Transcript Introduction of 1D software of LAMOST

The KIAA-Cambridge Joint Workshop on
Near-Field Cosmology and Galactic Archeology
The LAMOST 1d Spectroscopic
Pipeline
A-Li LUO
LAMOST team, NAOC
2008/12/3
Lessons from SDSS
• Three 1d pipelines of SDSS (template based )
Princeton 1d; Fermi 1d; SEGUE: SSPP
• Have been improving from DR1->DR7
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
Software Structure
Measurement
Classification
Preprocessing
Image processing &
Modular
Modular
Modular
Spectra extraction
ODBC/JDBC
Interface
QL
DBMS
File Management
System
Database Management
Interface
Storage
&distribution
CCD Raw Data
Production
1. Catalogs
2. Calibrated spectra with analysis results
AGN
Galaxies
Galaxies (z)
Normal galaxies
Stars
QSOs
Input Catalog
Results:
Starburst
Stars (rv)
Emission Line stars
O B Stars
QSOs (z)
A F G K Stars
M or later Stars
Unknown
Reflection Nebulae
Basic Production
Reference classification
Supernovae
Search
H II
Identification
StellarAtmosphericParameters
MultiWavelength
Identification
Candidate
Catalogue
Comparison between object type
and spectral class in SDSS DR5
UNKNOWN
STAR
GALALY
QSO
HIZ_QSO
STAR
-LATE
Object
type
Total
number
Correct
(after
spectral
Identify)
False
(after
spectral
Identify)
QSO
112147
58562
52.219%
53585
47.781%
3077
2.744%
22975
20.487%
16716
17.905%
53855
48.022%
4707
4.197%
10817
9.645%
GALAXY
565267
548789
97.085%
16478
2.915%
3403
0.602%
7333
1.297%
548789
97.085%
1179
0.209%
3
0.00053%
4560
0.807%
STAR
29595
28991
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
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
Identification automatically
Extracted Spectra
Absorption
band detection
Yes
Late type stars (M type)
with bands (TiO etc)
No
Emission line at
6563±20A,
4860±20A,
4340±20A ?
Continuum fitting
No
No line spectra
Lines
detection
Emission Line
Spectra ?
No
Starburst
AGN or
QSO etc.
Yes
Yes
S/N
low?
No
Continuum
High or low ?
Low
Redshift measurement
Yes
High
H II Region
No
BL LAC or high
Z galaxies
O_III 5007, H_alpha H_beta
Absorption lines of
Absorption lines at
6563±20A, 4860±20A,
4340±20A ?
No
NII 6583 measurement
NaI, Mgb and CaII
etc
He II lines
No
Normal
galaxies
Yes
O or early
B type star
Yes
No
Star
forming
galaxies
Star
burst
galaxies
QSO &
Seyfert I
Yes
A,F,G, early K star or
Reflection Nebular
Late type emission
line star + CSM
Early type emission
line star + CSM
Seyfert II
LINER
STELLAR ANALYSIS PIPELINE
A, F,G, K type
stellar spectra
Continuum Rectification
Rough model spectra grid
Teff~500K, logg~1.0dex,
[Fe/H]~1.0 dex
Line index definition
H_delta, H_zeta, ,
CaII triplet, H&K,
G band
BAD
Health Check?
Sub-grid model spectra
Teff~100K, logg~0.25dex
[Fe/H]~0.25dex
Best fit rough spectra
Cross-correlation
Vrad geo
Vrad geo Correction
Cross-correlation
Best fit spectra
±10-20 km/s
Line Index Measure
GOOD
Line index & Color
index calibration
(ANN, Polynomial)
Optimization
of different
methods
Color index
from Input
Catalog
[Fe/H]
[C/Fe]
Teff
logg
Absolute Magnitude
trash bin
High Resolution Spectra for example.
HERES: 372 stars
(VLT/UVES) R=20000
S/N=50
Visual Magnitude
distance
Line Indices
• To determine the local continuum level
• Width selection
Some lines used in the pipeline
•
•
•
•
•
•
CaII K line (3933A)
Balmer lines
CaII triplet
Mg I b
G band and [C/Fe]
Colors
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).
Balmer lines ~ Teff
Three Balmer lines in Kurucz model spectra
Hγ (434.0 nm)
[Fe/H]
=-3.0
[Fe/H]
=-1.0
[Fe/H]
=-2.0
[Fe/H]
=0
Hδ (410.2 nm)
[Fe/H]=-3.0
[Fe/H]=-1.0
[Fe/H]=-2.0
[Fe/H]=0
Hζ (388.9 nm)
[Fe/H]=-3.0
[Fe/H]=-1.0
[Fe/H]=-2.0
[Fe/H]=0
• Hδ and Hζ in CFLIB spectra are
obvious correlated with Teff.
• Since the resolution of 1 Å FWHM of
CFLIB and low S/N in the range around
Hζ for half of the CFLIB dataset, Hζ line
in 3889 Å is difficult to measure.
• Fitting Teff ~ Hδ :
Teff = 4572.813 + 546.716×Hδ − 53.773×Hδ2
error:100-200K
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.
MgI b ~ gravity
(left) SDSS data,
(right) ELODIE data.
G band ~ [C/Fe]
Relationship between G band and [C/Fe] with HES
follow up spectra
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)
Structure of the stellar analysis
pipeline
Independent compiled module +script
Already completed module list:
Kurucz model Continuum fitting ANN
calculation
(whole range)
Module
Spectra
synthesize
Continuum fitting Interpolation
(local range)
module
Cross
correlation
Line index
calculation
Regression
module
EW calculation
module
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
Test with Elodie library
Accuracy of the parameters
• Checked with SEGUE dr6 data
Accuracy of parameters with different SNR
Thanks !