Automated Identification of 2612 Late-K and M Dwarfs in the Lamost Commissioning Data Using Classification Template Fits

Abstract

We develop a template-fit method to automatically identify and classify late-type K and M dwarfs in spectra from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). A search of the commissioning data, acquired in 2009-2010, yields the identification of 2,612 late-K and M dwarfs. The template fit method also provides spectral classification to half a subtype, classifies the stars along the dwarf-subdwarf (dM/sdM/esdM/usdM) metallicity sequence, and provides improved metallicity/gravity information on a finer scale. The automated search and classification is performed using a set of cool star templates assembled from the Sloan Digital Sky Survey spectroscopic database. We show that the stars can be efficiently classified despite shortcomings in the LAMOST commissioning data which include bright sky lines in the red. In particular we find that the absolute and relative strengths of the critical TiO and CaH molecular bands around 7000Åare cleanly measured, which provides accurate spectral typing from late-K to mid-M, and makes it possible to estimate metallicity classes in a way that is more efficient and reliable than with the use of spectral indices or spectral-index based parameters such as ζ TiO/CaH . Most of the cool dwarfs observed by LAMOST are found to be metal-rich dwarfs (dM). However, we identify 52 metal-poor M subdwarfs (sdM), 5 very metal-poor extreme subdwarfs (esdM) and 1 probable ultra metal-poor subdwarf (usdM). We use a calibration of spectral type to absolute magnitude and estimate spectroscopic distances for all the stars; we also recover proper motions from the SUPERBLINK and PPMXL catalogs. Our analysis of the estimated transverse motions suggests a mean velocity and standard deviation for the UVW components of velocity to be: <U>=-9.8 km/s, σ U =35.6 km/s; <V>=-22.8 km/s,σ V =30.6 km/s; <W>=-7.9 km/s, σ W =28.4 km/s. The resulting values are general agreement with previous reported results, which yields confidence in our spectral classification and spectroscopic distance estimates, and illustrates the potential for using LAMOST spectra of K and M dwarfs for investigating the chemo-kinematics of the local Galactic disk and halo.

The Solar Neighborhood. XLII. Parallax Results from the CTIOPI 0.9 m Program—Identifying New Nearby Subdwarfs Using Tangential Velocities and Locations on the H–R Diagram

“…To confirm that these candidates are, indeed, cool subdwarfs, spectroscopic observations are typically necessary, such as those by (Scholz et al 2004b;Jao et al 2008;Zhang et al 2013). Large sky spectroscopic surveys like SDSS and LAMOST have also provided systematic ways to identify cool subdwarfs (West et al 2004;Zhong et al 2015). Finally, because subdwarfs have different colors from dwarfs, infrared colors from the all sky infrared surveys 2MASS and WISE have been used to identify local stellar and sub-stellar subdwarfs (Burgasser et al 2007;Kirkpatrick et al 2011).…”

Chemical Properties of the Local Galactic Disk and Halo. I. Fundamental Properties of 1544 Nearby, High Proper-motion M Dwarfs and Subdwarfs

“…Flux recalibration was therefore performed using the procedure outlined below (Section 3). Zhong et al (2015) assembled their classification templates using the spectra of relatively bright late-K and M dwarfs drawn from SDSS, mostly from the catalog of M dwarf stars by West et al (2011), complemented by additional identifications of M subdwarfs (Savchena et al 2014). Instead of using the Hammer software, they re-classified all stars in their sample based on the methods described by Lépine et al (2007Lépine et al ( , 2013.…”

“…The spectral types of these templates ranged from K7.0 to M8.5 including every half-subtype while the metallicity classes covered the original sequence of dM/sdM/esdM/usdM. Zhong et al (2015) increased the number of metallicity classes from 4 to 12 by adding two more subclasses to each class, a more metal-rich and a more metal-poor subclass, expanding each standard class to three subclasses labeled by "r" for the metal-rich, "s" for the standard and "p" for the metal-poor subclass. The extra templates were synthesized by linearly interpolating/extrapolating the gird one third of the way from each standard metallicity class, establishing 12 metallicity subclasses: dM r , dM s , dM p , sdM r , sdM s , sdM p , esdM r , esdM s , esdM p , usdM r , usdM s , usdMp.…”

“…The extra templates were synthesized by linearly interpolating/extrapolating the gird one third of the way from each standard metallicity class, establishing 12 metallicity subclasses: dM r , dM s , dM p , sdM r , sdM s , sdM p , esdM r , esdM s , esdM p , usdM r , usdM s , usdMp. Zhong et al (2015) then applied the template-fit method to 83,500 spectra collected from the Large sky Area Multi-object fiber Spectroscopic Telescope (LAM-OST) commissioning data (Cui et al 2012), and around 2600 stars were confirmed as M dwarfs/subdwafs. To validate the quality of the method and the resulting classification, a visual inspection was performed for a number of spectra spanning a broad range of spectral types.…”

“…Although this approach improved the reliability of the spectral typing, the method proved both time-consuming and human-intensive, and a more efficient technique was therefore required for analyzing large samples. To this end, Zhong et al (2015) developed an automated template-fit method, as an alternative to the direct measurements of band indices. They assigned spectral type and metallicity class to their M dwarf/subdwarf sample based on a comparison with empirical classification templates built from spectra of previously classified stars.…”