A sample of metal-poor galaxies identified from the LAMOST spectral survey
We present a sample of 48 metal-poor galaxies at selected from 92,510 galaxies in the LAMOST survey. These galaxies are identified for their detection of the auroral emission line [O iii]4363 above level, which allows a direct measurement of the electron temperature and the oxygen abundance. The emission line fluxes are corrected for internal dust extinction using Balmer decrement method. With electron temperature derived from [O iii] and electron density from , we obtain the oxygen abundances in our sample which range from (0.09 ) to (0.6 ). We find an extremely metal-poor galaxy with . With multiband photometric data from FUV to NIR and H measurements, we also determine the stellar masses and star formation rates, based on the spectral energy distribution fitting and H luminosity, respectively. We find that our galaxies have low and intermediate stellar masses with , and high star formation rates (SFRs) with . We also find that the metallicities of our galaxies are consistent with the local -based mass-metallicity relation, while the scatter is about 0.28 dex. Additionally, assuming the coefficient of , we find most of our galaxies follow the local mass-metallicity-SFR relation, while a scatter about 0.24 dex exists, suggesting the mass-metallicity relation is weakly dependent on SFR for those metal-poor galaxies.
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Metal-poor galaxies are less chemically evolved galaxies and provide ideal laboratory for investigating galaxy properties in extreme condition (Shi et al., 2005; Lian et al., 2016). Among them, extremely metal-poor galaxies (hereafter XMPGs), defined by their low oxygen abundance with (Kniazev et al., 2003; Pustilnik & Martin, 2007; Doyle et al., 2005), are the most promising young galaxy candidates in the local universe (Izotov & Thuan, 2004). These XMPGs are suspected to be primeval galaxies that are undergoing their first major mass assembly at the observed redshift (Kniazev et al., 2003). Studying these extreme objects can improve our understanding about the early stages of galaxy assembly.
The determination for abundance of elements are considered more reliable if the electron temperature can be measured directly, because the metallicity is anti-correlated with the electron temperature. The electron temperature can be obtained using the auroral line ratios, such as [O iii][O iii]. This technique is often called the method (Aller, 1984). However, galaxies with metallicities derived from the method with detections above 3 are extremely rare. To date, only about 174 such objects has been found (Ly et al., 2014, 2016b).
In order to enlarge the sample of metal-poor galaxies with [O iii]4363 detection, we carry out a systematic search for such objects in the LAMOST Data Release (DR3, DR4 Q1 and Q2). In Section 2, we present our approach for detecting and measuring nebular emission lines, and the selection criteria used to identify the metal-poor galaxies. We describe the determination for the dust attenuation properties and the gas-phase oxygen abundances in Section 3. In Section 4, we describe the methods for estimating stellar masses and SFRs, and then compare the mass-metallicity relation (MZR) and mass-metallicity-SFR relation with local -based relation in Section 5. In addition, we discuss our results in the context of other studies in Section 6. Finally, we summarize our main conclusions in Section 7.
Throughout this paper, we adopt a flat cosmology with , , and km s Mpc to determine distance-dependent measurements. For reference, we adopt (Allende Prieto et al., 2001) for metallicity measurements quoted against the solar value, .
2 Sample selection
2.1 LAMOST dataset
The Large Sky Area Multi–Object Fiber Spectroscopic Telescope (LAMOST, also called the Guo Shou Jing Telescope) is a special reflecting Schmidt telescope with an effective aperture of 4 m and a field of view (FoV) of 5 (Wang et al., 1996; Su & Cui, 2004; Zhao et al., 2012; Cui et al., 2012; Luo et al., 2012). It is equipped with 4000 fibers, covering a wavelength range of 3800 9000 (Luo et al., 2015) at a resolving power R 1800. The LAMOST Data Release 3 (DR3), Data Release 4 (DR4) Q1 and Q2, based on the past survey from October 2011 to February 2016, contain about three million spectra with limiting magnitude of 18.5 mag. The LAMOST 1D pipeline classifies spectroscopic targets as galaxies, stars, and QSOs by matching against observed SDSS spectral templates, see Luo et al. (2015) for detail. 92,510 objects from the LAMOST catalog are spectroscopically classified as galaxies (‘OBJTYPE’ = ‘GALAXY’).
