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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Measuring the electron temperature and identifying plasma detachment using machine learning and spectroscopy

A machine learning approach has been implemented to measure the electron temperature directly from the emission spectra of a tokamak plasma. This approach utilized a neural network (NN) trained on a dataset of 1865 time slices from operation of the DIII-D tokamak using extreme ultraviolet/vacuum ultraviolet emission spectroscopy matched with high-accuracy divertor Thomson scattering measurements of the electron temperature, T e . This NN is shown to be particularly good at predicting T e at low temperatures (T e < 10 eV) where the NN demonstrated a mean average error of less than 1 eV. Trained to detect plasma detachment in the tokamak divertor, a NN classifier was able to correctly identify detached states (T e < 5 eV) with a 99% accuracy (an F 1 score of 0.96) at an acquisition rate 10× faster than the Thomson scattering measurement. The performance of the model is understood by examining a set of 4800 theoretical spectra generated using collisional radiative modeling that was also used to predict the performance of a low-cost spectrometer viewing nitrogen emission in the visible wavelengths. Furthermore, these results provide a proof-of-principle that low-cost spectrometers leveraged with machine learning can be used to boost the performance of more expensive diagnostics on fusion devices and be used independently as a fast and accurate T e measurement and detachment classifier.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Machine learning methods for probabilistic locked-mode predictors in tokamak plasmas

A rotating tokamak plasma can interact resonantly with the external helical magnetic perturbations, also known as error fields. This can lead to locking and then to disruptions. We leverage machine learning (ML) methods to predict the locking events. We use a coupled third-order nonlinear ordinary differential equation model to represent the interaction of the magnetic perturbation and the plasma rotation with the error field. This model is sufficient to describe qualitatively the locking and unlocking bifurcations. Here, we explore using ML algorithms with the simulation data and experimental data, focusing on the methods that can be used with sparse datasets. These methods lead to the possibility of the avoidance of locking in real-time operations. We describe the operational space in terms of two control parameters: the magnitude of the error field and the rotation frequency associated with the momentum source that maintains the plasma rotation. The outcomes are quan- tified by order parameters that completely characterize the state, whether locked or unlocked. We use unsupervised ML methods to classify locked/unlocked states and note the usefulness of a certain normalization of the order parameters. Three supervised ML classifiers are used in suite to estimate the probability of locking in the region of control parameter space with hysteresis, i.e., the set of control parameters for which both locked and unlocked states can exist. The results show that a neural network gives the best estimate of the locking probability. An analogy of the present locking model with the van der Waals equation of state is also provided.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The superatomic state beyond conventional magic numbers: Ligated metal chalcogenide superatoms

The field of cluster science is drawing increasing attention due to the strong size and composition-dependent properties of clusters and the exciting prospect of clusters serving as the building blocks for materials with tailored properties. However, identifying a unifying central paradigm that provides a framework for classifying and understanding the diverse behaviors is an outstanding challenge. One such central paradigm is the superatom concept that was developed for metallic and ligand-protected metallic clusters. The periodic electronic and geometric closed shells in clusters result in their properties being based on the stability they gain when they achieve closed shells. This stabilization results in the clusters having a well-defined valence allowing them to be classified as superatoms – thus, extending the periodic table to a third dimension. This perspective focuses on extending the superatomic concept to ligated metal-chalcogen clusters that have recently been synthesized in solutions and form assemblies with counterions that have wide-ranging applications. Here we illustrate that the periodic patterns emerge in the electronic structure of ligated metal-chalcogenide clusters. The stabilization gained by the closing of their electronic shells allows for the prediction of their redox properties. Further investigations reveal how the selection of ligands may control the redox properties of the superatoms. These ligated clusters may serve as chemical dopants for two-dimensional semiconductors to control their transport characteristics. Superatomic molecules of multiple metal-chalcogen superatoms allow for the formation of nano pn junctions ideal for directed transport and photon harvesting. As a result, the perspective outlines future developments, including the synthesis of magnetic superatoms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Demonstration of object location, classification, and characterization by developed deep learning dust ablation trail analysis code package using plasma jets

