Chemical abundance analysis of 19 barium stars

We aim at deriving accurate atmospheric parameters and chemical abundances of 19 barium（Ba） stars, including both strong and mild Ba stars, based on the high signal-to-noise ratio and high resolution Echelle spectra obtained from the 2.16 m telescope at Xinglong station of National Astronomical Observatories, Chinese Academy of Sciences. The chemical abundances of the sample stars were obtained from an LTE, plane-parallel and line-blanketed atmospheric model by inputting the atmospheric parameters（effective temperatures Teff, surface gravities log g, metallicity [Fe/H] and microturbulence velocity ξt） and equivalent widths of stellar absorption lines. These samples of Ba stars are giants as indicated by atmospheric parameters, metallicities and kinematic analysis about UVW velocity. Chemical abundances of 17 elements were obtained for these Ba stars. Their Na, Al, α- and iron-peak elements（O, Na, Mg, Al, Si, Ca,Sc, Ti, V, Cr, Mn, Ni） are similar to the solar abundances. Our samples of Ba stars show obvious overabundances of neutron-capture（n-capture） process elements relative to the Sun. Their median abundances of[Ba/Fe], [La/Fe] and [Eu/Fe] are 0.54, 0.65 and 0.40, respectively. The Y I and Zr I abundances are lower than Ba, La and Eu, but higher than the α- and iron-peak elements for the strong Ba stars and similar to the iron-peak elements for the mild stars. There exists a positive correlation between Ba intensity and [Ba/Fe].For the n-capture elements（Y, Zr, Ba, La）, there is an anti-correlation between their [X/Fe] and [Fe/H]. We identify nine of our sample stars as strong Ba stars with [Ba/Fe]〉0.6 where seven of them have Ba intensity Ba=2-5, one has Ba=1.5 and another one has Ba=1.0. The remaining ten stars are classified as mild Ba stars with 0.17〈[Ba/Fe]〈0.54.

A search for Mg-poor stars using LAMOST survey data

Most Galactic metal-poor stars exhibit enhanced α-abundances（e.g.[Mg/Fe] ～ ＋0.4） according to previous studies of stellar chemical compositions.However, a handful of metal-poor stars with large deficiencies in Mg（e.g. [Mg/Fe]～-0.2） show severe departures from this α-enhancement trend. The sub-solar[Mg/Fe] ratios of these anomalous stars indicate that they possess different chemical enrichment histories than the majority of Galactic metal-poor stars. In previous work,we presented a method to select Mg-poor metal-poor stars from low-resolution SDSS spectra based on a spectral matching technique. In this paper, a similar method is applied to low-resolution（R ～ 1800） LAMOST spectra. Stellar [Mg/Fe] abundances are determined by using stellar parameters delivered by the LAMOST Data Release2 catalog. From a sample of ～ 60 000 stars with atmospheric parameters in the range Teff = [5500, 6500] K and [Fe/H] = [-2.4, ＋0.5], we select 15 candidate Mg-poor metal-poor stars.

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope(Mephisto)W Survey.Based on the variable sources light curve libraries from the Sloan Digital Sky Survey(SDSS)Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler,we have simulated the uvgriz multi-band light curves of RR Lyrae stars,quasars and other variable sources for the first year observation of Mephisto W Survey.We have applied the ensemble machine learning algorithm Random Forest Classifier(RFC)to identify RR Lyrae stars and quasars,respectively.We build training and test samples and extract~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification.We find that,our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness,with purity=95.4%and completeness=96.9%for the RR Lyrae RFC and purity=91.4%and completeness=90.2%for the quasar RFC.We have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.

13 Galactic Star Clusters in Gaia DR3 Identified by An Improved FoF and UPMASK Hybrid Method Using MvC

Open clusters(OCs)serve as invaluable tracers for investigating the properties and evolution of stars and galaxies.Despite recent advancements in machine learning clustering algorithms,accurately discerning such clusters remains challenging.We re-visited the 3013 samples generated with a hybrid clustering algorithm of FoF and pyUPMASK.A multi-view clustering(MvC)ensemble method was applied,which analyzes each member star of the OC from three perspectives—proper motion,spatial position,and composite views—before integrating the clustering outcomes to deduce more reliable cluster memberships.Based on the MvC results,we further excluded cluster candidates with fewer than ten member stars and obtained 1256 OC candidates.After isochrone fitting and visual inspection,we identified 506 candidate OCs in the Milky Way.In addition to the 493 previously reported candidates,we finally discovered 13 high-confidence new candidate clusters.