2.2 Emission-line fluxes determination
According to Song et al. (2012) and Luo et al. (2015), LAMOST 1D spectra are extracted from the CCD images used by the LAMOST 2D pipeline. The wavelength calibration of each spectrum is accomplished by using arc lamp spectra lines, with an average calibration error less than 0.02. The accuracy of the relative flux calibration of LAMOST is above 90.
Assuming unimodal gaussian line profile, we obtain fluxes of strong emission lines such as , , H, , H and by fitting their line profile using the IDL package mpfit (Markwardt et al., 2009). The expected location of emission lines are based on a priori redshift determined by the [O iii] line. In addition, to estimate the signal-to-noise ratios (S/N) of emission lines, we follow the calculation method in Ly et al. (2014).
2.3 Sample selection
Among all the galaxies from the LAMOST ExtraGAlactic Surveys (LEGAS), we first select a subsample of metal-poor galaxies with emission line flux ratios , which consists of 665 galaxies. Among them, we identify 237 objects with detection at . We inspect these 237 objects visually, and find 73 of them are false detections. We also exclude 115 objects that are regions in nearby large galaxies using optical images with SDSS DR12 skyserver 111http://skyserver.sdss.org/dr12/en/tools/chart/listinfo.aspx. Finally, we check the right ascension and declination of the remaining sources, and note that 1 object was observed twice by LAMOST. We keep the observation that has better spectral quality.
As a consequence, our final sample consists of 48 galaxies, making up only of all the LAMOST galaxies until DR4 Q2; this fraction is nearly the same as SDSS (Ly et al., 2014). The median S/N of is 6.1. We obtain the H equivalent width (EW) by dividing the H flux by continuum spectral flux intensity, which is assumed as the average value of observed flux intensities within 50 wide component around the H line. All of the galaxy spectra in our sample show strong emission lines with a median (average) EW of H of 42.9 (53.5) . The EW distribution of H is shown in Figure 1.
To exclude possible AGN contamination, we use the BPT diagram (see Baldwin et al., 1981; Veilleux & Osterbrock, 1987; Kewley et al., 2001; Kauffmann et al., 2003). Figure 2 shows the distribution of our sample in the BPT diagram. The grayscale 2D histogram shows the number density of LAMOST galaxies. The blue dots represent 48 galaxies in our final sample, and the black crosses represent 24 galaxies in our final sample that have also been spectrally detected by SDSS. Among these 24 galaxies detected with SDSS, 19 galaxy spectra also have the detections above 3; we will compare these 19 galaxy spectra from LAMOST and SDSS in Section 6.1. The solid and dashed lines are the demarcation curves between SFGs and AGNs derived by Kauffmann et al. (2003) and Kewley et al. (2001). Galaxies located between the two lines are usually classified as composite objects, which may host a mixture of star formation and AGN. It can be seen that all of the galaxies in our final sample are located in the star-forming region; however, this is unsurprising since we initially selected sources with .
Figure 3 shows example spectra for eight galaxies in our sample that have both been spectrally detected by LAMOST and SDSS. For each object, the left panel shows the LAMOST spectrum, while the right panel shows the SDSS spectrum. All of these spectra show strong emission lines such as (for all LAMOST spectra and part of SDSS spectra), H, , H and . The inserted panels show the zoomed spectra adjacent to lines. It can be seen that the weak [O iii] lines are all detected in the spectra of these galaxies.
3 Metallicity determination
3.1 Dust attenuation correction
We correct the emission-line fluxes for internal dust attenuation using the Balmer decrement measurements, which estimate the dust extinction by inspecting the change of Balmer line ratio, such as , from intrinsic value. Generally, the underlying stellar absorption in the Balmer lines should be well determined to obtain a reliable emission line measurement (Hu et al., 2016). In this work, we first subtract the underlying stellar continuum and stellar absorption for each spectrum using the starlight spectral synthesis code (Cid Fernandes et al., 2005). We assume the intrinsic flux ratio of (Hummer & Storey, 1987) under Case B recombination and use the Calzetti et al. (2000) reddening formalism to derive the color excesses , and then correct the emission line fluxes. In addition, we manually set the color excesses to zero when the ratios are less than 2.86.
The resulting reddening-corrected emission line fluxes relative to H and color excesses are listed in Table 1. As showed in panel of Figure 4, the measured dust extinction are very low with an average value as 0.03 mag.
