1. Jiannan Zhang, Yihan Song, Ali Luo NAOC, CHINA
Automated Stellar Parameters Measurement Technologies from Low-Resolution Spectra Basing on Spectral Line Information
Jiannan Zhang, Yihan Song, Ali Luo
NAOC, CHINA

2. For the vast amounts of spectra produced by the sky survey projects like SDSS, LAMOST, Gaia etc, the accurate stellar fundamental parameters (Teff, log g, [Fe/H]) will play key role for their scientific objects.
The popular parameterization methods for low-resolution spectra include template matching, ANN, line indices methods etc.
Most of these methods usually use whole spectrum information. And the methods using spectral lines information are mainly line indices.
According to the characteristic of the low-resolution spectra, automated spectra analysis methods basing on the spectra lines information will be studied in this work.
1) spectral lines extraction;
2) lines information temple;
3) the selective strategies of the spectral lines which are sensitive to the parameters;
4)regression model between spectral lines and parameters.

3. Popular methods generally consist of three types:
parameter calibrations based on photometric color indices;
parameter calibrations based on absorption line indices;
template matching or statistical learning methods that use spectroscopy.
color indices and line indices: generally applied, robust, may lose information.
Complete spectrum with template matching: easy to use, difficult for continuum normalization, …

4. MAFAGS-OS atmosphere models of the spectral grid provided by F
MAFAGS-OS atmosphere models of the spectral grid provided by F. Grupp(2004 A&A a,b);
Valid for A-, F- and G-type stars.
The same wavelength coverage of SDSS and LAMOST ranging from 300 nm to 1000 nm, and are decreased from
high-resolution spectra to a resolution of R=2000 with a 0.1 nm wavelength sampling step.
The microturbulence velocity is held constant at 2.0 km/s
4697 spectra 4600K~8000K
Steps of 200 K for cool stars, while 500 K for warm stars. The log g parameter ranges from 0.0 dex to 5.0 dex in steps of 0.2 dex, and the metallicity [Fe/H] ranges from 0.0 dex to 4.8 dex in steps of 0.3 dex.

5. Spectral lines extraction:
Apply 25 Lick line indices wavelength definition;
Each Lick line normalized with local pseo- continuum;
MAFAGS Lick line templates grid;

6. Example of 25 Lick lines extraction

7. 25 Lick lines with local continuum normalized

8. The 2nd example of 25 Lick lines extraction

9. 25 Lick lines with local continuum normalized

10. Group 1:
CN_1
CN_2
Hdelta_F
Hdelta_A
Hgamma_A
Hgamma_F
G4300
H_beta
Fe4383
Fe5015
Fe5270
Fe5335
Fe5406
Group 2:
Ca4455
Fe4531
Fe5709
Fe5782
Group 3:
TiO_2
Mg_1
Mg_2
Mg_b
Na_D
TiO_1
Ca4227
Fe4668
Sensitivity check: lick indices variation in parameter space group 1: sensitive group 2: less sensitive group 3: least sensitivity.

11. The Lick indices distribution in parameters space of three lines in group 1. From left to right: G4300, CN_1, Fe4383.

12. Lick indices distribution in parameters space of three lines in group 3. From left to right: Ca4227, Mg_b, TiO_2.

13. Test spectra:
604 spectra from MILES spectra Library
4500K~8000K
FWHM=2.3 Å, wavelength: 3700~7600Å

14. Teff by line method vs. Teff of MILES

15. log g by line method vs. log g of MILES

16. [Fe/H] by line method vs. [Fe/H] of MILES

17. The statistic of Teff, log g and [Fe/H] results that using whole spectrum

18. Summary:
Good Teff and metallicity estimation with Lick line information.
Log g not good, but the scatter of the error is similar with the method with whole spectrum information.
More line information to be included, such as H_alpha, Ca II tri. .

19. Thanks!