Preparation for CSST:Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method

Most existing star-galaxy classifiers depend on the reduced information from catalogs,necessitating careful data processing and feature extraction.In this study,we employ a supervised machine learning method(GoogLeNet)to automatically classify stars and galaxies in the COSMOS field.Unlike traditional machine learning methods,we introduce several preprocessing techniques,including noise reduction and the unwrapping of denoised images in polar coordinates,applied to our carefully selected samples of stars and galaxies.By dividing the selected samples into training and validation sets in an 8:2 ratio,we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies.The results indicate that the GoogLeNet model is highly effective,achieving accuracies of 99.6% and 99.9% for stars and galaxies,respectively.Furthermore,by comparing the results with and without preprocessing,we find that preprocessing can significantly improve classification accuracy(by approximately 2.0% to 6.0%)when the images are rotated.In preparation for the future launch of the China Space Station Telescope(CSST),we also evaluate the performance of the GoogLeNet model on the CSST simulation data.These results demonstrate a high level of accuracy(approximately 99.8%),indicating that this model can be effectively utilized for future observations with the CSST.

A search for strongly Mg-enhanced stars from the Sloan Digital Sky Survey

Strongly Mg-enhanced stars with [Mg/Fe] 〉 1.0 show peculiar abundance patterns and hence are of great interest for our understanding of stellar formation and chemical evolution of the Galaxy. A systematic search for strongly Mg-enhanced stars based on low-resolution （R≈2000） spectra from the Sloan Digital Sky Survey （SDSS） is carried out by finding the synthetic spectrum that best matches the observed one in the region of Mg I b lines around λ5170 ,A via a profile matching method. The advantage of our method is that fitting parameters are refined by reproducing the [Mg/Fe] ratios of 47 stars from the very precise high-resolution spectroscopic （HRS） analysis by Nissen ＆ Schuster; and these parameters are crucial to the precision and validity of the derived Mg abundances. As a further check of our method, Mg abun- dances are estimated with our method for member stars in four Galactic globular clus- ters （M92, M 13, M3, M71） which cover the same metallicity range as our sample, and the results are in good agreement with those of HRS analysis in the literature. The val- idation of our method is also demonstrated by the agreement of [Mg/Fe] between our values and those of HRS analysis by Aoki et al. Finally, 33 candidates of strongly Mg- enhanced stars with [Mg/Fe]〉 1.0 are selected from 14 850 F and G stars. Follow-up observations will be carried out on these candidates with high-resolution spectroscopy by large telescopes in the near future, so as to check our selection procedure and to perform a precise and detailed abundance analysis and to explore the origins of these stars.

Application of Random Forest Regressions on Stellar Parameters of A-type Stars and Feature Extraction

Measuring the stellar parameters of A-type stars is more difficult than FGK stars because of the sparse features in their spectra and the degeneracy between effective temperature(T_(eff))and gravity(log g).Modeling the relationship between fundamental stellar parameters and features through machine learning is possible because we can employ the advantage of big data rather than sparse known features.As soon as the model is successfully trained,it can be an efficient approach for predicting Teffand log g for A-type stars especially when there is large uncertainty in the continuum caused by flux calibration or extinction.In this paper,A-type stars are selected from LAMOST DR7 with a signal-to-noise ratio greater than 50 and the Teffranging within 7000 to 10,000 K.We perform the Random Forest(RF)algorithm,one of the most widely used machine learning algorithms to establish the regression relationship between the flux of all wavelengths and their corresponding stellar parameters(T_(eff))and(log g)respectively.The trained RF model not only can regress the stellar parameters but also can obtain the rank of the wavelength based on their sensibility to parameters.According to the rankings,we define line indices by merging adjacent wavelengths.The objectively defined line indices in this work are amendments to Lick indices including some weak lines.We use the Support Vector Regression algorithm based on our new defined line indices to measure the temperature and gravity and use some common stars from Simbad to evaluate our result.In addition,the Gaia Hertzsprung-Russell diagram is used for checking the accuracy of Teffand log g.

