Spectral Subclasses Research Articles

The Value-added Catalog of OB Stars in LAMOST DR7

Abstract In this work, we update the catalog of OB stars based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope data release 7 and modify the OB stars’ selection criteria in spectral line indices’ space. The new catalog includes 37,778 spectra of 27,643 OB stars, of which 3827 OB stars are newly identified. The spectral subclasses of 27,643 OB stars are obtained using the automatic classification code MKCLASS. We find that the modified OB star selection criteria can better improve the completeness of late B-type stars by analyzing their spectral classification results given by MKCLASS. We also identify 3006 Be-type stars or candidates by examining the Balmer lines in their spectra and find that the frequency of our Be-type stars (10.9%) is consistent with previous results. The spatial distribution of OB stars indicates that they are mainly located in the Galactic disk. This new catalog of OB stars will provide valuable data for studying the structure and evolution of the Milky Way.

On the Origin of Fast-rotating Stars. I. Photometric Calibration and Results of AO-assisted BVRI+Hα Imaging of NGC 330 with SAMI/SOAR

Hα emission is a clear indicator of circumstellar activity in Be stars, historically employed to assess the classical Be star (CBe) population in young open clusters (YOCs). The YOC NGC 330 in the Small Magellanic Cloud exhibits a large known fraction of CBe stars and was selected for a pilot study to establish a comprehensive methodology for identifying Hα emitters in the Magellanic Clouds, encompassing the entire B-type spectral range. Using the SOAR Adaptive Module Imager (SAMI), we investigated the stellar population of NGC 330 using multiband BVRI+Hα imaging. We identified Hα emitters within the entire V-band range covered by SAMI/SOAR observations (V ≲ 22), comprising the complete B-type stellar population and offering a unique opportunity to explore the Be phenomenon across all spectral subclasses. The stellar radial distribution shows a clear bimodal pattern between the most massive (B5 or earlier) and the lower mass main-sequence objects (later than B6) within the cluster. The former is concentrated toward the cluster center (showing a dispersion of σ = 4.26 ± 0.20 pc), whereas the latter extends across larger radii (σ = 10.83 ± 0.65 pc), indicating mass stratification within NGC 330. The total fraction of emitters is 4.4% ± 0.5%, notably smaller than previous estimates from flux- or seeing-limited observations. However, a higher fraction of Hα emitters is observed among higher mass stars (32.8% ± 3.4%) than within lower mass (4.4% ± 0.9%). Consequently, the putative CBe population exhibits distinct dynamical characteristics compared to the bulk of the stellar population in NGC 330. These findings highlight the significance of the current observations in providing a complete picture of the CBe population in NGC 330.

IRAS 00450+7401 and the Mid-infrared Fade/Burst Cycle of R Coronae Borealis-type Stars

We present optical and infrared imaging and spectroscopy of the R Coronae Borealis-type (R Cor Bor) star IRAS 00450+7401. Optical spectra further confirm its classification as a cool R Cor Bor system, having a hydrogen-deficient carbon star spectral subclass of HdC5 or later. Mid-infrared spectroscopy reveals the typical ∼8 μm “hump” seen in other R Cor Bor stars and no other features. A modern-epoch spectral energy distribution shows bright emission from hot dust having T dust > 600 K. Historical infrared data reveal generally cooler dust color temperatures combined with long-term fading trends, but provide no discernible correlation between flux level and temperature. Investigating the most mid-infrared variable R Cor Bor stars found in IRAS, AKARI, and WISE data reveals similar fading trends, bursts that can show a factor of up to 10 change in flux density between epochs, and blackbody-fit dust color temperatures that span 400–1300 K. While some R Cor Bor stars such as IRAS 00450+7401 appear to undergo fade/burst cycles in the mid-infrared, significant gaps in temporal coverage prevent conclusively identifying any preferred timescale for their mid-infrared variability and circumstellar dust temperature changes.