Based on deep learning, a Dust Ablation Trail Analysis (DATA) code package is developed to detect dust ablation trails in tokamaks, which is intended to analyze a large amount data of tokamak dusts. To validate and benchmark the DATA code package, 2440 plasma jet images are exploited for the training and test of the deep learning DATA code package, since plasma jets resemble the shape and size of dust ablation clouds in tokamaks. After being trained by 1920 plasma jet images, the DATA code package is able to locate 100% plasma jets, classify plasma jets with the accuracy of >99.9%, and output image skeleton information for classified plasma jets. The DATA code package trained by the plasma jet images is also used to analyze the dust ablation trails captured in the Experimental Advanced Superconducting (EAST) tokamak with the satisfactory performance, further verifying its applicability in the fusion dust ablation investigation. Based on its excellent performance presented here, it is demonstrated that our DATA code package is able to automatically identify and analyze dust ablation trails in tokamaks, which can be used for further detailed investigations, such as the three-dimensional reconstruction of dusts and their ablation trails.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

A glutamate/aspartate switch controls product specificity in a protein arginine methyltransferase

Trypanosoma brucei PRMT7 (TbPRMT7) is a protein arginine methyltransferase (PRMT) that strictly monomethylates various substrates, thus classifying it as a type III PRMT. However, the molecular basis of its unique product specificity has remained elusive. Here, we present the structure of TbPRMT7 in complex with its cofactor product S-adenosyl-L-homocysteine (AdoHcy) at 2.8 Å resolution and identify a glutamate residue critical for its monomethylation behavior. TbPRMT7 comprises the conserved methyltransferase and β-barrel domains, an N-terminal extension, and a dimerization arm. The active site at the interface of the N-terminal extension, methyltransferase, and β-barrel domains is stabilized by the dimerization arm of the neighboring protomer, providing a structural basis for dimerization as a prerequisite for catalytic activity. Mutagenesis of active-site residues highlights the importance of Glu181, the second of the two invariant glutamate residues of the double E loop that coordinate the target arginine in substrate peptides/proteins and that increase its nucleophilicity. Strikingly, mutation of Glu181 to aspartate converts TbPRMT7 into a type I PRMT, producing asymmetric dimethylarginine (ADMA). Isothermal titration calorimetry (ITC) using a histone H4 peptide showed that the Glu181Asp mutant has markedly increased affinity for monomethylated peptide with respect to the WT, suggesting that the enlarged active site can favorably accommodate monomethylated peptide and provide sufficient space for ADMA formation. In conclusion, these findings yield valuable insights into the product specificity and the catalytic mechanism of protein arginine methyltransferases and have important implications for the rational (re)design of PRMTs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2

The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π-π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single–amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single– and multiple–amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana . A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.

09 BIOMASS FUELS↗
DOE OSTI2017

Book Review: What Makes a Good Experiment? Reasons and Roles in Science

Classification is an arduous and sometimes controversial undertaking because of its inherent cultural particularity. One famous literary example is the fictitious nomenclature of animals found in Jorge Luis Borges’s ‘The Analytical Language of John Wilkins’. According to Borges, that taxonomy was discovered by a translator, F. Kuhn, in a Chinese source and divides all animals into fourteen categories, starting with ‘those that belong to the Emperor’ and ending with ‘those that from a long way off look like flies’. Physics of the twentieth century is a ramified full-fledged technical culture, and it should surprise no one that physics experiments can be classified in different ways, allowing certain experiments to belong to more than one category. Allan Franklin’s analysis of experiments is based on his classification, which substantially grasps the essence of the experimental enterprise. Although Franklin does not offer an explicit answer to the question posed in the title, he does include a detailed examination of several important physics and biology experiments in history

