Class Of Stars Research Articles

Classifying Single Stars and Spectroscopic Binaries Using Optical Stellar Templates

Abstract Stellar spectral classification is a fundamental tool of modern astronomy, providing insight into physical characteristics such as effective temperature, surface gravity, and metallicity. Accurate and fast spectral typing is an integral need for large all-sky spectroscopic surveys like the Sloan Digital Sky Survey (SDSS) and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope. Here, we present the next version of PyHammer, a stellar spectral classification software that uses optical spectral templates and spectral line index measurements. PyHammer v2.0 extends the classification power to include dwarf carbon stars, DA white dwarf stars, and also double-lined spectroscopic binaries (SB2). This release also includes a new empirical library of luminosity-normalized spectra that can be used to flux calibrate observed spectra or to create synthetic SB2 spectra. We have generated physically reasonable SB2 combinations as templates, adding the ability to spectrally type SB2s to PyHammer. We test classification success rates on SB2 spectra, generated from the SDSS, across a wide range of spectral types and signal-to-noise ratios. Within the defined range of pairings described, more than 95% of SB2s are correctly classified.

The massive binary system 9 Sgr revisited: New insights into disentangling methods

AbstractDisentangling techniques are often needed to obtain the spectra of the individual components of binary or multiple systems. A thorough analysis of the shift‐and‐add algorithm of Marchenko et al, PASP, 1999;110:1416 reveals that, in many cases, the line fluxes are poorly reproduced, and spurious wings appear. The causes of these discrepancies are discussed, and a new disentangling package, QER20, is presented, which significantly reduces these errors and vastly increases the performance. When applied to the massive binary 9 Sgr, our new code yields line fluxes that are notably different from those previously published and lead us to revise the spectral classification to slightly earlier subtypes: O 3 V ((f +)) for the primary and O 5 V ((f)) for the secondary. We show that, with the MME98 algorithm, the classification of massive stars in binaries can be off by several subtypes, while there are no such errors when the QER20 package is used.

Image-based Classification of Variable Stars: First Results from Optical Gravitational Lensing Experiment Data

Abstract Recently, machine learning methods have presented a viable solution for the automated classification of image-based data in various research fields and business applications. Scientists require a fast and reliable solution in order to handle increasingly large amounts of astronomical data. However, so far astronomers have been mainly classifying variable starlight curves based on various pre-computed statistics and light curve parameters. In this work we use an image-based Convolutional Neural Network to classify the different types of variable stars. We use images of phase-folded light curves from the Optical Gravitational Lensing Experiment (OGLE)-III survey for training, validating, and testing, and use OGLE-IV survey as an independent data set for testing. After the training phase, our neural network was able to classify the different types between 80% and 99%, and 77%–98%, accuracy for OGLE-III and OGLE-IV, respectively.

A Blended Artificial Intelligence Approach for Spectral Classification of Stars in Massive Astronomical Surveys.

This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the Morgan–Keenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of today’s major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.

Application of Artificial Neural Networks for the Automatic Spectral Classification

Classification in astrophysics is a fundamental process, especially when it is necessary to understand several aspects of the evolution and distribution of the objects. Over an astronomical image, we need to discern between stars and galaxies and to determine the morphological type for each galaxy. The spectral classification of stars provides important information about stellar physical parameters like temperature and allows us to determine their distance; with this information, it is possible to evaluate other parameters like their physical size and the real 3D distribution of each type of objects. In this work, we present the application of two Artificial Intelligence (AI) techniques for the automatic spectral classification of stellar spectra obtained from the first data release of LAMOST and also to the more recent release (DR5). Two types of Artificial Neural Networks were selected: a feedforward neural network trained according to the Levenberg–Marquardt Optimization Algorithm (LMA) and a Generalized Regression Neural Network (GRNN). During the study, we used four datasets: the first was obtained from the LAMOST first data release and consisted of 50731 spectra with signal-to-noise ratio above 20, the second dataset was obtained from the Indo-US spectral database (1273 spectra), the third one (the STELIB spectral database) was used as an independent test dataset, and the fourth dataset was obtained from LAMOST DR5 and consisted of 17990 stellar spectra with signal-to-noise ratio above 20 also. The results in the first part of the work, when the autoconsistency of the DR1 data was probed, showed some problems in the spectral classification available in LAMOST DR1. In order to accomplish a better classification, we made a two-step process: first the LAMOST and STELIB datasets were classified by the two IA techniques trained with the entire Indo-US dataset. The resulted classification allows us to discriminate at least three groups: the first group contained O and B type stars, whereas the second contained A, F, and G type stars, and finally, the third group contained K and M type stars. The second step consisted of a refinement of the classification, but this time for every group, the most relevant indices were selected. We compared the accuracy reached by the two techniques when they are trained and tested using LAMOST spectra and their published classification and the resultant classifications obtained with the ANNs trained with the Indo-US dataset and applied over the STELIB and LAMOST spectra. Finally, in the first part, we compared the LAMOST DR1 classification with the classification obtained by the application of the NNs GRNNs and LMA trained with the Indo-US dataset. In the second part of the paper, we analyze a set of 17990 stellar spectra from LAMOST DR5 and the very significant improvement in the spectral classification available in DR5 database was verified. For this, we trained ANNs using the k-fold cross-validation technique with k = 5.

