1. The LAMOST 1d Spectroscopic Pipeline A-Li LUO LAMOST team, NAOC 2008/12/3 The KIAA-Cambridge Joint Workshop on Near-Field Cosmology and Galactic Archeology

2. Lessons from SDSS Three 1d pipelines of SDSS ( template based ) Princeton 1d; Fermi 1d; SEGUE: SSPP Have been improving from DR1->DR7

3. Task of 1D pipeline Classification and Identification Measurement (z of galaxies and QSOs, rv of stars) Stellar parameter estimation Special Candidate searching (Supernovae, Metal-poor stars, HII …) – according to requirements of astronomers

4. Software Structure Measurement Modular Classification Modular Preprocessing Modular File Management System ODBC/JDBC Interface DBMS CCD Raw Data Database Management Interface QL Storage &distribution Image processing & Spectra extraction

5. Production Galaxies Stars QSOs Input Catalog Galaxies (z) Stars (rv) QSOs (z) Unknown Basic Production AGN StarburstSupernovae Search Emission Line stars H II Identification O B Stars M or later Stars A F G K Stars Reflection Nebulae Reference classification Stellar- Atmospheric- Parameters Normal galaxies Multi- Wavelength Identification Candidate Catalogue 1. Catalogs 2. Calibrated spectra with analysis results Results:

6. Comparison between object type and spectral class in SDSS DR5 Object type Total number Correct (after spectral Identify) False (after spectral Identify) UNKNOWNSTARGALALYQSOHIZ_QSOSTAR -LATE QSO 11214758562 52.219% 53585 47.781% 3077 2.744% 22975 20.487% 16716 17.905% 53855 48.022% 4707 4.197% 10817 9.645% GALAXY 565267548789 97.085% 16478 2.915% 3403 0.602% 7333 1.297% 548789 97.085% 1179 0.209% 3 0.00053% 4560 0.807% STAR 2959528991 97.959% 604 2.04% 175 0.591% 13426 45.366% 52 0.176% 217 0.733% 160 0.54% 15565 52.593% -- object type -- spectral class

7. Classification algorithm Automated Classification by objective methods (training by templates, predicting by distance or density ), collaborators: IA(CAS), BNU,SDU, etc. Identified by line measurement

8. Identification automatically Extracted Spectra Late type stars (M type) with bands (TiO etc) Normal galaxies Absorption band detection Lines detection Emission Line Spectra ? Absorption lines at 6563±20A, 4860±20A, 4340±20A ? He II lines Continuum fitting Emission line at 6563±20A, 4860±20A, 4340±20A ? Continuum High or low ? Absorption lines of NaI, Mgb and CaII etc O_III 5007, H_alpha H_beta NII 6583 measurement Star forming galaxies Star burst galaxies QSO & Seyfert I Seyfert IILINER Early type emission line star + CSM O or early B type star A,F,G, early K star or Reflection Nebular Late type emission line star + CSM Redshift measurement Starburst AGN or QSO etc. NoYes H II Region No Low High Yes No Yes No line spectra No Yes S/N low? No BL LAC or high Z galaxies No Yes

9. STELLAR ANALYSIS PIPELINE GOODBAD A, F,G, K type stellar spectra Continuum Rectification Cross-correlation V rad geo Correction V rad geo Line Index Measure Line index definition H _delta, H _zeta,, CaII triplet, H&K, G band trash bin Health Check ? Line index & Color index calibration (ANN, Polynomial) High Resolution Spectra for example. HERES: 372 stars (VLT/UVES) R=20000 S/N=50 ±10-20 km/s Color index from Input Catalog Rough model spectra grid Teff~500K, logg~1.0dex, [Fe/H]~1.0 dex Best fit rough spectra [Fe/H] [C/Fe] Teff logg distance Optimization of different methods Cross-correlation Best fit spectra Visual Magnitude Absolute Magnitude Sub-grid model spectra Teff~100K, logg~0.25dex [Fe/H]~0.25dex

10. Line Indices To determine the local continuum level Width selection

11. Some lines used in the pipeline CaII K line (3933A) Balmer lines CaII triplet Mg I b G band and [C/Fe] Colors

12. CaII K ~ [Fe/H] Relationship between [Fe/H] and CaII K in 4500K,5000K,5500K,6000K,6500K,7000K and 7500K respectively (Marcs model synthetic spectra). Lines (left) and 2 order polynomial (right) are used to fit the relationships from low to high temperature. Relationships between [Fe/H] and the strength of CaII K in SDSS/SEGUE (Dr6).

13. Balmer lines ~ Teff [Fe/H] =-3.0 [Fe/H] =-2.0 [Fe/H] =-1.0 [Fe/H] =0 [Fe/H]=-2.0[Fe/H]=-3.0 [Fe/H]=-1.0 [Fe/H]=0 Hγ (434.0 nm)Hδ (410.2 nm) Hζ (388.9 nm) [Fe/H]=-3.0 [Fe/H]=-2.0 [Fe/H]=0 [Fe/H]=-1.0 Three Balmer lines in Kurucz model spectra H δ and H ζ in CFLIB spectra are obvious correlated with T eff. Since the resolution of 1 Å FWHM of CFLIB and low S/N in the range around H ζ for half of the CFLIB dataset, H ζ line in 3889 Å is difficult to measure. Fitting T eff ~ H δ : Teff = 4572.813 + 546.716×Hδ − 53.773×Hδ 2 error:100-200K

14. CaII triplet Fitting of relationship between CaII triplet and Teff, [Fe/H], and logg respectively, CFLIB spectra were used as experimental dataset Relationship between CaII triplet and [Fe/H], EW of all Ca II triplet of SDSS/SEGUE spectra are plotted in left panel, and [Fe/H] varies with CaII triplet when T = 5000K, logg = 2.0 in right panel.

15. MgI b ~ gravity (left) SDSS data, (right) ELODIE data.

16. G band ~ [C/Fe] Relationship between G band and [C/Fe] with HES follow up spectra

17. Color ~ Teff Temperature varies with B-V Color in CFLIB dataset For SDSS, in the range -0.3 < g-r <1.0, the following expression provides the effective temperature with an rms only 2% (100-200K) (Ivezić et al 2006)

18. Structure of the stellar analysis pipeline Independent compiled module +script Already completed module list: Kurucz model calculation Continuum fitting (whole range) ANN Module Regression module Spectra synthesize Continuum fitting (local range) Interpolation module Cross correlation Line index calculation EW calculation module

19. Kurucz model calculation Atlas9 Kurucz/Castelli LTE NewODF Intermod: an interpolation program to quickly generate intermediate models from an initial grid Spectra Synthesize Synthe Spectrum Gray

20. Test with Elodie library

21. Accuracy of the parameters Checked with SEGUE dr6 data

22. Accuracy of parameters with different SNR

23. Thanks !