3.2 Metallicity calculation
With significant detection of [O iii], we can determine the metallicity using the so-called method. In this work, we use the python package pyneb 222http://www.iac.es/protecto/PyNeb/ (Luridiana et al., 2015) to calculate the electron densities () and electron temperatures (), which is evolved from the IRAF nebular package (Shaw & Dufour, 1995; Shaw et al., 1998). Nicholls et al. (2013) have demonstrated that the electron temperatures would be overestimated, and thus the oxygen abundances would be underestimated when using older collision strength data and approximate temperature calibration methods from Izotov et al. (2006). Therefore, we need to set the atomic recombination data and atomic collision strength data before the calculation. We adopt the atomic recombination data of Froese Fischer & Tachiev (2004) for , , and Tayal & Zatsarinny (2010) for . For collision strength data, we adopt those from Kisielius et al. (2009) for , Storey et al. (2014) for , and Tayal & Zatsarinny (2010) for .
As encouraged in Luridiana et al. (2015), we use a cross-converging method to calculate the electron temperatures of regions () and electron densities () with ratios of (Nicholls et al., 2013) and (Tayal & Zatsarinny, 2010). Once the electron temperatures and densities are determined, we can obtain the ionic oxygen abundances using the ratios with the relation derived from Izotov et al. (2006). In order to derive the electron temperatures of regions, we follow an iterative method used in Nicholls et al. (2014),
where is the total oxygen abundance, . The temperature and abundance will converge within five iterations, starting by using the abundance as the total oxygen abundance. Here, the abundance is determined from and the ratio using the Izotov et al. (2006) relation. Using other methods from Garnett (1992) or López-Sánchez et al. (2012), we will get a higher about 0.02 dex and a lower metallicity about 0.05 dex given a . Similarly, we will get a higher about 0.03 dex and thus get a lower metallicity about 0.05 dex when adopt the standard two-zone temperature model from Izotov et al. (2006) and Andrews & Martini (2013). In our final sample, 31 galaxies also have the detections above 3. Using the ratios to derive , we will get lower and higher metallicities, the differences between these average values on and metallicities are about 360 and 0.06 dex, respectively.
To estimate the uncertainties of electron temperatures, electron densities and oxygen abundances, we repeat the calculation 2000 times. For every object, we produce a series of fluxes for each emission line with a Gaussian distribution, assuming its average is the measured line flux and standard deviation is the measured error. Then, our final temperature, density and oxygen abundance are deemed to be the median values of these 2000 calculations, and the corresponding errors are estimated as the half of range of their distributions. We list the final electron temperatures, electron densities and oxygen abundances of our sample in Table 1.
Figure 4 shows the distributions of color excesses, electron densities, electron temperatures and oxygen abundances. The electron densities and temperatures in our sample range from to and , with median values of and , respectively. Their oxygen abundances range from 7.63 to 8.46, with a median of 8.16. The only XMPG found in our sample is ID26 with , which has already been found by Izotov et al. (2012a) with . Interestingly, galaxy ID33, also named as RC2 A1228+12, was regarded as an XMPG in Kunth & Östlin (2000) and Brorby et al. (2014), but the metallicity indicates it is not an XMPG. This judgement was also supported by Pustilnik et al. (2002) and Izotov et al. (2012b) with metallicity measurements of and , respectively.
4 stellar masses and star formation rates
4.1 Stellar masses
To determine the galactic stellar masses of our sample galaxies, we use the IDL code library fast developed by Kriek et al. (2009) to perform the spectral energy distribution (SED) fitting. fast compares the photometry measurements with stellar population synthesis models, based on the minimum template-fitting procedure, to determine mass-to-light ratios, which can be used to estimate the stellar masses of galaxies. We use the stellar templates of Bruzual & Charlot (2003) and a Chabrier (2003) initial mass function (IMF) to synthesize magnitudes. These models span four metallicities (0.004, 0.008, 0.02, 0.05 ) and an exponentially decreasing star formation models (SFR ) with a step from . We assume the dust attenuation law from Calzetti et al. (2000) allowing to vary from 0.0 to 2.0 and stellar population ages ranging from 0 to 100 Gyr. To determine the uncertainties of stellar masses, we use the Monte Carlo simulations and define the number of simulations as 1000. We choose the confidence interval as 68.