Blackbody Temperature of 200+ Stellar Flares Observed with the CoRoT Satellite

We estimated blackbody temperature for 209 flares observed at 69 F–K stars, significantly increasing the number of flare temperature determinations. We used the Blue and Red channels obtained by the 27 cm telescope of the CoRoT satellite at high cadence and long duration. The wavelength limits of the channels were estimated using spectra from the Pickles library for the spectral type and luminosity class of each star, provided by the Exodat Database. The temperatures were obtained from the flare energy Blue-to-Red ratio, using the flare equivalent duration and stellar flux in both channels. The expected value of the analyzed flares is equal to 6400 K with a standard deviation of 2800 K, where the mean stellar spectral type, weighted by the number of flares in each spectral subclass, is equal to G6. Contrary to our results, a stellar white-light flare is often assumed to emit as a blackbody with a temperature of 9000 or 10,000 K. Our estimates agree, however, with values obtained for solar flares. The GAIA G-band transmissivity is comparable to that of the CoRoT White channel, which allows us to calibrate the flares to the Gaia photometric system. The energy in the G band of the analyzed flares varies between 1032 and 1037 erg, and the flare area ranges from 30 μsh to 3 sh (solar hemisphere). The energy release per area in a flare is proportional to , at least up to 10,000 K.

THE OVERCONTACT BINARY V563 LYRAE

Radial velocity (RV) and light curve (LC) data for the overcontact binary V563 Lyr have been obtained and analysed with the 2003 version of the WilsonDevinney (WD) code with results K1 = 147.4 ± 2.1 km/s, K2 = 247.2 ± 1.1 km/s, RVγ = 22.5 ± 1.5 km/s, qsp = 0.596 ± 0.008, M1 = 2.49(4)M☉, M2 = 1.45(4)M☉, R1 = 2.23(2)R☉, R2 = 1.81(2)R☉. A third component was identified, with radial velocity RV3 = 18.8 ± 6.7 km/s. Inserting the derived parameters of the eclipsing pair into a Log (L)-Log (Teff) plot for each star using data from Yakut and Eggleton (2005) suggested that both stars are over-luminous and evolved to, and perhaps past, the terminal age main sequence (TAMS). The companion (star 3) has a spectral type of A0 ± 1 spectral subclass but cannot be gravitationally bound to the eclipsing pair, as its flux would dominate that of the pair, which was not observed. The companion must lie at some other distance.

Increasing spin of a B-Star during the common envelope stage

Context. In its evolution, the MWC 656 binary system, consisting of a Be star and a black hole, has gone through the common envelope stage. The O and B stars of the early spectral subclasses can be characterised by lower rotational velocities and increased binary frequency. The B star in the MWC 656 system may have undergone rapid rotation during the common envelope stage. Aims. We study the change in the B star’s state of rotation due to an increase in its angular momentum during the common envelope stage and immediately afterwards. Methods. We performed one-dimensional numerical calculations of angular momentum transport in the interior of a slightly evolved star on the main sequence with a mass of 16 M⊙. Meridional circulation and shear turbulence are taken into account. Results. Due to the arrival of angular momentum through the star’s surface, the velocity of the meridional circulation increases by more than six orders of magnitude. Meridional circulation is the main mechanism for the transport of angular momentum into the star’s deep interior. The acquired angular momentum enters the convective core during the common envelope stage if the angular velocity of the accreted matter exceeds half the Kepler value. Conclusions. The star acquires a rotation typical of classical Be stars if the angular velocity of accreted matter rotation is close to the maximum possible value during the common envelope stage (∼80% of the Kepler value).

The overcontact binary RZ Tauri

The first reliable radial velocity curves for RZ Tau have been obtained. Combined with new light curve (LC) data, simultaneous Roche-based light curve analyses have been carried out using the 2003 version of the Wilson-Devinney code. New values for the masses and radii have been obtained: M1 = 1.53 (4) Mʘ, M2 = 0.57 (2) Mʘ, and R1 = 1.50 (4) Rʘ, R2 = 0.99 (2) Rʘ. Although there is a multitude of spectral classifications listed in the literature, the classification of F0 from Struve et al. (1950) is shown to be the most reliable. On the basis of the latter, the most likely luminosities are L1 = 4.97 (44) Lʘ and L2 = 2.11 (19) Lʘ. Additional modelling was carried out using the same RV data, but with LC data (binned and unbinned) from Binnendijk (1963); the results were substantially the same except that they favoured slightly later spectral sub-classes.

