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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Genome-Wide Identification, Classification, and Expression Analysis of the Hsf Gene Family in Carnation ( Dianthus caryophyllus )

Heat shock transcription factors (Hsfs) are a class of important transcription factors (TFs) which play crucial roles in the protection of plants from damages caused by various abiotic stresses. The present study aimed to characterize the Hsf genes in carnation (Dianthus caryophyllus), which is one of the four largest cut flowers worldwide. In this study, a total of 17 non-redundant Hsf genes were identified from the D. caryophyllus genome. Specifically, the gene structure and motifs of each DcaHsf were comprehensively analyzed. Phylogenetic analysis of the DcaHsf family distinctly separated nine class A, seven class B, and one class C Hsf genes. Additionally, promoter analysis indicated that the DcaHsf promoters included various cis-acting elements that were related to stress, hormones, as well as development processes. In addition, cis-elements, such as STRE, MYB, and ABRE binding sites, were identified in the promoters of most DcaHsf genes. According to qRT-PCR data, the expression of DcaHsfs varied in eight tissues and six flowering stages and among different DcaHsfs, even in the same class. Moreover, DcaHsf-A1, A2a, A9a, B2a, B3a revealed their putative involvement in the early flowering stages. The time-course expression profile of DcaHsf during stress responses illustrated that all the DcaHsfs were heat- and drought-responsive, and almost all DcaHsfs were down-regulated by cold, salt, and abscisic acid (ABA) stress. Meanwhile, DcaHsf-A3, A7, A9a, A9b, B3a were primarily up-regulated at an early stage in response to salicylic acid (SA). This study provides an overview of the Hsf gene family in D. caryophyllus and a basis for the breeding of stress-resistant carnation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

Redshift Measurement and Spectral Classification for eBoss Galaxies with the Redmonster Software

“Cosmological redshift surveys” are experiments conducted with astronomical telescopes, imagers, and spectrographs, which map the three-dimensional structure of the universe on the largest scales. These maps are delineated by the positions of galaxies, quasars, and intergalactic hydrogen clouds. When interpreted in the context of Einstein’s theory of gravity, these maps can be used to infer the nature of the contents of the universe, including the mysterious “dark energy” that is driving the expansion of the universe to accelerate. While the directional positions of galaxies and other objects can be measured directly in images of the sky, the third dimension of their position (i.e., their distance from the Earth and the Milky Way Galaxy) must be measured by spectrographs that distribute their light as a function of frequency, enabling a measurement of their cosmological Doppler shift (or “redshift”), which serves as an observable proxy for distance. The largest cosmological redshift surveys, such as the “eBOSS” experiment of the fourth Sloan Digital Sky Survey, collect spectroscopic data for hundreds of thousands to millions of galaxies. Future experiments such as the Dark Energy Spectroscopic Instrument will in turn collect tens of millions of spectra. To be feasible, redshift measurement methods in datasets of this scale must be made with automated software. This paper describes the algorithms, astrophysical templates, and implementation of a new redshift measurement software package that is optimized to run on large numbers of spectra with relatively low signal-to-noise ratio, typical of the most ambitious current and future cosmological redshift surveys. The software is demonstrated on spectroscopic data from the eBOSS survey, with performance that meets the scientific requirements of that experiment. The software is implemented in a general framework that will allow application to spectra from the DESI project in the future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Identification of RR Lyrae stars in multiband, sparsely-sampled data from the Dark Energy Survey using template fitting and Random Forest classification

Many studies have shown that RR Lyrae variable stars (RRL) are powerful stellar tracers of Galactic halo structure and satellite galaxies. The Dark Energy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag in a single exposure; over 5000 deg2) provides a rich opportunity to search for substructures out to the edge of the Milky Way halo. However, the sparse and unevenly sampled multiband light curves from the DES wide-field survey (a median of four observations in each of grizY over the first three years) pose a challenge for traditional techniques used to detect RRL. We present an empirically motivated and computationally efficient template-fitting method to identify these variable stars using three years of DES data. When tested on DES light curves of previously classified objects in SDSS stripe 82, our algorithm recovers 89% of RRL periods to within 1% of their true value with 85% purity and 76% completeness. Using this method, we identify 5783 RRL candidates, ~28% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data sets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

The sami galaxy survey: revisiting galaxy classification through high-order stellar kinematics