Automatic removal of false image stars in disk-resolved images of the Cassini Imaging Science Subsystem

Taking a large number of images,the Cassini Imaging Science Subsystem(ISS)has been routinely used in astrometry.In ISS images,disk-resolved objects often lead to false detection of stars that disturb the camera pointing correction.The aim of this study was to develop an automated processing method to remove the false image stars in disk-resolved objects in ISS images.The method included the following steps:extracting edges,segmenting boundary arcs,fitting circles and excluding false image stars.The proposed method was tested using 200 ISS images.Preliminary experimental results show that it can remove the false image stars in more than 95%of ISS images with disk-resolved objects in a fully automatic manner,i.e.,outperforming the traditional circle detection based on Circular Hough Transform(CHT)by 17%.In addition,its speed is more than twice as fast as that of the CHT method.It is also more robust(no manual parameter tuning is needed)when compared with CHT.The proposed method was also applied to a set of ISS images of Rhea to eliminate the mismatch in pointing correction in automatic procedure.Experiment results showed that the precision of final astrometry results can be improve by roughly 2 times that of automatic procedure without the method.It proved that the proposed method is helpful in the astrometry of ISS images in a fully automatic manner.

BV(RI )c photometric study of three variable PMS stars in the field of V733 Cephei

This paper reports results from the first long-term BV(RI)c photometric CCD observations of three variable pre-main-sequence stars collected during the period from February 2007 to January 2020. The investigated stars are located in the field of the PMS star V733 Cep within the Cepheus OB3 association. All stars from our study show rapid photometric variability in all-optical passbands. In this paper, we describe and discuss the photometric behavior of these stars and the possible reasons for their variability. In the light variation of two of the stars, we found periodicity.

On the LSP3 estimates of surface gravity for LAMOST-Kepler stars with asteroseismic measurements

Asteroseismology allows for deriving precise values of the surface gravity of stars. The accurate asteroseismic determinations now available for the large number of stars in the Kepler fields can be used to check and calibrate surface gravities that are currently being obtained spectroscopically for a huge number of stars targeted by large-scale spectroscopic surveys, such as the on-going Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） Galactic survey. The LAMOST spectral surveys have obtained a large number of stellar spectra in the Kepler fields. Stellar atmospheric parameters of those stars have been determined with the LAMOST Stellar Parameter Pipeline at Peking University （LSP3）, by template matching with the MILES empirical spectral library. In the current work, we compare surface gravities yielded by LSP3 with those of two asteroseismic samples-- the largest Kepler asteroseismic sample and the most accurate Kepler asteroseismic sample. We find that LSP3 surface gravities are in good agreement with asteroseismic values of Hekker et al., with a dispersion of -0.2 dex. Except for a few cases, asteroseismic surface gravities ofHuber et al. and LSP3 spectroscopic values agree for a wide range of surface gravities. However, some patterns in the differences can be identified upon close inspection. Potential ways to further improve the LSP3 spectroscopic estimation of stellar atmospheric parameters in the near future are briefly discussed. The effects of effective temperature and metallicity on asteroseismic determinations of surface gravities for giant stars are also discussed.

Identifying Carbon stars from the LAMOST pilot survey with the efficient manifold ranking algorithm

Carbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the efficient manifold ranking algorithm to search for carbon stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） pilot survey, whose performance and robustness are verified comprehensively with four test experiments. Using this algorithm, we find a total of 183 carbon stars, and 158 of them are new findings. According to different spectral features, our carbon stars are classified as 58 C-H stars, 11 C-H star candidates, 56 C-R stars, ten C-R star candidates, 30 C-N stars, three C-N star candidates, and four C-J stars. There are also ten objects which have no spectral type because of low spec- tral quality, and a composite spectrum consisting of a white dwarf and a carbon star. Applying the support vector machine algorithm, we obtain the linear optimum clas- sification plane in the J - H versus/-/- Ks color diagram which can be used to distinguish C-H from C-N stars with their J - H and H - Ks colors. In addition, we identify 18 dwarf carbon stars with their relatively high proper motions, and find three carbon stars with FUV detections likely have optical invisible companions by cross matching with data from the Galaxy Evolution Explorer. In the end, we detect four variable carbon stars with the Northern Sky Variability Survey, the Catalina Sky Survey and the LINEAR variability databases. According to their periods and ampli- tudes derived by fitting light curves with a sinusoidal function, three of them are likely semiregular variable stars and one is likely a Mira variable star.

