Yanxin Guo

M DWARF CATALOG OF THE LAMOST PILOT SURVEY

We present a spectroscopic catalog of 58,360 M dwarfs from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope pilot survey. For each spectrum in the catalog, spectral subtype, radial velocity, Hα equivalent width, a number of prominent molecular band indices, and the metal-sensitive parameter ζ are provided. We use the Sloan Digital Sky Survey Data Release 7 Spectroscopic M dwarf catalog to verify the precision of our methods of classifying the spectral types and measuring the radial velocities. The magnetic activity properties of M dwarfs are also traced by Hα emission lines. The molecular band indices included in this catalog are sensitive to temperature or metallicity, and can be used for further study of the physical properties of M dwarfs. This M dwarf catalog is available on the Web site http://sciwiki.lamost.org/MCatalogPilot/.

LAMOST SPECTROGRAPH RESPONSE CURVES: STABILITY AND APPLICATION TO FLUX CALIBRATION

The task of flux calibration for Large sky Area Multi-Object Spectroscopic Telescope (LAMOST) spectra is difficult due to many factors, such as the lack of standard stars, flat-fielding for large field of view, and variation of reddening between different stars, especially at low Galactic latitudes. Poor selection, bad spectral quality, or extinction uncertainty of standard stars not only might induce errors to the calculated spectral response curve (SRC) but also might lead to failures in producing final 1D spectra. In this paper, we inspected spectra with Galactic latitude and reliable stellar parameters, determined through the LAMOST Stellar Parameter Pipeline (LASP), to study the stability of the spectrograph. To guarantee that the selected stars had been observed by each fiber, we selected 37,931 high-quality exposures of 29,000 stars from LAMOST DR2, and more than seven exposures for each fiber. We calculated the SRCs for each fiber for each exposure and calculated the statistics of SRCs for spectrographs with both the fiber variations and time variations. The result shows that the average response curve of each spectrograph (henceforth ASPSRC) is relatively stable, with statistical errors ≤10%. From the comparison between each ASPSRC and the SRCs for the same spectrograph obtained by the 2D pipeline, we find that the ASPSRCs are good enough to use for the calibration. The ASPSRCs have been applied to spectra that were abandoned by the LAMOST 2D pipeline due to the lack of standard stars, increasing the number of LAMOST spectra by 52,181 in DR2. Comparing those same targets with the Sloan Digital Sky Survey (SDSS), the relative flux differences between SDSS spectra and LAMOST spectra with the ASPSRC method are less than 10%, which underlines that the ASPSRC method is feasible for LAMOST flux calibration.

M Dwarf catalog of LAMOST general survey data release one

We present a spectroscopic catalog of 93 619 M dwarfs from the first data release of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) general survey. During sample selection, M giant contamination was eliminated using 2MASS photometry and CaH/TiO molecular indices. For each spectrum, the spectral subtype and values are provided including radial velocity, Hα equivalent width, a series of prominent molecular band indices, and the metal–sensitive parameter ζ, as well as distances and the space motions for high S/N objects. In addition, Hα emission lines are measured to examine the magnetic activity properties of M dwarfs and 7179 active ones are found. In particular, a subsample with significant variation in magnetic activity is revealed through observations from different epochs. Finally, statistical analysis for this sample is performed, including the metallicity classification, the distribution of molecular band indices and their errors.

ON THE CONSTRUCTION OF A NEW STELLAR CLASSIFICATION TEMPLATE LIBRARY FOR THE LAMOST SPECTRAL ANALYSIS PIPELINE

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

Carbon Stars Identified from LAMOST DR4 Using Machine Learning

In this work, we present a catalog of 2651 carbon stars from the fourth Data Release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST). Using an efficient machine-learning algorithm, we find out these stars from more than seven million spectra. As a by-product, 17 carbon-enhanced metal-poor (CEMP) turnoff star candidates are also reported in this paper, and they are preliminarily identified by their atmospheric parameters. Except for 176 stars that could not be given spectral types, we classify the other 2475 carbon stars into five subtypes including 864 C-H, 226 C-R, 400 C-J, 266 C-N, and 719 barium stars based on a series of spectral features. Furthermore, we divide the C-J stars into three subtypes of CJ( H), C-J(R), C-J(N), and about 90% of them are cool N-type stars as expected from previous literature. Beside spectroscopic classification, we also match these carbon stars to multiple broadband photometries. Using ultraviolet photometry data, we find that 25 carbon stars have FUV detections and they are likely to be in binary systems with compact white dwarf companions.