Classification of star/galaxy/QSO and star spectral types from LAMOST data release 5 with machine learning approaches

We use 343,747 sources from LAMOST DR5 to do star/galaxy/QSO classification with machine learning approaches. Specifically, the 312,767 spectral labeled stars (G, K, M, F, A) are used to do star classification. The photometry of u, g, r, i, z, J, and H are used as machine learning features. For star/galaxy/QSO classification, the k nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF) and support vector machine (SVM) perform well. For star classification, the accuracy of RF and SVM classification are higher than the accuracy of KNN and DT. The area under receiver operating characteristic curves of the four models are approaching to 1. The accuracy, precision, recall, f_score, Matthews correlation coefficient are always greater than 0.5. The four models perform all right in predicting the nature of sources and the star label.

Imbalance learning for variable star classification

ABSTRACT The accurate automated classification of variable stars into their respective subtypes is difficult. Machine learning–based solutions often fall foul of the imbalanced learning problem, which causes poor generalization performance in practice, especially on rare variable star subtypes. In previous work, we attempted to overcome such deficiencies via the development of a hierarchical machine learning classifier. This ‘algorithm-level’ approach to tackling imbalance yielded promising results on Catalina Real-Time Survey (CRTS) data, outperforming the binary and multiclass classification schemes previously applied in this area. In this work, we attempt to further improve hierarchical classification performance by applying ‘data-level’ approaches to directly augment the training data so that they better describe underrepresented classes. We apply and report results for three data augmentation methods in particular: Randomly Augmented Sampled Light curves from magnitude Error (RASLE), augmenting light curves with Gaussian Process modelling (GpFit) and the Synthetic Minority Oversampling Technique (SMOTE). When combining the ‘algorithm-level’ (i.e. the hierarchical scheme) together with the ‘data-level’ approach, we further improve variable star classification accuracy by 1–4 per cent. We found that a higher classification rate is obtained when using GpFit in the hierarchical model. Further improvement of the metric scores requires a better standard set of correctly identified variable stars, and perhaps enhanced features are needed.

Stellar Feedback and Resolved Stellar IFU Spectroscopy in the Nearby Spiral Galaxy NGC 300

Abstract We present MUSE integral field unit (IFU) observations of five individual H ii regions in two giant star-forming complexes in the low-metallicity, nearby dwarf spiral galaxy NGC 300. In combination with high spatial resolution Hubble Space Telescope photometry, we demonstrate the extraction of stellar spectra and classification of individual stars from ground-based IFU data at the distance of 2 Mpc. For the two star-forming complexes, in which no O-type stars had previously been identified, we find a total of 13 newly identified O-type stars and 4 Wolf-Rayet stars (two already-known sources and two Wolf-Rayet star candidates that this work has now confirmed). We use the derived massive stellar content to analyze the impact of stellar feedback on the H ii regions. As already found for H ii regions in the Magellanic Clouds, the dynamics of the analyzed NGC 300 H ii regions are dominated by a combination of the pressure of the ionized gas and stellar winds. Moreover, we analyze the relation between the star formation rate and the pressure of the ionized gas as derived from small (<100 pc) scales, both quantities being systematically overestimated when derived on galactic scales. With the wealth of upcoming IFU instruments and programs, this study serves as a pathfinder for the systematic investigation of resolved stellar feedback in nearby galaxies, delivering the necessary analysis tools to enable massive stellar content and feedback studies sampling an unprecedented range of H ii region properties across entire galaxies in the nearby universe.