Pronskikh, Vitaly↗
DOE OSTI2022

A Machine Learning Method for the Forensics Attribution of Separated Plutonium

Plutonium (Pu) source attribution would be a powerful tool to support nuclear nonproliferation efforts. This capability to find the source of a Pu sample would act as a deterrent to smuggling efforts, and also help regulatory agencies verify declared nuclear activities. Work at Texas A&M University yielded a nuclear forensics methodology, which is capable of determining separated Pu’s reactor of origin, fuel burnup, and the time since irradiation (TSI)—three parameters of interest. The methodology used a set of ten intra-element isotopic ratios found in separated Pu, which was compared to a library of isotopic ratio values produced using neutronics simulations for reactors of interest. By calculating the probability that unknown Pu sample’s isotopic ratio set matched a set in the library, the methodology could predict the three parameters of interest of the sample. One shortcoming of this methodology was an inability to correctly attribute spoofed Pu, where Pu sourced from two different reactors or two different fuel burnup levels are mixed. A new methodology to rectify this vulnerability using machine learning (ML) technique is developed, instead of the maximum likelihood calculation previously used and the results are satisfactory. The ML approach leverages the existing simulated data for training the algorithm, but use them efficiently by only using intra-element isotope ratios that contribute to the attribution one of the three parameters at a time. Previously, all isotope ratios were used to attribute all three parameters together. The new methodology attributes the Pu parameters in three steps, one for each parameter, rather than resolving all of the three parameters simultaneously like the previous maximum likelihood approach. First, a support vector machine classifier with a set of seven isotopic ratios finds the reactor of origin and a set of regression models trained using gaussian process predicts the burnup with a different set of seven isotopes. Finally, TSI is calculated analytically using decay equations. Thus far, the new methodology is capable of attributing pure Pu samples and has been validated using experimental data. The next step will to be augment the classifier training data set with spoofed Pu data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Identification of Important Phenomena for Light Water Reactors During Heat Transport System Failure Events in Integrated Energy Systems

This work adapts historical literature and existing phenomena identification and ranking tables (PIRT) to be applicable to a novel nuclear power plant (NPP) and chemical or thermal process integrated energy system (IES), particularly focusing on the process heat and heat transport system failure events that are not a concern during normal NPP operation but become vital when an IES is considered. Nuclear energy has been suggested to go beyond base-load applications and be used for hydrogen co-generation systems, amongst other IESs. Prior to the implementation of nuclear IESs, sufficient analysis must be performed on accident events to ensure public safety. The events considered were deemed important because of their potential to damage systems, structures, and components (SSCs). Process thermal events of concern include loss of heat load and temperature transient events. Loss of heat load events were characterized as having high importance and being well understood. Temperature transient events may be further categorized by the cyclic loading and harmonics phenomena. Cyclic loading issues were classified as medium to high importance with knowledge gaps existing regarding fatigue and low power operation, while harmonics phenomena were classified as low importance and are well understood. Heat transport system failure events of concern include intermediate and process heat exchanger failures, mass addition to reactor coolant, ingress of material from thermal manifold/energy storage, and loss of intermediate fluid. Furthermore, these events tended to be of high or medium importance, with some knowledge gaps needing to be filled for individual reactor systems due to unique designs.

Integrated Energy System (IES)↗
DOE OSTI2014

e/a classification of Hume–Rothery Rhombic Triacontahedron-type approximants based on all-electron density functional theory calculations

