SDSS
description
Transcript of SDSS
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The SDSS is Two Surveys
The Fuzzy Blob Survey
The Squiggly Line Survey
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The Site
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The telescope•2.5 m mirror
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1.3 MegaPixels
$150
4.3 Megapixels
$850
100 GigaPixels
$10,000,000
Digital Cameras
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CCDs
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CCDs: Drift Scan Mode
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NGC 450
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NGC 1055
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NGC 4437
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NGC 5792
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NGC 1032
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NGC 4753
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NGC 60
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NGC 5492
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NGC 936
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NGC 5750
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NGC 3521
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NGC 2967
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NGC 5719
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UGC 01962
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NGC 1087
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NGC 5334
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UGC 05205
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UGC 07332
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UGCA 285
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Arp 240
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UCG 08584
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NGC 799NGC 800
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NGC 428
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UGC 10770
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Measuring Quantities From the Images:Measuring Quantities From the Images:The Photo pipelineThe Photo pipeline
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Most People use Magnitudesm = –2.5 Log (flux) + C
How do you measure brightness?
We use Luptitudes
m = –2.5 ln (10) [asinh( ) + ln(b)] f/f0
2b
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OK, but how do you measure flux?
Isophotal magnitudes:What we don’t do
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OK, but how do we measure flux?
Petrosian Radius:Surface brightnessRatio =0.2
Petrosion flux:Flux within 2 Petrosian Radii
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Some Other Measures
PSF magnitudes
Fiber magnitudes
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Galaxy Models
de Vaucouleurs magnitudes:assume profile associated with ellipiticals
Exponential magnitudes:Assume profile associated with spirals
I=I0 exp {-7.67[(r/re)1/4]}
I=I0 exp {-1.68(r/re)}
Model magnitudes pick best
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Which Magnitudes to Use?
Photometry of Distant QSOs
PSF magnitudes
Colors of Stars PSF magnitudes
Photometry of Nearby Galaxies
Petrosian magnitudes
Photometry of Distant Galaxies
Petrosian magnitudes
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Other Image Parameters
• Size
• Type
psfMag – expMag > 0.145
• Many hundreds of others
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SPECTRA
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OBAFGKMLT
h
e
ine
irl/Guy
iss
eong
ime
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Galaxy Spectra
Galaxies =Star+gas
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QSO spectra
Z=0.1
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QSO spectra
Z=1
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QSO spectra
Z=2
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QSO spectra
Z=3
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QSO spectra
Z=4
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QSO spectra
Z=5
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Types of MapsTypes of Maps
• Main Galaxy Sample• LRG sample• Photo-z sample• QSO sample• QSO absorption systems• Galactic Halo• Ly-α systems• Asteroids• Space Junk
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EDR PhotoZ
Tamás BudaváriThe Johns Hopkins
University
István Csabai – Eötvös University, Budapest
Alex Szalay – The Johns Hopkins University
Andy Connolly – University of Pittsburgh
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Template fittingTemplate fittingComparing known
spectra to photometry
++ no need for calibrators, physics in templates
++ more physical outcome, spectral type, luminosity
–– template spectra are not perfect, e.g. CWW
Empirical methodEmpirical methodRedshifts from calibrators
with similar colors
++ quick processing time
–– new calibrator set and fit required for new data
–– cannot extrapolate, yields dubious results
Pros and Cons
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Empirical Methods
• Nearest neighbor– Assign redshift of closest calibrator
• Polynomial fitting function– Quadratic fit, systematic errors
• Kd-tree– Quadratic fit in cells
z = 0.033 z = 0.027 z = 0.023
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Template Fitting
• Physical inversion– More than just redshift– Yield consistent spectral type,
luminosity & redshift– Estimated covariances
• SED Reconstruction– Spectral templates that match
the photometry better– ASQ algorithm dynamically
creates and trains SEDs
Ltype
z
u’g’r’i’z’
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Trained LRG Template
• Great calibrator set up to z = 0.5 – 0.6 !