Open Cluster IC 1369 and Its Vicinity: Multicolor Photometry and Gaia DR2 Astrometry

Abstract The open cluster IC 1369, located in Cygnus close to the galactic equator at the 89.°6 longitude, and its vicinity are investigated by applying two-dimensional photometric classification of stars down to V = 19 mag measured in the Vilnius seven-color photometric system. Employing the Gaia DR2 coordinates, parallaxes, and proper motions, 143 cluster members are found having cluster membership probabilities ≥0.75. The hottest stars of the evolved main sequence are of spectral class B9. Distances to the cluster, determined by two methods based on the Gaia parallaxes, are 3280 and 3370 pc with an uncertainty of 400–500 pc. Employing the physical HR diagram with the coordinates versus and the Padova isochrones, the cluster age is found to be between 300 and 350 Myr. Among the cluster members we identified six red giants of spectral classes G5–G8 and luminosities II–III, 17 Am stars, one Ap star, and one B5 blue straggler. The extinction shows a steep rise up to ∼2.0 mag at 300–700 pc, caused by the extensions of the dust cloud LDN 970 and the Great Cygnus Rift, and up to ∼2.5 mag at the edge of the Perseus arm at 3.0 kpc. The extinction is almost constant between 0.9 and 2.5 kpc from the Sun. The average extinction of the cluster members is 2.54 mag. A systematic error of color excesses found in the Argonaut calculator results is discussed.

Scalable end-to-end recurrent neural network for variable star classification

ABSTRACT During the last decade, considerable effort has been made to perform automatic classification of variable stars using machine-learning techniques. Traditionally, light curves are represented as a vector of descriptors or features used as input for many algorithms. Some features are computationally expensive, cannot be updated quickly and hence for large data sets such as the LSST cannot be applied. Previous work has been done to develop alternative unsupervised feature extraction algorithms for light curves, but the cost of doing so still remains high. In this work, we propose an end-to-end algorithm that automatically learns the representation of light curves that allows an accurate automatic classification. We study a series of deep learning architectures based on recurrent neural networks and test them in automated classification scenarios. Our method uses minimal data pre-processing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive data sets. We transform each light curve into an input matrix representation whose elements are the differences in time and magnitude, and the outputs are classification probabilities. We test our method in three surveys: OGLE-III, Gaia, and WISE. We obtain accuracies of about $95{{\ \rm per\ cent}}$ in the main classes and $75{{\ \rm per\ cent}}$ in the majority of subclasses. We compare our results with the Random Forest classifier and obtain competitive accuracies while being faster and scalable. The analysis shows that the computational complexity of our approach grows up linearly with the light-curve size, while the traditional approach cost grows as Nlog (N).

Rare Object Search From Low-S/N Stellar Spectra in SDSS

Rare objects such as white dwarf+main sequence (WDMS) and cataclysmic variables (CVs) are very important for studying the evolution of the galaxy and the universe. The large amount of spectra obtained by the large sky surveys such as the Sloan Digital Sky Survey (SDSS) are rich sources of these rare objects. However, a considerable fraction of these spectra are low-S/N spectra. These low-S/N spectra contain similar useful information as the high-S/N spectra, and making better use of these spectra can significantly improve the chance of finding rare objects. Nevertheless, little research has been done on them. In this study we propose a novel method based on the combination of PCA (Principal Components Analysis) and CFSFDP (Clustering by Fast Search and Find of Density Peak) to search for rare objects from low-S/N spectra. The PCA first extracts principal components from high-S/N spectra to generate general feature spectra and reconstructs low-S/N stellar spectra with these general feature spectra. Then the CFSFDP calculates the Local Density ρ and the Distance δ of the reconstructed spectra, and select the outliers through the decision graph quickly and accurately. We first apply our method to spectra in SDSS stellar classification template library with adding white gaussian noise to search for rare objects (carbon stars, carbon white dwarfs, carbon_lines, white dwarfs and white dwarfs magnetic). Then we apply our method to observed spectra with different low-S/Ns from SDSS and compared with Lick-index+K-means and Support Vector Machines (SVM). The experimental results show that our method has a higher efficiency compared to other methods.

Towards Five Star Classification Shopping Centre in Malaysia: An Exploratory Research on Categories of Shopping Centre

Shopping Centre is the most wonderful spots nowadays with their appealing shops and a wide assortment individual spend their free occasions to unwind, shop and make the most of their occasions. Be that as it may, it shows up the powers driving retail development everywhere throughout the world have withdrawn from real customers request. A shopping centre is a group of retail stores. With the harried number development of shopping centres in Malaysia, the competition among of those commercial retailers is seen as crucial. The shopping centre springing up around us like mushrooms after the rain. The changes format of urbanisation, globalisation, new technologies and innovations are leading the entire retail market makes our shopping centres management more challenging. Because shopping centres comprise of many types with different features to serve its function, the measurement of the quality of shopping centres becomes more complex. One of the ways to facilitate the measurement of quality of different types of shopping centres is by classifying the shopping centres. Therefore, this paper looks on the category the shopping centre in Malaysia based on physical criteria, rental rate, tenant’s revenues and retails customers patronage satisfaction. The criteria of shopping centre acquired through literatures can be filled in as an orientation in making a categorised of shopping centres in Malaysia. Interview with government and private sectors involved in shopping centres has been conducted to find out the suitable category of shopping centre in Malaysia. This is an initial study toward the classification of shopping centres in Malaysia. Noted that the classification is very important for investment purpose and as a guideline, therefore, shopping centre must get a better rating tool for the developers and investors for their investment decision in the future.

