We cross-matched 1.3 million white dwarf (WD) candidates from Gaia EDR3 with spectral data from L... more We cross-matched 1.3 million white dwarf (WD) candidates from Gaia EDR3 with spectral data from LAMOST DR7 within 3′′. Applying machine learning described in our previous work, we spectroscopically identified 6 190 WD objects after visual inspection, among which 1 496 targets were firstly confirmed. 32 detailed classes were adopted for them, including but not limited to DAB and DB+M. We estimated the atmospheric parameters for the DA and DB type WD using LevenbergMarquardt least-squares algorithm (LM). Finally, a catalog of WD spectra from LAMOST was provided online.
Phase \RNum{2} of the LAMOST-{\sl Kepler/K}2 survey (LK-MRS), initiated in 2018, aims at collecti... more Phase \RNum{2} of the LAMOST-{\sl Kepler/K}2 survey (LK-MRS), initiated in 2018, aims at collecting medium-resolution spectra ($R\sim7,500$; hereafter MRS) for more than $50,000$ stars with multiple visits ($\sim60$ epochs) over a period of 5 years (2018 September to 2023 June). We selected 20 footprints distributed across the {\sl Kepler} field and six {\sl K}2 campaigns, with each plate containing a number of stars ranging from $\sim2,000$ to $\sim 3,000$. During the first year of observations, the LK-MRS has already collected $\sim280,000$ and $\sim369,000$ high-quality spectra in the blue and red wavelength range, respectively. The atmospheric parameters and radial velocities for $\sim259,000$ spectra of $21,053$ targets were successfully calculated by the LASP pipeline. The internal uncertainties for the effective temperature, surface gravity, metallicity, and radial velocity are found to be $100$\,K, $0.15$\,dex, $0.09$\,dex, and $1.00$\,km\,s$^{-1}$, respectively. We found $1...
In this paper, we report 591 high-velocity star candidates (HiVelSCs) selected from over 10 milli... more In this paper, we report 591 high-velocity star candidates (HiVelSCs) selected from over 10 million spectra of Data Release 7 (DR7) of the Large Sky Area Multi-object Fiber Spectroscopic Telescope and the second Gaia data release, with three-dimensional velocities in the Galactic rest frame larger than 445 km s−1. We show that at least 43 HiVelSCs are unbound to the Galaxy with escape probabilities larger than 50%, and this number decreases to eight if the possible parallax zero-point error is corrected. Most of these HiVelSCs are metal-poor and slightly α-enhanced inner halo stars. Only 14% of them have [Fe/H] > −1, which may be the metal-rich “in situ” stars in the halo formed in the initial collapse of the Milky Way or metal-rich stars formed in the disk or bulge but kinematically heated. The low ratio of 14% implies that the bulk of the stellar halo was formed from the accretion and tidal disruption of satellite galaxies. In addition, HiVelSCs on retrograde orbits have slight...
The NASA Kepler mission obtained long-term high-quality photometric observations for a large numb... more The NASA Kepler mission obtained long-term high-quality photometric observations for a large number of stars in its original field of view from 2009 to 2013. To provide reliable stellar parameters in a homogeneous way, the LAMOST telescope began to carry out low-resolution spectroscopic observations for as many stars as possible in the Kepler field in 2012. By June 2018, 238 386 low-resolution spectra with SNR g ≥ 6 had been collected for 155 623 stars in the Kepler field, enabling the determination of atmospheric parameters and radial velocities, as well as spectral classification of the target stars. This information has been used by astronomers to carry out research in various fields, including stellar pulsations and asteroseismology, exoplanets, stellar magnetic activity and flares, peculiar stars and the Milky Way, binary stars, etc. We summarize the research progress in these fields where the usage of data from the LAMOST-Kepler (LK) project has played a role. In addition, tim...
Abstract Stellar spectral classification is one of the most fundamental tasks in survey astronomy... more Abstract 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.
An automatic and efficient method for cataclysmic variables candidates is presented in the presen... more An automatic and efficient method for cataclysmic variables candidates is presented in the present paper. The identified CVs were selected as templates. A model was constructed by random forest algorithm with templates and random selected spectra. Wavelength ranking was described by the model and the classifier was constructed afterwards. Most of the non-candidates were excluded by the method. Template matching strategy was used to identify the final candidates which were analyzed to complement the templates as feedback. 16 new CVs candidates were found in the experiment that shows that our approach to finding special celestial bodies can be feasible in LAMOST.
Using the Lick line index, according to the magnanimity characteristics of the spectrum an effici... more Using the Lick line index, according to the magnanimity characteristics of the spectrum an efficient algorithm of the atmospheric physical parameters measurement by the linear regression method from the point of view of statistical regression was designed. The linear regression was used to achieve the best regression effect by selecting the type of regression and the composition of line index. The formula obtained from the regression model makes the computation speed fast when applied to new data, and the clarity and ease of analysis processing can not be reached by other methods. The experimental results show that through the line index regression method to get the atmospheric physical parameters is feasible.