Photometric measurements are collected from various survey catalogue. We adopt values of modelmag magnitudes of , and bands from SDSS DR12 photometry catalogue (Abazajian et al., 2004; Alam et al., 2015), magnitudes of , and bands from 2MASS All-Sky Point Source catalog (PSC) and 2MASS All-Sky Extended Source Catalog (XSC) (Skrutskie et al., 2006), magnitudes of (3.4 ) and (4.6 ) from All WISE Source Catalog (Wright et al., 2010), and magnitudes of FUV and NUV from GR6/7 Data Release Catalog (Bianchi et al., 2014). However, not all of our sample galaxies have these photometric measurements. For example, 45 galaxies have FUV photometry, while 3 galaxies are not located in surveyed areas. For these three galaxies, we just use their magnitudes from band to band to perform the SED fitting. We find our sample galaxies spanning three orders with . We should note that we do not make the point spread function (PSF) matching for our photometric data using same observation aperture, which may lead to some uncertainties in stellar mass measurements. The average and median values on stellar mass measurement uncertainties are 0.14 dex and 0.12 dex, respectively. However, comparing our results with total stellar masses in MPA-JHU catalog (Kauffmann et al., 2003; Brinchmann et al., 2004) for these galaxies included in MPA-JHU catalog, we find the differences of average and median values are about 0.1 dex and 0.03 dex, respectively.
Notes: — Basic information, emission line fluxes, electron temperatures and oxygen abundances for our sample galaxies. For every object, the first (second) line presents the parameter (error) values.
’ID’ is the serial number for every object and it will be referred to throughout this paper.
The right ascension (J2000) and declination (J2000) of our sample galaxies are given in units of degrees. The RA, DEC, and redshift are obtained from the header of the spectral FITS files.
Reddening corrected emission line fluxes for our sample galaxies measured from the LAMOST spectra are relative to H. The H fluxes are reported in units of .
The H equivalent widths are given in units of , assuming the mean values of observed flux intensities within 50 wide component around the H as the continuum spectral flux intensities.
The nebular color excesses are derived from the observed flux ratios , and are assumed to be zero when the observed flux ratios are less than 2.86.
Electron temperatures are computed from the oxygen emission line ratios . Electron densities are calculated from an iterative process with and ratios.
The flag numbers indicate the spectral detected states for our objects with SDSS. ”1” (”0”) represent this object has (not) been spectroscopically detected by SDSS.
4.2 Star formation rates
In this work, we use the H emission line luminosities to determine dust-corrected SFRs, assuming a Chabrier (2003) IMF and solar metallicity. The SFR can be calculated from H luminosity as:
where . However, the latest work of Ly et al. (2016a) demonstrated that the above parameter would overestimate the SFR at lower metallicities, and gave the metallicity-dependent parameter as:
where . Above all, the final stellar masses and SFRs in our sample are listed in Table 1.
5 The Mass–Metallicity and Mass–Metallicity–SFR Relations
5.1 The mass-metallicity relation
In panel of Figure 5, we plot the mass-metallicity relation (MZR) with -based metallicities for our sample. These dot symbols of our galaxies are colour-coded by their SFRs. For comparison, we also show the MZRs obtained by Andrews & Martini (2013) and Berg et al. (2012) for their galaxy sample in local universe, which are shown as solid and dotted-dashed black lines, based on metallicity calculation.
The MZR in Berg et al. (2012) is a simple linear fit for a small sample of low luminosity metal-poor galaxies with stellar masses ranging from 5.9 to 9.15. As is shown in panel , the metallicities of our metal-poor galaxies are systematic higher than the MZR in Berg et al. (2012) by about 0.25 dex. The MZR of Andrews & Martini (2013) is fitted with a asymptotic logarithmic formula for about two hundred thousands nearby star-forming galaxies in stellar mass from . Most of our galaxies are in good agreement with the MZR in Andrews & Martini (2013), the average and median values of residuals between metallicities and MZR are 0.0015 dex and 0.025 dex, respectively. We find that the scatter in the MZR from LAMOST data, relative to the MZR of Andrews & Martini (2013), is 0.28 dex.
5.2 Mass-metallicity-SFR relation
The mass-metallicity-SFR relation, also referred to as the fundamental metallicity relation (FMR), is proposed by Mannucci et al. (2010) to describe the anti-correlation between metallicity and SFR at fixed stellar mass. Mannucci et al. (2010) defined a new quantity to minimize the dispersion in MZR for local galaxies. Using the semi-empirical “strong-line” metallicity calibration of Maiolino et al. (2008), Mannucci et al. (2010) yielded . However, Andrews & Martini (2013) found a new value of based on the metallicity calculation method. In this work, we assume the value of , since metallicities of our sample galaxy are also determined with method.