Spectral Classification of B Stars: The Empirical Sequence Using SDSS-IV/APOGEE Near-IR Data

Abstract We present a semi-empirical spectral classification scheme for normal B-type stars using near-infrared (NIR) spectra (1.5–1.7 μm) from the Sloan Digital Sky Survey Apache Point Observatory Galaxy Evolution Experiment (APOGEE2)-N data release 14 (DR14) database. The main motivation for working with B-type stars is their importance in the evolution of young stellar clusters; however, we also take advantage of having a numerous sample (316 stars) of B-type star candidates in APOGEE2-N, for which we also have optical (3600–9100 Å) counterparts from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey. By first obtaining an accurate spectral classification of the sources using the LAMOST DR3 spectra and the canonical spectral classification scheme, we found a linear relation between optical spectral types and the equivalent widths of the hydrogen lines of the Brackett series in the APOGEE2-N NIR spectra. This relation extends smoothly from a similar relation for O and early B stars found by Roman-Lopes et al. This way, we obtain a catalog of B-type sources with features in both the optical and NIR and a classification scheme refined down to one spectral subclass.

Classification for Unrecognized Spectra in LAMOST DR6 Using Generalization of Convolutional Neural Networks

Commonly used template classification for celestial spectra always fails dealing with low signal-to-noise ratio (S/N) spectra, which are very numerous in spectroscopic surveys. In the sixth data release of Large sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST DR6 V1), more than 0.7 million bad quality data were refused to classify by LAMOST pipeline and archived as “UNKNOWN.” To recognize as many objects with low S/N spectra as possible in the “UNKNOWN” data set, one-dimensional convolutional neural network (CNN) based classifier was adapted from the widely used two-dimensional CNN. In this work, two CNN based classifier were applied, a classifier for distinguishing galaxy, QSO and star, and a classifier for discriminating subtypes of stars. To solve the problem caused by imbalanced training samples among different classes for the stellar classifier, a semi supervised learning algorithm by two CNNs and Spectral Generative Adversarial Network (SGAN) was introduced to produce artificial spectra for the minority O type. The SGAN solution is better than over-sampling in solving overfitting caused by imbalanced training set. The trained CNN classifiers were applied to classify “UNKNOWN” spectra into candidates of galaxies, QSOs, and stars. and further classify star candidates into spectral subclasses of O to M. Each spectra can be recognized to a spectral type with a probability by CNN algorithm, and 101,082 stellar spectra were remained with the probability larger than 99%, making up a supplemental star catalog of LAMOST DR6, which includes 294 O, 2 850 B, 269 A, 6 431 F, 626 G, 60 527 K, and 30 085 M types. To verify the catalog, the distances to corresponding templates from recognized spectra in each class were also checked comparing with known spectra. In addition, 200 O type stars were manually confirmed from 294 automatically identified O type stars in the catalog, because O type spectra have weak features and easily to be confused with no signal spectra. The classification result as a part of this work are available at http://paperdata.china-vo.org/Classification_SGAN/result.zip.

Decomposability of Krein space operators

In this paper, we review some properties in the local spectral theory and various subclasses of decomposable operators. We prove that every Krein space selfadjoint operator having property (?) is decomposable, and clarify the relation between decomposability and property (?) for J-selfadjoint operators. We prove the equivalence of these properties for J-selfadjoint operators T and T* by using their local spectra and local spectral subspaces.

Application of convolutional neural networks for stellar spectral classification

ABSTRACTDue to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification and regression problems like the determination of stellar atmospheric parameters Teff, $\rm {\log g}$, and [Fe/H]. We propose an automated approach for the classification of stellar spectra in the optical region using convolutional neural networks (CNNs). Traditional machine learning (ML) methods with ‘shallow’ architecture (usually up to two hidden layers) have been trained for these purposes in the past. However, deep learning methods with a larger number of hidden layers allow the use of finer details in the spectrum which results in improved accuracy and better generalization. Studying finer spectral signatures also enables us to determine accurate differential stellar parameters and find rare objects. We examine various machine and deep learning algorithms like artificial neural networks, Random Forest, and CNN to classify stellar spectra using the Jacoby Atlas, ELODIE, and MILES spectral libraries as training samples. We test the performance of the trained networks on the Indo-U.S. Library of Coudé Feed Stellar Spectra (CFLIB). We show that using CNNs, we are able to lower the error up to 1.23 spectral subclasses as compared to that of two subclasses achieved in the past studies with ML approach. We further apply the trained model to classify stellar spectra retrieved from the SDSS data base with SNR > 20.

Reducing the impacts of intra-class spectral variability on the accuracy of soft classification and super-resolution mapping of shoreline

ABSTRACTThe main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spectral variation, but the effect could be reduced through the use of spectral sub-classes. The latter is illustrated in mapping the shoreline at a sub-pixel scale from Landsat ETM+ data. Reducing the degree of intra-class spectral variation increased the accuracy of soft classification, with the correlation between predicted and actual class coverage rising from 0.87 to 0.94, and super-resolution mapping, with the RMSE in shoreline location decreasing from 41.13 m to 35.22 m.

