SDSS

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SDSS
The Sloan Digital Sky Survey
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Mapping The Universe
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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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Digital Cameras
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CCDs
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CCDs Drift Scan Mode
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The SDSS ATLAS of GALAXIES
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HUBBLE's TUNING FORK DIAGRAM
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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 799 NGC 800
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NGC 428
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UGC 10770
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Measuring Quantities From the Images The Photo
pipeline
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How do you measure brightness?
Most People use Magnitudes m 2.5 Log (flux) C
We use Luptitudes
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OK, but how do you measure flux?
Isophotal magnitudes What we dont do
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OK, but how do we measure flux?
Petrosian Radius Surface brightness Ratio 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
II0 exp -7.67(r/re)1/4
Exponential magnitudes Assume profile associated
with spirals
II0 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 gt 0.145
• Many hundreds of others

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SPECTRA
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An Astronomical Primer of Stellar Spectra
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• O
• B
• A
• F
• G
• K
• M
• L
• T

• h
• e
• ine
• irl/Guy
• iss
• e

Stellar Spectral Types
ong ime
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O
Stellar Spectra
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B
Stellar Spectra
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A
Stellar Spectra
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F
Stellar Spectra
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G
Stellar Spectra
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K
Stellar Spectra
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M
Stellar Spectra
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L
Stellar Spectra
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T
Stellar Spectra
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Cataclysmic Variables
Stellar Spectra
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White Dwarfs
Stellar Spectra
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Carbon Stars
Stellar Spectra
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Stellar Spectra
M star White Dwarf
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Galaxy Spectra
Galaxies Stargas
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Double Galaxy
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QSO spectra
Z0.1
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QSO spectra
Z1
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QSO spectra
Z2
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QSO spectra
Z3
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QSO spectra
Z4
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QSO spectra
Z5
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Mapping The Universe
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Finding Redshifts
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Types of Maps

• Main Galaxy Sample
• LRG sample
• Photo-z sample
• QSO sample
• QSO absorption systems
• Galactic Halo
• Ly-a systems
• Asteroids
• Space Junk

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EDR PhotoZ

• Tamás Budavári
• The Johns Hopkins University

István Csabai Eötvös University, Budapest Alex
Szalay The Johns Hopkins University Andy
Connolly University of Pittsburgh
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Pros and Cons
Template fitting
Empirical method
Comparing known spectra to photometry
Redshifts from calibrators with similar colors
no need for calibrators, physics in
templates more physical outcome, spectral type,
luminosity template spectra are not perfect,
e.g. CWW
quick processing time new calibrator set and
fit required for new data cannot extrapolate,
yields dubious results
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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
  

ugriz
L type 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 gt 0.2 ? ?z 0.026
• Blue sample ?z 0.05
• Continuous type
• Red sample ?z 0.029
• z gt 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 0.05
?z 0.029
?z 0.04
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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

• Scatter is big but
• thats why needed the photoz plates

• The first results
• Galaxies are indeed blue
• and higher redshift!

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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 ?(r)
• The power Spectrum
• N-point Statistics
• Counts in Cells
• Topological measures
• Maximum Likelihood parameter estimation

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Constraining Cosmological Parameters from
Apparent Redshift-space Clusterings
Taka Matsubara Alex Szalay
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Constraining Cosmological Parameters
(Traditional) Quadratic Methods
Redshift Survey Data ? or
?

• Effective for spatially homogeneous, isotropic
  samples.
• However, evaluation of in real
  (comoving) space
• is not straightforward. (z-evolution,
  redshift-space
• distortion)

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Example
Redshift-space
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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)
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Geometric distortions (non-small z)
real space
redshift space
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Likelihood analysis of cosmological parameters
without direct determination of or
(Bayesian)
Linear regime ? Gaussian,
fully determined by a correlation matrix
Huge matrix ? a novel, fast algorithm to
calculate Cij for
arbirtrary z under development
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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
simultaneous determination (marginalized)

Normal 14 57 51 2
Red 9 10 33 0.9
QSO 170 75 360 69
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Summary

• 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

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1.0
0.8
0.6
O?
0.4
0.2
0.0
0.0
1.0
0.2
0.4
0.6
0.8
OM
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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