Recent cosmological hydrodynamical simulations suggest that integral field spectroscopy can connect the high-order stellar kinematic moments h{sub 3} (∼skewness) and h{sub 4} (∼kurtosis) in galaxies to their cosmological assembly history. Here, we assess these results by measuring the stellar kinematics on a sample of 315 galaxies, without a morphological selection, using two-dimensional integral field data from the SAMI Galaxy Survey. Proxies for the spin parameter (λ{sub R{sub e}}) and ellipticity (ϵ{sub e}) are used to separate fast and slow rotators; there exists a good correspondence to regular and non-regular rotators, respectively, as also seen in earlier studies. We confirm that regular rotators show a strong h{sub 3} versus V/σ anti-correlation, whereas quasi-regular and non-regular rotators show a more vertical relation in h{sub 3} and V/σ. Motivated by recent cosmological simulations, we develop an alternative approach to kinematically classify galaxies from their individual h{sub 3} versus V/σ signatures. Within the SAMI Galaxy Survey, we identify five classes of high-order stellar kinematic signatures using Gaussian mixture models. Class 1 corresponds to slow rotators, whereas Classes 2–5 correspond to fast rotators. We find that galaxies with similar λ{sub R{sub e}}--ϵ{sub e} values can show distinctly different h{sub 3}--V/σ signatures. Class 5 objects are previously unidentified fast rotators that show a weak h{sub 3} versus V/σ anti-correlation. From simulations, these objects are predicted to be disk-less galaxies formed by gas-poor mergers. From morphological examination, however, there is evidence for large stellar disks. Instead, Class 5 objects are more likely disturbed galaxies, have counter-rotating bulges, or bars in edge-on galaxies. Finally, we interpret the strong anti-correlation in h{sub 3} versus V/σ as evidence for disks in most fast rotators, suggesting a dearth of gas-poor mergers among fast rotators.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Identifying the 3FHL Catalog. I. Archival Swift Observations and Source Classification

We present the results of an identification campaign of unassociated sources from the Fermi Large Area Telescope 3FHL catalog. Out of 200 unidentified sources, we selected 110 sources for which archival Swift-XRT observations were available, 52 of which were found to have exactly one X-ray counterpart within the 3FHL 95% positional uncertainty. In this work, we report the X-ray, optical, IR, and radio properties of these 52 sources using positional associations with objects in various catalogs. The Wide-field Infrared Survey Explorer color–color plot for sources suggests that most of these belong to the blazar class family. The redshift measurements for these objects range from z = 0.277 to z = 2.1. Additionally, under the assumption that the majority of these sources are blazars, three machine-learning algorithms are employed to classify the sample into flat spectrum radio quasars or BL Lacertae objects. Finally, these suggest that the majority of the previously unassociated sources are BL Lac objects, in agreement with the fact the BL Lac objects represent by far the most numerous population detected above 10 GeV in 3FHL.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Impact of Void-finding Algorithms on Galaxy Classification

We explore how the definition of a void influences the conclusions drawn about the impact of the void environment on galactic properties using two void-finding algorithms in the Void Analysis Software Toolkit: Voronoi Voids (V 2 ), a Python implementation of ZOnes Bordering On Voidness (ZOBOV); and VoidFinder, an algorithm that grows and merges spherical void regions. Using the Sloan Digital Sky Survey Data Release 7, we find that galaxies found in VoidFinder voids tend to be bluer and fainter and to have higher (specific) star formation rates than galaxies in denser regions. Conversely, galaxies found in V 2 voids show less significant differences when compared to galaxies in denser regions, less consistent with the large-scale environmental effects on galaxy properties expected from both simulations and previous observations. These results align with previous simulation results that show V 2 -identified voids “leak” into the dense walls between voids because their boundaries extend up to the density maxima in the walls. As a result, when using ZOBOV-based void-finders, galaxies likely to be part of wall regions are instead classified as void galaxies, a misclassification that can be critical to our understanding of galaxy evolution.

cosmic web↗
DOE OSTI2018

A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures

Atomic-level structural characterization of flexible proteins, such as intrinsically disordered proteins and multi-domain proteins connected by flexible linkers, is challenging as they possess distinct conformations in physiological conditions. Significant efforts have been made to develop integrated approaches by combining small angle neutron/X-ray scattering experiments with molecular simulations to reveal the distinct atomic structures and the corresponding populations for these flexible proteins. One widely used method, the basis-set supported ensemble method, classifies the simulation-generated protein conformations into a set of structural basis and then derives the corresponding populations by fitting to the experimental data. This method makes an implicit assumption that protein conformations of similar structures have similar small angle scattering profiles.The present work demonstrates that, for various protein systems ranging from compact globular proteins and flexible multidomain proteins through to intrinsically disordered proteins, this method provides inaccurate assessment of the structural ensemble of the protein molecules due to the breakdown of the assumption made. To alleviate this problem, a two-fold-clustering method is developed to cluster the simulation-generated protein structures using information on both 3D structure and scattering profiles. As benchmarked by both simulation and experimental results, this new method yields much more accurate populations of structural basis of protein molecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High resolution hydrologic routing with machine learning assisted waterbody classification

This software contains the code for a machine learning-based pipeline for creating persistent waterbody databases used in hydrologic routing. It consists of three components, 1) a PyTorch library (TorchWBType) for classifying/labeling arbitrary waterbodies into lakes and non-lakes, 2) a batch processing orchestrator (wbextractor) for delineating waterbodies from remote sensing imagery, and 3) graphing/analysis scripts for reproducing the plots in an associated journal article (LA-UR-24-22590).

Stachelek, Jemma↗