Automated Separation of Stars and Normal Galaxies Based on Statistical Mixture Modeling with RBF Neural Networks

For LAMOST, the largest sky survey program in China, the solution of the problem of automatic discrimination of stars from galaxies by spectra has shown that the results of the PSF test can be significantly refined. However, the problem is made worse when the redshifts of galaxies are not available. We present a new automatic method of star/(normal) galaxy separation, which is based on Statistical Mixture Modeling with Radial Basis Function Neural Networks (SMM-RBFNN). This work is a continuation of our previous one, where active and non-active celestial objects were successfully segregated. By combining the method in this paper and the previous one, stars can now be effectively separated from galaxies and AGNs by their spectra-a major goal of LAMOST, and an indispensable step in any automatic spectrum classification system. In our work, the training set includes standard stellar spectra from Jacoby's spectrum library and simulated galaxy spectra of EO, SO, Sa, Sb types with redshift ranging from 0 to 1.2, and the test set of stellar spectra from Pickles' atlas and SDSS spectra of normal galaxies with SNR of 13. Experiments show that our SMM-RBFNN is more efficient in both the training and testing stages than the BPNN (back propagation neural networks), and more importantly, it can achieve a good classification accuracy of 99.22% and 96.52%, respectively for stars and normal galaxies.

Photometric observations and light curve solutions of the W UMa stars NSVS 2244206,NSVS 908513,CSS J004004.7+385531 and VSX J062624.4+570907

Photometric observations in Sloan 9＇ and i＇ bands of four W UMa stars, NSVS 2244206, NSVS 908513, CSS J004004.7＋385531 and VSX J062624.4＋570907, are presented. The light curve solutions reveal that all targets have overcontact configurations with fillout factors within 0.15-0.26. Their components are G-K spectral types and are almost in thermal contact. They are also relatively close in size and luminosity： the radius ratios r2/r1 are within 0.75-0.90; the luminosity ratios 12/11 are within 0.53-0.63. The results of the light curve solution of CSS J004004.7＋385531 imply the weak limb-darkening effect of its primary component and possible presence of additional absorbing features in the system.

Light curve solutions of six eclipsing binaries at the lower limit of periods for W UMa stars

Photometric observations are presented in V and I bands of six eclipsing binaries at the lower limit of the orbital periods for W UMa stars. Three of them are newly discovered eclipsing systems. The light curve solutions reveal that all shortperiod targets are contact or overcontact binaries and six new binaries are added to the family of short-period systems with estimated parameters. Four binaries have com- ponents that are equal in size and a mass ratio near 1. The phase variability shown by the V-I colors of all targets may be explained by lower temperatures on their back surfaces than those on their side surfaces. Five systems exhibit the O＇Connell effect that can be modeled by cool spots on the side surfaces of their primary components. The light curves of V1067 Her in 2011 and 2012 are fitted by diametrically opposite spots. Applying the criteria for subdivision of W UMa stars to our targets leads to ambiguous results.

Long-term V(RI) CCD photometry of pre-main-sequence stars in the association Cepheus OB3

Results from optical CCD photometric observations of 13 pre-main-sequence stars collected during the period from February 2007 to November 2020 are presented.These stars are located in the association Cepheus OB3,in the field of the young star V733 Cephei.Photometric observations,especially concerning the long-term variability of the stars,are missing in the literature.We present the first longterm V(RI)c monitoring for them,that cover 13 years.Results from our study indicate that all of the investigated stars manifest strong photometric variability.The presented paper is a part of our program for the photometric study of pre-main-sequence stars located in active star-forming regions.

Stellar loci IV.red giant stars

In the fourth paper of this series,we present the metallicity-dependent Sloan Digital Sky Survey(SDSS)stellar color loci of red giant stars,using a spectroscopic sample of red giants in the SDSS Stripe82 region.The stars span a range of 0.55-1.2 mag in color g-i,-0.3--2.5 in metallicity[Fe/H],and have values of surface gravity log g smaller than 3.5 dex.As in the case of main-sequence(MS)stars,the intrinsic widths of loci of red giants are also found to be quite narrow,a few mmag at maximum.There are however systematic differences between the metallicity-dependent stellar loci of red giants and MS stars.The colors of red giants are less sensitive to metallicity than those of MS stars.With good photometry,photometric metallicities of red giants can be reliably determined by fitting the u-g,g-r,r-i,and i-z colors simultaneously to an accuracy of 0.2-0.25 dex,comparable to the precision achievable with low-resolution spectroscopy for a signal-to-noise ratio of 10.By comparing fitting results to the stellar loci of red giants and MS stars,we propose a new technique to discriminate between red giants and MS stars based on the SDSS photometry.The technique achieves completeness of~70 per cent and efficiency of~80 per cent in selecting metal-poor red giant stars of[Fe/H]≤-1.2.It thus provides an important tool to probe the structure and assemblage history of the Galactic halo using red giant stars.