Is Partial Mixing of Matter in the Components of Binary Systems Possible?

Data on the absolute elements of the components of binary systems which are in the hydrogen burning phase and belong to different early spectral B subclasses are analyzed using models of stars with partial mixing of matter from the radiative envelope and convective core. Partial mixing favors a larger increase in the luminosity and a smaller increase in the size of a star as it evolves along the main sequence (MS). The available data on the masses, sizes, and luminosities of the components of binary systems support the possibility of partial mixing in their interiors similar to the mixing that occurs in isolated MS B-stars in the same spectral subclasses. The mechanism of partial mixing can serve as an alternative or a supplement to the mechanism of additional mixing at the boundary of the convective core in analyses of the observed characteristics of binary systems, in particular the enhanced luminosity of the optical components of x-ray binaries, and this requires further study. The existing data on the absolute elements of the components are insufficient for imposing strict limits on the amount of partial mixing and identifying quantitative differences between mixing in the components of binary systems and isolated stars, if this occurs.

Production of a Large-Area Individual Tree Species Map for Forest Inventory in a Complex Forest Setting and Lessons Learned

A large-area trial of automated individual tree crown isolation and species classification on a 228-km2 site in central-eastern Ontario using 40 cm multispectral imagery gave insight into the complications and effectiveness of such approaches when applied to a complex mixed forest setting. Tree isolation was reasonably effective with few omissions and 77% of automated tree isolations (isols) were considered a good correspondence to ground truth delineations. There were issues with grouping several trees within 1 delineation, poor isols at the edges of stands and minor splitting (multiple isols per tree). Spectral characteristics of 18 species indicated considerable variability within species and overlap of signatures. To circumvent this, several spectral subclasses were created for certain species and the site was divided into broad and localized zones, which for ecological reasons had a reduced number of species. Mapping accuracy and classification accuracy of manually delineated trees for a main classification zone of 14 species, both typically ranged from 40% to 85%. For a simpler zone of 8 species, manual tree class accuracy averaged 76%. Possible improvements are discussed. Regardless of sophistication of techniques used, production of individual tree species maps in complex forests will require judicious use of human judgment and intervention.

Primeval very low-mass stars and brown dwarfs – I. Six new L subdwarfs, classification and atmospheric properties

We have conducted a search for L subdwarf candidates within the photometric catalogues of the UKIRT Infrared Deep Sky Survey and Sloan Digital Sky Survey. Six of our candidates are confirmed as L subdwarfs spectroscopically at optical and/or near infrared wavelengths. We also present new optical spectra of three previously known L subdwarfs (WISEA J001450.17-083823.4, 2MASS J00412179+3547133, ULAS J124425.75+102439.3). We examined the spectral types and metallicity subclasses classification of known L subdwarfs. We summarised the spectroscopic properties of L subdwarfs with different spectral types and subclasses. We classify these new L subdwarfs by comparing their spectra to known L subdwarfs and L dwarf standards. We estimate temperatures and metallicities of 22 late type M and L subdwarfs by comparing their spectra to BT-Settl models. We find that L subdwarfs have temperatures between 1500 K and 2700 K, which are higher than similarly-typed L dwarfs by around 100-400 K depending on different subclasses and subtypes. We constrained the metallicity ranges of subclasses of M, L and T subdwarfs. We also discussed the spectral type and absolute magnitude relationships for L and T subdwarfs.

Adaptive stellar spectral subclass classification based on Bayesian SVMs

Stellar spectral classification is one of the most fundamental tasks in survey astronomy. Many automated classification methods have been applied to spectral data. However, their main limitation is that the model parameters must be tuned repeatedly to deal with different data sets. In this paper, we utilize the Bayesian support vector machines (BSVM) to classify the spectral subclass data. Based on Gibbs sampling, BSVM can infer all model parameters adaptively according to different data sets, which allows us to circumvent the time-consuming cross validation for penalty parameter. We explored different normalization methods for stellar spectral data, and the best one has been suggested in this study. Finally, experimental results on several stellar spectral subclass classification problems show that the BSVM model not only possesses good adaptability but also provides better prediction performance than traditional methods.