There are three key electronic parameters in elucidating the physics behind the Hume–Rothery electron concentration rule: the square of the Fermi diameter (2kF)2, the square of the critical reciprocal lattice vector and the electron concentration parameter or the number of itinerant electrons per atom e/a. We have reliably determined these three parameters for 10 Rhombic Triacontahedron-type 2/1–2/1–2/1 (N = 680) and 1/1–1/1–1/1 (N = 160–162) approximants by making full use of the full-potential linearized augmented plane wave-Fourier band calculations based on all-electron density-functional theory. We revealed that the 2/1–2/1–2/1 approximants Al13Mg27Zn45 and Na27Au27Ga31 belong to two different sub-groups classified in terms of equal to 126 and 109 and could explain why they take different e/a values of 2.13 and 1.76, respectively. Among eight 1/1–1/1–1/1 approximants Al3Mg4Zn3, Al9Mg8Ag3, Al21Li13Cu6, Ga21Li13Cu6, Na26Au24Ga30, Na26Au37Ge18, Na26Au37Sn18 and Na26Cd40Pb6, the first two, the second two and the last four compounds were classified into three sub-groups with = 50, 46 and 42; and were claimed to obey the e/a = 2.30, 2.10–2.15 and 1.70–1.80 rules, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Untold Story of Building the First Megaton Thermonuclear Fusion Device: The Simple Element and IVY Mike

This report is a research and development engineer’s perspective on the fascinating story of the world’s first megaton-class thermonuclear device, IVY Mike (10.4 Mt). Few modern scientific endeavors have matched the complexity and breadth of scientific achievement in such a short amount of time as IVY Mike. This paper will take a look at the design, engineering, fielding, and execution of the world’s first megaton-class physics experiment by Los Alamos Scientific Laboratory and the birth of industrial liquid hydrogen use spawned by the Cold War effort. Although others have written on aspects of this technical history, they have not benefited from access to the original classified documents used by the present author. The present paper must necessarily omit some technical details that remain classified but represents the most comprehensive summary of the engineering and fabrication of the IVY Mike device in the open literature.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2011

THE OUTER DISKS OF EARLY-TYPE GALAXIES. II. SURFACE-BRIGHTNESS PROFILES OF UNBARRED GALAXIES AND TRENDS WITH HUBBLE TYPE

We present azimuthally averaged radial profiles of R-band surface brightness for a complete sample of 47 early-type, unbarred galaxies, as a complement to our previous study of early-type barred galaxies. Following very careful sky subtraction, the profiles can typically be determined down to brightness levels well below 27 mag arcsec{sup -2} and in the best cases below 28 mag arcsec{sup -2}. We classified the profiles according to the scheme used previously for the barred sample: Type I profiles are single unbroken exponential radial declines in brightness; Type II profiles ('truncations') have an inner shallow slope (usually exponential) which changes at a well-defined break radius to a steeper exponential; and Type III profiles ('antitruncations') have an inner exponential that is steeper, giving way to a shallower outer (usually exponential) decline. By combining these profiles with previous studies, we can make the first clear statements about the trends of outer-disk-profile types along the Hubble sequence (including both barred and unbarred galaxies), and their global frequencies. We find that Type I profiles are most frequent in early-type disks, decreasing from one-third of all S0-Sa disks to barely 10% of the latest-type spirals. Conversely, Type II profiles (truncations) increase in frequency with Hubble type, from only {approx}25% of S0 galaxies to {approx}80% of Sd-Sm spirals. Overall, the fractions of Type I, II, and III profiles for all disk galaxies (Hubble types S0-Sm) are 21%, 50%, and 38%, respectively; this includes galaxies ({approx}8% of the total) with composite Type II+III profiles (counted twice). Finally, we note the presence of bars in 10 galaxies previously classified (optically) as 'unbarred'. This suggests that {approx}20% of optically unbarred galaxies are actually barred; the bars in such cases can be weak, obscured by dust, or so large as to be mistaken for the main disk of the galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