Streaming classification of variable stars

ABSTRACTIn the last years, automatic classification of variable stars has received substantial attention. Using machine learning techniques for this task has proven to be quite useful. Typically, machine learning classifiers used for this task require to have a fixed training set, and the training process is performed offline. Upcoming surveys such as the Large Synoptic Survey Telescope will generate new observations daily, where an automatic classification system able to create alerts online will be mandatory. A system with those characteristics must be able to update itself incrementally. Unfortunately, after training, most machine learning classifiers do not support the inclusion of new observations in light curves, they need to re-train from scratch. Naively re-training from scratch is not an option in streaming settings, mainly because of the expensive pre-processing routines required to obtain a vector representation of light curves (features) each time we include new observations. In this work, we propose a streaming probabilistic classification model; it uses a set of newly designed features that work incrementally. With this model, we can have a machine learning classifier that updates itself in real time with new observations. To test our approach, we simulate a streaming scenario with light curves from Convention, Rotation and planetary Transits (CoRoT), Orbital Gravitational Lensing Experiment (OGLE), and Massive Compact Halo Object (MACHO) catalogues. Results show that our model achieves high classification performance, staying an order of magnitude faster than traditional classification approaches.

Searching for Hot Subdwarf Stars from the LAMOST Spectra. III. Classification of Hot Subdwarf Stars in the Fourth Data Release of LAMOST Using a Deep Learning Method

Abstract Hot subdwarf stars are core He burning stars located at the blue end of the horizontal branch, which is also known as the extreme horizontal branch. The spectra of hot subdwarf stars can provide detailed information on stellar atmospheric parameters, such as the effective temperature, gravity, and abundances of helium, which can help clarify the astrophysical and statistical properties of hot subdwarf stars. These properties provide important constraints on the theoretical models of stars. The identification of hot subdwarf stars from the spectral data obtained by the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) can significantly increase the sample size and help us to better understand the nature of hot subdwarf stars. In this study, we propose a new method to select hot subdwarf stars from LAMOST spectra using convolutional neural networks and a support vector machine (CNN+SVM). By applying CNN+SVM to sample data selected from LAMOST Data Release 4 we obtain an F1 score of 76.98%. A comparison with other machine-learning algorithms, such as linear discriminant analysis and k-nearest neighbors, demonstrates that an approach based on CNN+SVM obtains better results than the others. Therefore it is a method well suited to the problem of searching for hot subdwarf stars in large spectroscopic surveys. Finally, we include an extensive discussion on how we determined the optimal hyperparameters of our proposed method.

A novel approach for space debris recognition based on the full information vectors of star points

The recognition and detection of space debris has become one of significant research fields recently. Compared with natural images, effective information are very few contained in star images. In the past years, the gray values of star points and the continuity of sequential star images are utilized by numerous algorithms to carry out the recognition and detection through fusion of consecutive star images, which have been achieved good performance. However, with the rapid increase of star image data, those algorithms seem to be inadequate in recognition ability. In this paper, we propose one novel approach based on the full information vectors of star points to recognize moving targets with the machine learning method which is never utilized in space debris recognition field. Besides gray values, we further deeply excavate the characteristics of each star point in a single frame by the equal probability density curve of Gaussian distribution. The elliptical pattern characteristic vectors of star points can be input into the machine learning method for classification of static stars and moving targets in a single frame. Finally, trajectories of moving targets can be determined within 3 frames by the full information vectors. Therefore, traditional processing methods are abandoned and the proposed brand new approach redefines the recognition technical route of space debris. The experimental results demonstrate that moving targets can be successfully recognized in a single frame and the coverage rate of moving targets can reach 100%. Compared with other traditional methods, the proposed approach has better performance and more robustness.

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.

Classification of close binary stars using recurrence networks.

