correctly processed by parameter estimation pipelines, and we need to know about them. We present a semi-automated classification scheme that identifies different types of peculiar spectral morphologies in an effort to discover and flag potentially problematic spectra and thus help to preserve the integrity of the survey results. To this end, we employ the recently developed dimensionality reduction technique t-SNE ( t -distributed stochastic neighbor embedding), which enables us to represent the complex spectral morphology in a two-dimensional projection map while still preserving the properties of the local neighborhoods of spectra. We find that the majority (178,483) of the 209,533 Galah spectra considered in this study represents normal single stars, whereas 31,050 peculiar and problematic spectra with very diverse spectral features pertaining to 28,579 stars are distributed into 10 classification categories: hot stars, cool metal-poor giants, molecular absorption bands, binary stars, H α /H β emission, H α /H β emission superimposed on absorption, H α /H β P-Cygni, H α /H β inverted P-Cygni, lithium absorption, and problematic. Classified spectra with supplementary information are presented in the catalog, indicating candidates for follow-up observations and population studies of the short-lived phases of stellar evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1994

Design of Ternary Correlation Filters to Reduce Probability of Error

The problem of designing ternary phase and amplitude filters (TPAF's) that reduce the probability of image misclassification for a two-class image set is studied. The Fisher ratio is used as a measure of the correct classification rate, and an attempt is made to maximize this quantity in the filter designs. Given the nonanalytical nature of the design problem, a simulated annealing optimization technique is employed. Computer simulation results are presented for several cases including single in-class and out-of-class image sets and multiple image sets corresponding to the design of synthetic discriminant function filters. Significant improvements are found in expected rates of correct classification in comparison to binary phase-only filters and other TPAF designs. Approaches to accelerate the filter design process are also discussed.

Downie, John D.↗
DOE OSTI2011

THE CONTINUING OUTBURST OF V1647 ORIONIS: WINTER/SPRING 2011 OBSERVATIONS

We present optical and near-IR observations of the young eruptive variable star V1647 Orionis which illuminates McNeil's Nebula. In late 2003, V1647 Ori was observed to brighten by around 5 mag to r' = 17.7. In early 2006 the star faded back to its quiescent brightness of r' {approx} 23, however in mid-2008 it brightened yet again by {approx}5 mag. Our new observations, taken in early 2011, show V1647 Ori to be in an elevated photometric state with an optical brightness similar to the value found at the start of the 2003 and 2008 outbursts. Optical images taken between 2008 and 2011 suggest that the star has remained in outburst from mid-2008 to the present. H{alpha} and the far-red Ca II triplet lines remain in emission with H{alpha} possessing a significant P Cygni profile. A self-consistent study of the accretion luminosity and rate using data taken in 2004, 2007, 2008, and 2011 indicates that when bright, V1647 Ori has values of 16 {+-} 2 L{sub sun} and (4 {+-} 2) x 10{sup -6} M{sub sun} yr{sup -1}, respectively. We support the premise that the accretion luminosity and rate both declined by a factor of 2-3 during the 5 mag fading in 2007. However, a significant part of the fading was due to either variable extinction or dust reformation. We discuss these new observations in relation to previous published data and the classification schemes for young eruptive variables.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

A Double-line M-dwarf Eclipsing Binary from CSS × SDSS

Eclipsing binaries offer a unique opportunity to determine basic stellar properties. With the advent of wide-field camera and all-sky time-domain surveys, thousands of eclipsing binaries have been charted via light curve classification, yet their fundamental properties remain unexplored mainly due to the extensive efforts needed for spectroscopic follow-ups. In this paper, we present the discovery of a short-period ( P = 0.313 day), double-lined M-dwarf eclipsing binary, CSSJ114804.3+255132/SDSSJ114804.35+255132.6, by cross-matching binary light curves from the Catalina Sky Survey and spectroscopically classified M dwarfs from the Sloan Digital Sky Survey. We obtain follow-up spectra using the Gemini telescope, enabling us to determine the mass, radius, and temperature of the primary and secondary component to be M {sub 1} = 0.47 ± 0.03(statistic) ± 0.03(systematic) M {sub ⊙}, M {sub 2} = 0.46 ± 0.03(statistic) ± 0.03(systematic) M {sub ⊙}, R {sub 1} = 0.52 ± 0.08(statistic) ± 0.07(systematic) R {sub ⊙}, R {sub 2} =0.60 ± 0.08(statistic) ± 0.08(systematic) R {sub ⊙}, T {sub 1} = 3560 ± 100 K, and T {sub 2} = 3040 ± 100 K, respectively. The systematic error was estimated using the difference between eccentric and non-eccentric fits. Our analysis also indicates that there is definitively third-light contamination (66%) in the CSS photometry. The secondary star seems inflated, probably due to tidal locking of the close secondary companion, which is common for very short-period binary systems. Future spectroscopic observations with high resolution will narrow down the uncertainties of stellar parameters for both components, rendering this system as a benchmark for studying fundamental properties of M dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).

Lure, Y. M. Fleming↗
DOE OSTI2022

XMM–Newton study of the Sculptor dwarf spheroidal galaxy

ABSTRACT We analysed observations with XMM–Newton in the field of the Sculptor dwarf spheroidal galaxy (dSph). The aim of the study was the classification of X-ray binaries and accreting white dwarfs belonging to the Sculptor dSph. Using different methods of X-ray timing and spectral analyses, together with an extensive multiwavelength study of the optical and infrared counterparts of the X-ray sources, we classified the sources detected with XMM–Newton in the field of the Sculptor dSph. The long-term variability of the sources has been studied over two XMM–Newton observations. None of the members of the Sculptor dSph show significant long-term variability over these two observations. We also searched for periodicity and pulsation using the Lomb–Scargle and Rayleigh Z$^{2}_{n}$ techniques. No signals of pulsation or periodicity have been found for the X-ray sources. The results show the presence of a noticeable number of background X-ray sources in the field of this galaxy. We classified 43 sources as active galactic nuclei, galaxies, and galaxy candidates. Three Galactic foreground stars have been identified in the field of the Sculptor dSph, and one of them is an M-dwarf candidate. Moreover, we classified four symbiotic-star candidates and three quiescent low-mass X-ray binary candidates in the Sculptor dSph. The luminosity of these X-ray sources is ∼1033−35 erg s−1.

79 ASTRONOMY AND ASTROPHYSICS↗