DISCOVERY OF TWO VERY WIDE BINARIES WITH ULTRACOOL COMPANIONS AND A NEW BROWN DWARF AT THE L/T TRANSITION

We present the discovery and spectroscopic follow-up of a nearby late-type L dwarf (2M0614+3950), and two extremely wide very low mass binary systems (2M0525-7425AB and 2M1348-1344AB), resulting from our search for common proper motion pairs containing ultracool components in the Two Micron All Sky Survey and the Wide-field Infrared Survey Explorer catalogs. The near-infrared spectrum of 2M0614+3950 indicates a spectral type L9 {+-} 1 object residing at a distance of 26.0 {+-} 1.8 pc. The optical spectrum of 2M0525-7425A reveals an M3.0 {+-} 0.5 dwarf primary, accompanied by a secondary previously classified as L2. The system has an angular separation of {approx}44'', equivalent to {approx}2000 AU at a distance of 46.0 {+-} 3.0 pc. Using optical and infrared spectra, respectively, we classify the components of 2M1348-1344AB as M4.5 {+-} 0.5 and T5.5 {+-} 1. The angular separation of {approx}68'' is equivalent to {approx}1400 AU at a distance of 20.7 {+-} 1.4 pc. 2M1348-1344AB is one of only six very wide (separation >1000 AU) systems containing late T dwarfs known to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

THE SPITZER SPACE TELESCOPE SURVEY OF THE ORION A AND B MOLECULAR CLOUDS. I. A CENSUS OF DUSTY YOUNG STELLAR OBJECTS AND A STUDY OF THEIR MID-INFRARED VARIABILITY

We present a survey of the Orion A and B molecular clouds undertaken with the IRAC and MIPS instruments on board Spitzer. In total, five distinct fields were mapped, covering 9 deg{sup 2} in five mid-IR bands spanning 3-24 {mu}m. The survey includes the Orion Nebula Cluster, the Lynds 1641, 1630, and 1622 dark clouds, and the NGC 2023, 2024, 2068, and 2071 nebulae. These data are merged with the Two Micron All Sky Survey point source catalog to generate a catalog of eight-band photometry. We identify 3479 dusty young stellar objects (YSOs) in the Orion molecular clouds by searching for point sources with mid-IR colors indicative of reprocessed light from dusty disks or infalling envelopes. The YSOs are subsequently classified on the basis of their mid-IR colors and their spatial distributions are presented. We classify 2991 of the YSOs as pre-main-sequence stars with disks and 488 as likely protostars. Most of the sources were observed with IRAC in two to three epochs over six months; we search for variability between the epochs by looking for correlated variability in the 3.6 and 4.5 {mu}m bands. We find that 50% of the dusty YSOs show variability. The variations are typically small ({approx}0.2 mag) with the protostars showing a higher incidence of variability and larger variations. The observed correlations between the 3.6, 4.5, 5.8, and 8 {mu}m variability suggests that we are observing variations in the heating of the inner disk due to changes in the accretion luminosity or rotating accretion hot spots.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

AKARI INFRARED CAMERA SURVEY OF THE LARGE MAGELLANIC CLOUD. II. THE NEAR-INFRARED SPECTROSCOPIC CATALOG

We performed a near-infrared spectroscopic survey toward an area of {approx}10 deg{sup 2} of the Large Magellanic Cloud (LMC) with the infrared satellite AKARI. Observations were carried out as part of the AKARI Large-area Survey of the Large Magellanic Cloud (LSLMC). The slitless multi-object spectroscopic capability of the AKARI/IRC enabled us to obtain low-resolution (R {approx} 20) spectra in 2-5 {mu}m for a large number of point sources in the LMC. As a result of the survey, we extracted about 2000 infrared spectra of point sources. The data are organized as a near-infrared spectroscopic catalog. The catalog includes various infrared objects such as young stellar objects (YSOs), asymptotic giant branch (AGB) stars, supergiants, and so on. It is shown that 97% of the catalog sources have corresponding photometric data in the wavelength range from 1.2 to 11 {mu}m, and 67% of the sources also have photometric data up to 24 {mu}m. The catalog allows us to investigate near-infrared spectral features of sources by comparison with their infrared spectral energy distributions. In addition, it is estimated that about 10% of the catalog sources are observed at more than two different epochs. This enables us to study a spectroscopic variability of sources by using the present catalog. Initial results of source classifications for the LSLMC samples are presented. We classified 659 LSLMC spectra based on their near-infrared spectral features by visual inspection. As a result, it is shown that the present catalog includes 7 YSOs, 160 C-rich AGBs, 8 C-rich AGB candidates, 85 O-rich AGBs, 122 blue and yellow supergiants, 150 red super giants, and 128 unclassified sources. Distributions of the classified sources on the color-color and color-magnitude diagrams are discussed in the text. Continuous wavelength coverage and high spectroscopic sensitivity in 2-5 {mu}m can only be achieved by space observations. This is an unprecedented large-scale spectroscopic survey toward the LMC in the near-infrared. A large number of near-infrared spectral data provided by the survey possess scientific potential that can be applied to various studies. In this paper, we present the details of the spectroscopic survey and the catalog, and discuss its scientific applications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