Close binary stars are binary stars where the component stars are close enough such that they can exchange mass and/or energy. They are subdivided into semidetached, overcontact, or ellipsoidal binary stars. A challenging problem in the context of close binary stars is their classification into these subclasses based solely on their light curves. Conventionally, this is done by observing subtle features in the light curves like the depths of adjacent minima, which is tedious when dealing with large datasets. In this work, we suggest the use of machine learning algorithms applied to quantifiers derived from recurrence networks to differentiate between classes of close binary stars. We show that overcontact binary stars occupy a region different from semidetached and ellipsoidal binary stars in a plane of characteristic path length and average clustering coefficient, computed from their recurrence networks. We use standard clustering algorithms and report that the clusters formed correspond to the standard classes with a high degree of accuracy.

Properties of green valley galaxies in relation to their selection criteria

AbstractThe distribution of galaxies has been studied to show the difference between the blue cloud and red sequence and to define the green valley region. However, there are still many open questions regarding the importance of the green valley for understanding the morphological transformation and evolution of galaxies, how galaxies change from late-type to early-type and the role of AGN in galaxy formation and evolution scenario. The work focused on studying in more details the properties of green valley galaxies by testing the six most used selection criteria, differences between them, and how they may affect the main results and conclusions. The main findings are that, by selecting the green valley galaxies using different criteria, we are selecting different types of galaxies in terms of their stellar masses, sSFR, SFR, spectroscopic classification and morphological properties, where the difference was more significant for colour criteria than for sSFR and SFR vs. M* criteria.

Rising Star Evaluation Based on Extreme Learning Machine in Geo-Social Networks

In social networks, rising stars are junior individuals who may be not so charming at first but turn out to be outstanding over time. Recently, rising star evaluation has become a popular research topic in the field of social analysis, which is helpful for decision support, cognitive computation, and other practical problems. In this paper, we study the problem of rising star evaluation in geo-social networks. Specifically, given a topic keyword Q and a time point t, we aim at evaluating the latent influence of users to find rising stars, which refer to experts who have few activities and little impact currently on the underlying geo-social network but may become influential experts in the future. To efficiently evaluate future stars, we propose a novel processing framework based on extreme learning machine (ELM) called FS-ELM. FS-ELM consists of three key components. The first component constructs features by incorporating social topology and user behavior patterns. The second component extracts supervised information by discovering topic experts of Q at time (t + Δt); that is, excluding those detected at time t, topic experts obtained at time (t + Δt) can be regarded as rising stars at time t. The third component is ELM-based future star classification that leverages ELM as a departure point to evaluate whether a user is a rising star. Our experimental studies conducted on real-world datasets show that (1) FS-ELM can effectively discover rising stars with a query topic at time t and outperform other traditional methods and (2) user social characteristics have an important impact on the rising star evaluation. This paper studies a novel problem, namely, rising star evaluation in geo-social networks. We propose an advanced processing framework based on ELM by exploiting social topology characteristics and user behavior patterns. The experimental results encouragingly demonstrate the efficiency and effectiveness of the proposed approach.

Dynamical Confirmation of a Black Hole in MAXI J1820+070

Abstract We present time-resolved 10.4 m Gran Telescopio Canarias and 4.2 m William Herschel Telescope intermediate resolution spectroscopy of the X-ray transient MAXI J1820+070 (=ASASSN-18ey) obtained during its decline to the quiescent state. Cross-correlation of the 21 individual spectra against late-type templates reveals a sinusoidal velocity modulation with a period of 0.68549 ± 0.00001 day and semi-amplitude of 417.7 ± 3.9 km s−1. We derive a mass function f(M) = 5.18 ± 0.15 M ⊙, dynamically confirming the black hole (BH) nature of the compact object. Our analysis of the stellar absorption features supports a K3-5 spectral classification for the donor star, which contributes ≈20% of the total flux at 5200–6800 Å. The photometric 0.703 ± 0.003 day periodicity observed during outburst is 2.6% longer than the orbital period supporting the presence of a superhump modulation in the outburst light curves. In line with this interpretation, we constrain the binary mass ratio to be q ≃ 0.12. In addition, we observe a sharp increase in the Hα emission line equivalent width during inferior conjunction of the donor star that we interpret as a grazing eclipse of the accretion disk and allows us to constrain the binary inclination to i ≳ 69°. On the other hand, the absence of X-ray eclipses during outburst imply i ≲ 77°. These inclination limits, together with our dynamical solution, lead to a BH mass in the range 7–8 M ⊙. We also measure a systemic velocity γ = −21.6 ± 2.3 km s−1, which, combined with the Gaia second data release proper motion and parallax, implies a large peculiar velocity ∼100 km s−1.