There are many valuable rare and unusual objects in spectra dataset of Sloan Digital Sky Survey (... more There are many valuable rare and unusual objects in spectra dataset of Sloan Digital Sky Survey (SDSS) Data Release eight (DR8), such as special white dwarfs (DZ, DQ, DC), carbon stars, white dwarf main-sequence binaries (WDMS), cataclysmic variable (CV) stars and so on, so it is extremely significant to search for rare and unusual celestial objects from massive spectra dataset. A novel algorithmbased on Kernel dense estimation and K-nearest neighborhoods (KNN) has been presented, and applied to search for rare and unusual celestial objects from 546 383 stellar spectra of SDSS DR8. Their densities are estimated using Gaussian kernel density estimation, the top 5 000 spectra in descend order by their densities are selected as rare objects, and the top 300 000 spectra in ascend order by their densities are selected as normal objects. Then, KNN were used to classify the rest objects, and simultaneously K nearest neighbors of the 5 000 rare spectra are also selected as rare objects. As ...
Proceedings of the International Astronomical Union, 2013
We positionally cross-matched the stellar observations between LAMOST pilot survey and SDSS/SEGUE... more We positionally cross-matched the stellar observations between LAMOST pilot survey and SDSS/SEGUE database, picked out more than 4000 targets observed by both projects, mostly are late A and FGK type stars. For the two dataset, we adopted ULySS program (Koleva et al. 2009, Wu et al. 2011a) to determine the stellar atmospheric parameters (T eff, log g, [Fe/H], Radial Velocity) with the ELODIE library as a reference (Wu et al. 2011b). For the individual parameter, we made two kinds of comparison, first for the SDSS spectra, second for the LAMOST spectra, tested the differences between the SSPP (SDSS/SEGUE stellar parameter pipeline) measurements and the ULySS derived results. Since the LAMOST pilot survey observations SNR (Signal to Noise Ratio) are low, for the latter comparison we excluded those stars with g band SNR < 10, then the sample volumn reduced to around 1300. Fig.1 displays the details of the second kind comparison for each parameter. All the comparisons demonstrate acc...
We cross-matched 1.3 million white dwarf (WD) candidates from Gaia EDR3 with spectral data from L... more We cross-matched 1.3 million white dwarf (WD) candidates from Gaia EDR3 with spectral data from LAMOST DR7 within 3′′. Applying machine learning described in our previous work, we spectroscopically identified 6 190 WD objects after visual inspection, among which 1 496 targets were firstly confirmed. 32 detailed classes were adopted for them, including but not limited to DAB and DB+M. We estimated the atmospheric parameters for the DA and DB type WD using LevenbergMarquardt least-squares algorithm (LM). Finally, a catalog of WD spectra from LAMOST was provided online.
Phase \RNum{2} of the LAMOST-{\sl Kepler/K}2 survey (LK-MRS), initiated in 2018, aims at collecti... more Phase \RNum{2} of the LAMOST-{\sl Kepler/K}2 survey (LK-MRS), initiated in 2018, aims at collecting medium-resolution spectra ($R\sim7,500$; hereafter MRS) for more than $50,000$ stars with multiple visits ($\sim60$ epochs) over a period of 5 years (2018 September to 2023 June). We selected 20 footprints distributed across the {\sl Kepler} field and six {\sl K}2 campaigns, with each plate containing a number of stars ranging from $\sim2,000$ to $\sim 3,000$. During the first year of observations, the LK-MRS has already collected $\sim280,000$ and $\sim369,000$ high-quality spectra in the blue and red wavelength range, respectively. The atmospheric parameters and radial velocities for $\sim259,000$ spectra of $21,053$ targets were successfully calculated by the LASP pipeline. The internal uncertainties for the effective temperature, surface gravity, metallicity, and radial velocity are found to be $100$\,K, $0.15$\,dex, $0.09$\,dex, and $1.00$\,km\,s$^{-1}$, respectively. We found $1...
In this paper, we report 591 high-velocity star candidates (HiVelSCs) selected from over 10 milli... more In this paper, we report 591 high-velocity star candidates (HiVelSCs) selected from over 10 million spectra of Data Release 7 (DR7) of the Large Sky Area Multi-object Fiber Spectroscopic Telescope and the second Gaia data release, with three-dimensional velocities in the Galactic rest frame larger than 445 km s−1. We show that at least 43 HiVelSCs are unbound to the Galaxy with escape probabilities larger than 50%, and this number decreases to eight if the possible parallax zero-point error is corrected. Most of these HiVelSCs are metal-poor and slightly α-enhanced inner halo stars. Only 14% of them have [Fe/H] > −1, which may be the metal-rich “in situ” stars in the halo formed in the initial collapse of the Milky Way or metal-rich stars formed in the disk or bulge but kinematically heated. The low ratio of 14% implies that the bulk of the stellar halo was formed from the accretion and tidal disruption of satellite galaxies. In addition, HiVelSCs on retrograde orbits have slight...