The panel of Figure 5 shows the FMR for our metal-poor galaxies. The solid black line represents the FMR relation derived by Andrews & Martini (2013). Most of our galaxies are consistent with the FMR, the average and median values of the residuals between metallicities and FMR are 0.002 dex and 0.009 dex, respectively. The scatter in the FMR from LAMOST data, relative to the FMR of Andrews & Martini (2013), is about 0.24 dex.
6.1 Comparison with SDSS spectrum
Among our metal-poor galaxy sample, 24 galaxies are also spectrally detected by SDSS, and are marked with flag ”1” in ’SDSS’ column of Table 1. We select these galaxies from SDSS DR12 by matching the RA and DEC with our sample within one arcsec. We also obtain the emission line fluxes from these 24 SDSS galaxy spectra and find that there are 19 spectra with detections above 3. Similarly, we calculate their metallicities with the method. Figure 6 shows the comparisons of S/Ns for weak lines, [O iii] and [S ii] line fluxes ratios (, ), electron temperatures (), electron densities () and oxygen abundances derived from LAMOST spectra and from SDSS spectra. The quality of the SDSS spectra are generally better than those from LAMOST with higher S/N on the weak . Panels and present strong correlation for [O iii] ratios and electron temperatures between the LAMOST and SDSS measurements. Although there are several objects that have large dispersion in the comparison for [S ii] ratios and electron densities, the differences between the final oxygen abundances from these two measurements are less than 0.01 dex.
6.2 Comparison with other [O iii] galaxy samples
All of galaxies in our sample are selected from the local universe (), and have stellar masses spanning three orders with . The only XMPG is detected with in our sample, however, it has already been found by Izotov et al. (2012a). In the past decades, there have been many efforts to search for galaxies and XMPGs in local universe. For example, Kniazev et al. (2003) discovered 12 XMPGs with using SDSS spectroscopy. Izotov et al. (2006) found 6 new XMPGs in 310 galaxies from SDSS DR3. And Berg et al. (2012) also researched 19 low luminosity galaxies with MMT telescope. Additionally, for the intermediate and high redshift universe, Kakazu et al. (2007) mapped 12 XMPGs to with Keck II DEIMOS. Ly et al. (2014) identified 4 XMPGs in 20 emission-line galaxies with at by MMT and Keck telescope. Amorín et al. (2014) also discovered 4 XMPGs from 31 low-luminosity extreme emission line galaxies out to in the VIMOS Ultra-Deep Survey. Recently, Ly et al. (2015) found 28 metal-poor galaxies with stellar mass spanning in DEEP2 at redshift . Ly et al. (2016a) also presented a larger sample of 164 galaxies with weak line at from the “Metal Abundances across Cosmic Time” survey. Compared with these samples, the galaxy number in our sample is small, which may be caused by a limiting magnitude selection of LAMOST. However, the fraction of galaxies with detections in LAMOST data is nearly same as that in SDSS.