Identifying Symbiotic Stars with Machine Learning

Symbiotic stars are interacting binary systems, making them valuable for studying various astronomical phenomena, such as stellar evolution, mass transfer, and accretion processes. Despite recent progress in the discovery of symbiotic stars, a significant discrepancy between the observed population of symbiotic stars and the number predicted by theoretical models. To bridge this gap, this study utilized machine learning techniques to efficiently identify new symbiotic star candidates. Three algorithms(XGBoost, LightGBM, and Decision Tree)were applied to a data set of 198 confirmed symbiotic stars and the resulting model was then used to analyze data from the LAMOST survey, leading to the identification of 11,709 potential symbiotic star candidates. Out of these potential symbiotic star candidates listed in the catalog, 15 have spectra available in the Sloan Digital Sky Survey(SDSS) survey. Among these 15 candidates, two candidates, namely V^(*)V603 Ori and V^(*)GN Tau, have been confirmed as symbiotic stars. The remaining 11 candidates have been classified as accreting-only symbiotic star candidates. The other two candidates, one of which has been identified as a galaxy by both SDSS and LAMOST surveys, and the other identified as a quasar by SDSS survey and as a galaxy by LAMOST survey.

Discovery of 2716 hot emission-line stars from LAMOST DR5

We present a catalog of 3339 hot emission-line stars(ELSs)identified from 451695 O,B and A type spectra,provided by LAMOST Data Release 5(DR5).We developed an automated Python routine that identified 5437 spectra having a peak between 6561 and 6568.False detections and bad spectra were removed,leaving 4138 good emission-line spectra of 3339 unique ELSs.We re-estimated the spectral types of 3307 spectra as the LAMOST Stellar Parameter Pipeline(LASP)did not provide accurate spectral types for these emission-line spectra.As Herbig Ae/Be stars exhibit higher excess in near-infrared and mid-infrared wavelengths than classical Ae/Be stars,we relied on 2 MASS and WISE photometry to distinguish them.Finally,we report 1089 classical Be,233 classical Ae and 56 Herbig Ae/Be stars identified from LAMOST DR5.In addition,928 B[em]/A[em]stars and 240 CAe/CBe potential candidates are identified.From our sample of 3339 hot ELSs,2716 ELSs identified in this work do not have any record in the SIMBAD database and they can be considered as new detections.Identification of such a large homogeneous set of emission-line spectra will help the community study the emission phenomenon in detail without worrying about the inherent biases when compiling from various sources.

PSRDP:A Parallel Processing Method for Pulsar Baseband Data

To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRDP can perform operations such as baseband data unpacking,channel separation,coherent dedispersion,Stokes detection,phase and folding period prediction,and folding integration in GPU clusters.We tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes,and the J0332+5434 pulsar baseband data generated by the self-developed backend of the Nan Shan Radio Telescope.We obtained the pulse profiles of each baseband data.Through experimental analysis,we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy(DSPSR),which verified the effectiveness of the PSRDP algorithm.Furthermore,using the same baseband data,we compared the processing speed of PSRDP with DSPSR,and the results showed that PSRDP was not slower than DSPSR in terms of speed.The theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT(Qi Tai radio Telescope)ultra-wide bandwidth pulsar baseband data.

A star-based method for precise wavelength calibration of the Chinese Space Station Telescope(CSST) slitless spectroscopic survey

The Chinese Space Station Telescope(CSST)spectroscopic survey aims to deliver high-quality low-resolution(R>200)slitless spectra for hundreds of millions of targets down to a limiting magnitude of about 21 mag,distributed within a large survey area(17500 deg2)and covering a wide wavelength range(255-1000 nm by three bands GU,GV,and GI).As slitless spectroscopy precludes the usage of wavelength calibration lamps,wavelength calibration is one of the most challenging issues in the reduction of slitless spectra,yet it plays a key role in measuring precise radial velocities of stars and redshifts of galaxies.In this work,we propose a star-based method that can monitor and correct for possible errors in the CSST wavelength calibration using normal scientific observations,taking advantage of the facts that(ⅰ)there are about ten million stars with reliable radial velocities now available thanks to spectroscopic surveys like LAMOST,(ⅱ)the large field of view of CSST enables efficient observations of such stars in a short period of time,and(ⅲ)radial velocities of such stars can be reliably measured using only a narrow segment of CSST spectra.We demonstrate that it is possible to achieve a wavelength calibration precision of a few km s^(-1) for the GU band,and about 10 to 20 kms^(-1) for the GV and GI bands,with only a few hundred velocity standard stars.Implementations of the method to other surveys are also discussed.