Stellar classification from single-band imaging using machine learning

Information on the spectral types of stars is of great interest in view of the exploitation of space-based imaging surveys. In this article, we investigate the classification of stars into spectral types using only the shape of their diffraction pattern in a single broad-band image. We propose a supervised machine learning approach to this endeavour, based on principal component analysis (PCA) for dimensionality reduction, followed by artificial neural networks (ANNs) estimating the spectral type. Our analysis is performed with image simulations mimicking the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) in the F606W and F814W bands, as well as the Euclid VIS imager. We first demonstrate this classification in a simple context, assuming perfect knowledge of the point spread function (PSF) model and the possibility of accurately generating mock training data for the machine learning. We then analyse its performance in a fully data-driven situation, in which the training would be performed with a limited subset of bright stars from a survey, and an unknown PSF with spatial variations across the detector. We use simulations of main-sequence stars with flat distributions in spectral type and in signal-to-noise ratio, and classify these stars into 13 spectral subclasses, from O5 to M5. Under these conditions, the algorithm achieves a high success rate both for Euclid and HST images, with typical errors of half a spectral class. Although more detailed simulations would be needed to assess the performance of the algorithm on a specific survey, this shows that stellar classification from single-band images is well possible.

Stellar Spectral Classification with Locality Preserving Projections and Support Vector Machine

With the help of computer tools and algorithms, automatic stellar spectral classification has become an area of current interest. The process of stellar spectral classification mainly includes two steps: dimension reduction and classification. As a popular dimensionality reduction technique, Principal Component Analysis (PCA) is widely used in stellar spectra classification. Another dimensionality reduction technique, Locality Preserving Projections (LPP) has not been widely used in astronomy. The advantage of LPP is that it can preserve the local structure of the data after dimensionality reduction. In view of this, we investigate how to apply LPP+SVM in classifying the stellar spectral subclasses. In the comparative experiment, the performance of LPP is compared with PCA. The stellar spectral classification process is composed of the following steps. Firstly, PCA and LPP are respectively applied to reduce the dimension of spectra data. Then, Support Vector Machine (SVM) is used to classify the 4 subclasses of K-type and 3 subclasses of F-type spectra from Sloan Digital Sky Survey (SDSS). Lastly, the performance of LPP+SVM is compared with that of PCA+SVM in stellar spectral classification, and we found that LPP does better than PCA.

Anomalous Spectral Types and Intrinsic Colors of Young Stars

AbstractWe highlight differences in spectral types and intrinsic colors observed in pre-main sequence (pre-MS) stars. Spectral types of pre-MS stars are wavelength-dependent, with near-infrared spectra being 3-5 spectral sub-classes later than the spectral types determined from optical spectra. In addition, the intrinsic colors of young stars differ from that of main-sequence stars at a given spectral type. We caution observers to adopt optical spectral types over near-infrared types, since Hertzsprung-Russell (H-R) diagram positions derived from optical spectral types provide consistency between dynamical masses and theoretical evolutionary tracks. We also urge observers to deredden pre-MS stars with tabulations of intrinsic colors specifically constructed for young stars, since their unreddened colors differ from that of main sequence dwarfs. Otherwise, V-band extinctions as much as ~0.6 mag erroneously higher than the true extinction may result, which would introduce systematic errors in the H-R diagram positions and thus bias the inferred ages.

Stellar Spectral Subclasses Classification Based on Fisher Criterion and Manifold Learning

Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) are two widely used feature extraction methods. The advantage of LDA is that it takes the global structure of the data into consideration by maximizing the ratio of the between-class scatter to the within-class scatter. LPP tries to preserve the local structure of the data. The global and local structure of the data are very important in dealing with feature extraction problems but it is regretful that the above two methods cannot fully utilize all the information. In view of this, Modified Discriminant Analysis based on Fisher Criterion and Manifold Learning (MDA) is proposed in this paper. Two important concepts are introduced: Manifold based Within-Class Scatter (MWCS) and Manifold based Between-Class Scatter (MBCS). MDA aims to find an optimal projection matrix by maximizing the ratio of MBCS to MWCS based on Fisher criterion. In this paper, we will investigate the performance of MDA in the stellar spectral subclasses classification. We first reduce the dimension of spectra data by PCA (Principal Component Analysis), LDA, LPP, and MDA, respectively. Then we apply support vector machine (SVM) to classify the four subclasses of K-type spectra, three subclasses of F-type spectra, and three subclasses of G-type spectra from Sloan Digital Sky Survey (SDSS). The comparative experiment results verify MDA can preserve both the local and global structure of the data when embed the original data into much lower dimensional space.