• Reconstructed SED redder than CWW Ell
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Trained LRG Template
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Photometric Redshifts• 4 discrete templates
– Red sample z = 0.028• z > 0.2 z = 0.026
– Blue sample z = 0.05
• Continuous type– Red sample z = 0.029
• z > 0.2 z = 0.035
– Blue sample z = 0.04
• Outliers– Excluded 2% of galaxies
• Sacrifice?– Ell type galaxies have better
estimates with only 1 SED– Maybe a decision tree?
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z = 0.028
z = 0.029 z = 0.04
z = 0.05
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PhotoZ Plates
• The Goal– Deeper spectroscopic sample
of blue SDSS galaxies• Blind test• New calibrator set
• Selection– Based on photoz results– Color cuts to get
• High-z objects• Not red galaxies
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Plate 672
• The first results– Galaxies are indeed
blue
– … and higher redshift!
• Scatter is big but…– … that’s why needed the
photoz plates
LRGs
z = 0.085
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Plate 672
• Redshift distributions compare OK# of g = 519– Photometric redshifts (Run 752 & 756)
– Spectroscopic redshifts (Histogram scaled)
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Measures of the Clustering
• The two point correlation function ξ(r)
• The power Spectrum
• N-point Statistics
• Counts in Cells
• Topological measures
• Maximum Likelihood parameter estimation
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Constraining CosmologicalParameters from Apparent Redshift-space Clusterings
Taka MatsubaraAlex Szalay
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Redshift Survey Data → or →
Constraining Cosmological Parameters
(Traditional) Quadratic Methods
)(kP )(r,... , , , , , , 8BM nbh
• Effective for spatially homogeneous, isotropic samples.• However, evaluation of in real (comoving) space is not straightforward. (z-evolution, redshift-space distortion)
)(kP
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)()2,1( r
2z
1z
12 r
),,()2,1( 1221 zzRedshift-space:
:space-real ,1z
Example:
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Anisotropy of the clustering
Velocity distortions
real space redshift space
Finger-of-God (non-linear scales)
Squashing by infall (linear scales)
pec0 vrHcz
b/6.0
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Geometric distortions (non-small z) real space redshift space
)(zH
)(zd A
)1()1()1()( M2
M3
0 zzHzH
z
A zH
zdH
Hzd
0M0
M0 )(1sinh
1
1)(
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Likelihood analysis of cosmological parameters without direct determination of or)(kP )(r
LLL ii || (Bayesian)
,...,,,,,, , 8M nbhbii
x
Linear regime → : Gaussian, fully determined by a correlation matrix
modeljiijC
Huge matrix ← a novel, fast algorithm to calculate Cij for arbirtrary z : under development
|iL
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Results
single determination
Normal ±3% ±19% ±16% ±4% ±2% ±0.5% ±0.5%
Red ±2% ±4% ±9% ±2% ±1% ±0.3% ±0.4%
QSO ±14% ±15% ±76% ±20% ±14% ±5% ±6%
M MB b8nh
simultaneous determination (marginalized)
Normal ±14% ±57% ±51% ±2%
Red ±9% ±10% ±33% ±0.9%
QSO ±170% ±75% ±360% ±69%
M MB b
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Direct determinations of cosmological parameters
A novel, fast algorithm to calculate correlation matrix in redshift space
Normal galaxies : dense, low-z, small sample volume
QSOs : sparse, high-z, large sample volume
Red galaxies : intermediate → best constraints on cosmological parameters
Summary
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0.00.0 1.0
1.0
0.8
0.6
0.4
0.2
0.2 0.4 0.6 0.8
ΩM
ΩΛ
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Visualization
• CAVE VR system at Argonne National Laboratory
• SDSS VS v. 1.0 Windows based visualization system
• Tool directly tied to the skyserver for general visualization of multi-dimensional data
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Accessing the Data
• Two databases
• Skyserver (MS SQL)– Skyserver.fnal.gov
• SDSSQT– Download from www.sdss.org
• Lab astro.uchicago.edu/~subbarao/chautauqua.html