The NASA Kepler mission obtained long-term high-quality photometric observations for a large numb... more The NASA Kepler mission obtained long-term high-quality photometric observations for a large number of stars in its original field of view from 2009 to 2013. To provide reliable stellar parameters in a homogeneous way, the LAMOST telescope began to carry out low-resolution spectroscopic observations for as many stars as possible in the Kepler field in 2012. By June 2018, 238 386 low-resolution spectra with SNR g ≥ 6 had been collected for 155 623 stars in the Kepler field, enabling the determination of atmospheric parameters and radial velocities, as well as spectral classification of the target stars. This information has been used by astronomers to carry out research in various fields, including stellar pulsations and asteroseismology, exoplanets, stellar magnetic activity and flares, peculiar stars and the Milky Way, binary stars, etc. We summarize the research progress in these fields where the usage of data from the LAMOST-Kepler (LK) project has played a role. In addition, tim...
Abstract Stellar spectral classification is one of the most fundamental tasks in survey astronomy... more Abstract 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.
An automatic and efficient method for cataclysmic variables candidates is presented in the presen... more An automatic and efficient method for cataclysmic variables candidates is presented in the present paper. The identified CVs were selected as templates. A model was constructed by random forest algorithm with templates and random selected spectra. Wavelength ranking was described by the model and the classifier was constructed afterwards. Most of the non-candidates were excluded by the method. Template matching strategy was used to identify the final candidates which were analyzed to complement the templates as feedback. 16 new CVs candidates were found in the experiment that shows that our approach to finding special celestial bodies can be feasible in LAMOST.
Using the Lick line index, according to the magnanimity characteristics of the spectrum an effici... more Using the Lick line index, according to the magnanimity characteristics of the spectrum an efficient algorithm of the atmospheric physical parameters measurement by the linear regression method from the point of view of statistical regression was designed. The linear regression was used to achieve the best regression effect by selecting the type of regression and the composition of line index. The formula obtained from the regression model makes the computation speed fast when applied to new data, and the clarity and ease of analysis processing can not be reached by other methods. The experimental results show that through the line index regression method to get the atmospheric physical parameters is feasible.
There are many valuable rare and unusual objects in spectra dataset of Sloan Digital Sky Survey (... more There are many valuable rare and unusual objects in spectra dataset of Sloan Digital Sky Survey (SDSS) Data Release eight (DR8), such as special white dwarfs (DZ, DQ, DC), carbon stars, white dwarf main-sequence binaries (WDMS), cataclysmic variable (CV) stars and so on, so it is extremely significant to search for rare and unusual celestial objects from massive spectra dataset. A novel algorithmbased on Kernel dense estimation and K-nearest neighborhoods (KNN) has been presented, and applied to search for rare and unusual celestial objects from 546 383 stellar spectra of SDSS DR8. Their densities are estimated using Gaussian kernel density estimation, the top 5 000 spectra in descend order by their densities are selected as rare objects, and the top 300 000 spectra in ascend order by their densities are selected as normal objects. Then, KNN were used to classify the rest objects, and simultaneously K nearest neighbors of the 5 000 rare spectra are also selected as rare objects. As ...
Proceedings of the International Astronomical Union, 2013
We positionally cross-matched the stellar observations between LAMOST pilot survey and SDSS/SEGUE... more We positionally cross-matched the stellar observations between LAMOST pilot survey and SDSS/SEGUE database, picked out more than 4000 targets observed by both projects, mostly are late A and FGK type stars. For the two dataset, we adopted ULySS program (Koleva et al. 2009, Wu et al. 2011a) to determine the stellar atmospheric parameters (T eff, log g, [Fe/H], Radial Velocity) with the ELODIE library as a reference (Wu et al. 2011b). For the individual parameter, we made two kinds of comparison, first for the SDSS spectra, second for the LAMOST spectra, tested the differences between the SSPP (SDSS/SEGUE stellar parameter pipeline) measurements and the ULySS derived results. Since the LAMOST pilot survey observations SNR (Signal to Noise Ratio) are low, for the latter comparison we excluded those stars with g band SNR < 10, then the sample volumn reduced to around 1300. Fig.1 displays the details of the second kind comparison for each parameter. All the comparisons demonstrate acc...
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