On the construction of a new stellar classification template library for the LAMOST spectral analysis pipeline

The LAMOST spectral analysis pipeline, called the 1D pipeline, aims to classify and measure the spectra observed in the LAMOST survey. Through this pipeline, the observed stellar spectra are classified into different subclasses by matching with template spectra. Consequently, the performance of the stellar classification greatly depends on the quality of the template spectra. In this paper, we construct a new LAMOST stellar spectral classification template library, which is supposed to improve the precision and credibility of the present LAMOST stellar classification. About one million spectra are selected from LAMOST Data Release One to construct the new stellar templates, and they are gathered in 233 groups by two criteria: (1) pseudo g – r colors obtained by convolving the LAMOST spectra with the Sloan Digital Sky Survey ugriz filter response curve, and (2) the stellar subclass given by the LAMOST pipeline. In each group, the template spectra are constructed using three steps. (1) Outliers are excluded using the Local Outlier Probabilities algorithm, and then the principal component analysis method is applied to the remaining spectra of each group. About 5% of the one million spectra are ruled out as outliers. (2) All remaining spectra are reconstructed using the first principal components of each group. (3) The weighted average spectrum is used as the template spectrum in each group. Using the previous 3 steps, we initially obtain 216 stellar template spectra. We visually inspect all template spectra, and 29 spectra are abandoned due to low spectral quality. Furthermore, the MK classification for the remaining 187 template spectra is manually determined by comparing with 3 template libraries. Meanwhile, 10 template spectra whose subclass is difficult to determine are abandoned. Finally, we obtain a new template library containing 183 LAMOST template spectra with 61 different MK classes by combining it with the current library.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Characterizing Wolf-Rayet stars in the near- and mid-infrared

We present refined color-color selection criteria for identifying Wolf-Rayet (WR) stars using available mid-infrared (MIR) photometry from WISE in combination with near-infrared (NIR) photometry from the Two Micron All Sky Survey. Using a sample of spectrally classified objects, we find that WR stars are well distinguished from the field stellar population in the (W1 – W2) versus (J – K{sub s} ) color-color diagram, and further distinguished from other emission line objects such as planetary nebulae, Be, and cataclysmic variable stars using a combination of NIR and MIR color constraints. As proof of concept we applied the color constraints to a photometric sample in the Galactic plane, located WR star candidates, and present five new spectrally confirmed and classified WC (1) and WN (4) stars. Analysis of the 0.8-5.0 μm spectral data for a subset of known, bright WC and WN stars shows that emission lines (primarily He I) extend into the 3.0-5.0 μm spectral region, although their strength is greatly diminished compared to the 0.8-2.5 μm region. The WR population stands out relative to background field stars at NIR and MIR colors due to an excess continuum contribution, likely caused by free-free scattering in dense winds. Mean photometric properties of known WRs are presented and imply that reddened late-type WN and WC sources are easier to detect than earlier-type sources at larger Galactic radii. WISE W3 and W4 images of 10 WR stars show evidence of circumstellar shells linked to mass ejections from strong stellar winds.

79 ASTRONOMY AND ASTROPHYSICS↗