Automated Stellar Parameters Measurement from Low-Resolution Spectra Using Spectral Line Information

1. 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. 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 parameterranges 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 spacegroup 1: sensitivegroup 2: less sensitivegroup 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!