Nearby M subdwarfs from LAMOST data release 2

We identify 108 M subdwarfs(sd Ms) out of more than two hundred thousand M type spectra from the second data release(DR2) of the LAMOST regular survey. This sample, among which 58 members are identified for the first time, includes 33 extreme subdwarfs(esd Ms) and 11 ultra subdwarfs(usd Ms).The selection is based on the usual ratio of absorption depth of Ca H2, Ca H3 and TiO 5 band systems.We also emphasize the use of the Ca H1 band. We provide estimates of spectral subtype(SPT), L′epine metallicity index ζ, effective temperature and [Fe/H]. Both ζ–[Fe/H] and SPT–Teff figures show reasonable consistency; compared to PHOENIX model spectra, average rounded values of [Fe/H] for sd Ms, esd Ms and usd Ms are respectively –0.5, –1 and –1.5. The photometric distances are estimated, indicating that most sources are located within 500 pc of the Sun and 350 pc of the Galactic disk. Velocities and 3D Galactic motions are also briefly discussed. Among the 108 subdwarfs, seven stars appear to be active with a significant Hα emission line. The source LAMOST J104521.52+482823.3 is a white dwarf- M subdwarf binary, while LAMOST J123045.52+410943.8, also active, exhibits carbon features in red.

An Efficient Method for Rare Spectra Retrieval in Astronomical Databases

One of important aims of astronomical data mining is to systematically search for specific rare objects in a massive spectral dataset, given a small fraction of identified samples with the same type. Most existing methods are mainly based on binary classification, which usually suffer from uncompleteness when the known samples are too few. While, rank-based methods would provide good solutions for such case. After investigating several algorithms, a method combining bipartite ranking model with bootstrap aggregating techniques was developed in this paper. The method was applied in searching for carbon stars in the spectral data of Sloan Digital Sky Survey (SDSS) DR10, and compared with several other popular methods used in data mining. Experimental results validate that the proposed method is not only the most effective but also less time consuming among these competitors automatically searching for rare spectra in a large but unlabelled dataset.128

A sample of E+A galaxy candidates in the Second Data Release of LAMOST Survey

A sample of 70 E+A galaxies is selected from 37 206 galaxies in the second data release of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). This sample is selected according to the criteria for E+A galaxies defined by Goto, and each of these objects is further visually identified. In this sample, most objects are low redshift E+A galaxies with z < 0.25 , and are located in an area of the sky with high Galactic latitude and magnitude from 14 to 18 mag in the g , r and i b ands. A stellar population analysis of the whole sample indicates that the E+A galaxies are characterized by both young and old stellar populations (SPs), and the metal rich SPs have relatively higher contributions than the metal-poor ones. Additionally, a morphological classification of these objects is performed based on images taken from the Sloan Digital Sky Survey .

Design of LAMOST data processing and production database

Before LAMOST spectra release, raw data need to go through a series of processes, i.e. a pipeline after observed, including 2D reduction, spectral analysis, eyeball identification. It is a proper strategy that utilizing a database to integrate them. By using database the coupling between relative modules would be reduced to make the adding or removing of them more convenient, and the dataflow seems to be more clearly. The information of a specific object, from target selection to intermediate results and spectrum production, can be efficiently accessed and traced back through the database search, rather than via FITS reading. Furthermore, since the pipeline has not been perfected yet, the eyeball check is needed before the spectra are released, and an appropriate database can make the feedback period of eyeball check result more conveniently, thus the improvement of the pipeline will be more purposely. Finally, database can be a data mining tools for the statistics and analysis of massive astronomical data. This article focuses on the database design and the data processing flow built on it for LAMOST. The database design requirement of the existing routines, such as input/output, the relationship or dependence between them is introduced. Accordingly, the database structure suited for multiple version data process and eyeball verification is presented. The dataflow, how the pipeline is integrated relied on such a dedicated database system and how it worked are also explained. In addition, some user interfaces, eyeball check interfaces, statistical functions are also presented.