6.3 MZR and FMR
The MZR relation, which was established originally by Lequeux et al. (1979) and developed by Garnett & Shields (1987), Skillman et al. (1989), Brodie & Huchra (1991), Zaritsky et al. (1994), Tremonti et al. (2004), indicates that the metallicities of galaxies correlate with their stellar masses. Taking SFR into consideration, Mannucci et al. (2010) found that metallicity decreases with increasing SFR at low stellar mass, while does not depend on SFR at high stellar mass (). However, Yates et al. (2012) suggested that high-mass () galaxies have lower metallicities when their SFRs are lower. These different results may be caused by different metallicity calculation methods. In addition, the MZR is also affected by other physical parameters, such as stellar age and gas fraction. Lian et al. (2015) found that the metallicity is strongly dependent on the , which interpreted galaxies with older stellar ages as having higher metallicities at a fixed stellar mass. Hughes et al. (2013) found that galaxies with higher gas fraction have lower metallicities at a fixed mass. Lara-López et al. (2010) and Mannucci et al. (2010) argued that the MZR is in fact a projection of FMR. In the past years, many efforts (e.g., Mannucci et al. (2010); Berg et al. (2012); Andrews & Martini (2013); Salim et al. (2014); Ly et al. (2015, 2016b)) have been made to explore the MZR and FMR feasibility from low mass to high mass, as well as the evolution with redshift. In the local universe, the metallicity increases with increasing stellar mass, and decreases with increasing SFR at a fixed stellar mass when . Salim et al. (2014) found that the metallicity is anti-corrected with specific SFR regardless of different metallicity indicators or methods used when , while the dependence is weak or absent for massive galaxies when . Salim et al. (2014) also demonstrated that the relative specific SFR is a more physically motivated second parameter for the MZR, and found that the overall scatter in the FMR relation does not significantly decrease relative to the dispersion in the MZR. Recently, Bothwell et al. (2016) reported that the FMR is between stellar mass, metallicity and gas mass instead of the SFR. In addition, Kashino et al. (2016) measured the metallicity of star-forming galaxies based on Dopita et al. (2016) and Maiolino et al. (2008) calibrations, and found that whether the FMR exists or not depend on the metallicity measurement method. The dependence on metallicity and SFR at high stellar mass is still in argument (Kashino et al., 2016). For the intermediate redshift universe, Ly et al. (2016b) showed clearly that the MZR evolves toward lower metallicity at fixed stellar mass with increasing redshift , and found a much weaker dependence of MZR on SFR than in the local universe.
In panel of Figure 5, we colour-code our galaxy points with their SFRs. Figure 5 shows that most of galaxies in our sample have higher metallicities than that of galaxies in Berg et al. (2012), but are consistent with the result in Andrews & Martini (2013). The difference between our work and Berg et al. (2012) may be caused by difference in sample selection and calibrations for electron temperatures and . Andrews & Martini (2013) found that the scatter in MZR for the M-SFR stacks with -based metallicity is 0.22 dex, while the scatter in the FMR is 0.13 dex. The decrease of scatter value in Andrews & Martini (2013) reflects a strong SFR-dependence on the MZR. From visual examination, we do not find strong dependence of MZR on SFR. However, the scatter in FMR is 0.24 dex, lower than the 0.28 dex scatter in MZR, suggesting MZR is weakly dependent on SFR. We note that the average and median values of metallicity measurement uncertainties are 0.09 dex and 0.08 dex, respectively. The average and median values on stellar mass measurement uncertainties are about 0.14 dex and 0.12 dex, respectively. The larger scatters in MZR and FMR compared with Andrews & Martini (2013) relations may be caused by the small galaxy sample size, as well as the measurement uncertainties on stellar mass.
We inspect all the 92,510 galaxies in LAMOST DR3, DR4 Q1 and Q2, and select 48 galaxies with detected at as our metal-poor galaxy sample. Using the method, we obtain the metallicities of these metal-poor galaxies with a median , spanning from 7.63 to 8.46. The most metal-deficient galaxy in our sample is ID26 with , which is the only XMPG we found, but has already been discovered by Izotov et al. (2012a). We also confirm that the galaxy ID33 (RC2 A1228+12) is not an XMPG.
With multiband photometric data from FUV to NIR and H measurements, we determine the stellar masses and dust-corrected SFRs, based on the SED fitting and reddening corrected H luminosities, respectively. We compare the relationship between stellar mass, -based metallicity and SFR of our galaxies with galaxies in the local universe. We find that the metallicities of our galaxies are in good agreement with the local -based MZR in Andrews & Martini (2013) with average and median values of residuals as 0.0015 dex and 0.025 dex, respectively. However, the MZR in Berg et al. (2012) may be systematic lower than the metallicities of our metal-poor galaxies. Assuming the coefficient of , we find most of our galaxies are consistent with the FMR in Andrews & Martini (2013). However, the scatter in FMR is 0.24 dex, lower than the 0.28 dex scatter in MZR, suggesting MZR has a weak dependence on SFR.
Acknowledgements.We are very grateful to the referee’s insightful suggestions and comments, who greatly improve the manuscript for this work. Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences. This work is supported by the Strategic Priority Research Program ”The Emergence of Cosmological Structures” of the Chinese Academy of Sciences (No. XDB09000000), the National Basic Research Program of China (973 Program)(2015CB857004), and the National Natural Science Foundation of China (NSFC, Nos. 11225315, 1320101002, 11433005 and 11421303).
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