The SDSS is Two Surveys The Fuzzy Blob Survey The Squiggly Line Survey
The Site
The telescope 2.5 m mirror
Digital Cameras 1.3 MegaPixels $150 4.3 Megapixels $850 100 GigaPixels $10,000,000
CCDs
CCDs: Drift Scan Mode
NGC 450
NGC 1055
NGC 4437
NGC 5792
NGC 1032
NGC 4753
NGC 60
NGC 5492
NGC 936
NGC 5750
NGC 3521
NGC 2967
NGC 5719
UGC 01962
NGC 1087
NGC 5334
UGC 05205
UGC 07332
UGCA 285
Arp 240
UCG 08584
NGC 799 NGC 800
NGC 428
UGC 10770
Measuring Quantities From the Images: The Photo pipeline
How do you measure brightness? Most People use Magnitudes m = 2.5 Log (flux) + C We use Luptitudes m = ln 2.5 (10) [asinh( f/f 0 ) 2b + ln(b)]
OK, but how do you measure flux? Isophotal magnitudes: What we don t do
OK, but how do we measure flux? Petrosian Radius: Surface brightness Ratio =0.2 Petrosion flux: Flux within 2 Petrosian Radii
Some Other Measures PSF magnitudes Fiber magnitudes
Galaxy Models de Vaucouleurs magnitudes: assume profile associated with ellipiticals I=I 0 exp {-7.67[(r/r e ) 1/4 ]} Exponential magnitudes: Assume profile associated with spirals I=I 0 exp {-1.68(r/r e )} Model magnitudes pick best
Which Magnitudes to Use? Photometry of Distant QSOs Colors of Stars PSF magnitudes PSF magnitudes Photometry of Nearby Galaxies Photometry of Distant Galaxies Petrosian magnitudes Petrosian magnitudes
Other Image Parameters Size Type psfmag expmag > 0.145 Many hundreds of others
SPECTRA
O B h e A F G K M ine irl/guy iss e L T ong ime
Galaxy Spectra Galaxies = Star+gas
Z=0.1 QSO spectra
Z=1 QSO spectra
Z=2 QSO spectra
Z=3 QSO spectra
Z=4 QSO spectra
Z=5 QSO spectra
Types of Maps Main Galaxy Sample LRG sample Photo-z sample QSO sample QSO absorption systems Galactic Halo Ly-_ systems Asteroids Space Junk
EDR PhotoZ Tamás Budavári The Johns Hopkins University István Csabai Eötvös University, Budapest Alex Szalay The Johns Hopkins University Andy Connolly University of Pittsburgh
Pros and Cons Empirical method Redshifts from calibrators with similar colors + quick processing time new calibrator set and fit required for new data cannot extrapolate, yields dubious results Template fitting Comparing 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 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
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 L type z u g r i z
Trained LRG Template Great calibrator set up to z = 0.5 0.6! Reconstructed SED redder than CWW Ell
Trained LRG Template
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?
z = 0.028 z = 0.05 z = 0.029 z = 0.04
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
Plate 672 The first results Galaxies are indeed blue and higher redshift! LRGs z = 0.085 Scatter is big but that s why needed the photoz plates
Plate 672 Redshift distributions compare OK# of g = 519 Photometric redshifts (Run 752 & 756) Spectroscopic redshifts (Histogram scaled)
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
Constraining Cosmological Parameters from Apparent Redshift-space Clusterings Taka Matsubara Alex Szalay
Constraining Cosmological Parameters (Traditional) Quadratic Methods Redshift Survey Data (k) or Ω P ξ(r) M, ΩΛ, ΩB, h, σ 8, b, n,... Effective for spatially homogeneous, isotropic samples. However, evaluation of P(k) in real (comoving) space is not straightforward. (z-evolution, redshift-space distortion)
Example: z 2 θ12 r z 1 z <<1, real -space : ξ( 1,2) = ξ( r) Redshift-space: ξ( 1,2) = ξ( z1, z2, θ12)
Anisotropy of the clustering Velocity distortions cz = H r + 0 v pec real space redshift space Finger-of-God (non-linear scales) Squashing by infall (linear scales) 0.6 Ω / b
Geometric distortions (non-small z) real space redshift space H (z) d A (z) H ( 0 M z M 3 2 z) = H (1 + z) Ω + (1 + ) (1 Ω Ω ) + Λ Ω Λ d A ( z) 1 = sinh H 0 1 ΩM ΩΛ H 1 Ω Ω dz H ( z 0 ) 0 M Λ z
Likelihood analysis of cosmological parameters without direct determination of P (k or) ξ(r) L δ i ( θ δ ) L( δ θ ) L( θ ) = α δρ ρ (Bayesian) i i α ( x ) ( θ ) = ( Ω, Ω, Ω, h, σ, b, n,... ) i, α M Λ b 8 Linear regime i : Gaussian, fully determined by a correlation matrix C ij L ( θα) = δiδ j model( ) Huge matrix a novel, fast algorithm to calculate Cij for arbirtrary z : under development α ( δ ) θ α θ α
Results single determination ±6% ±5% ±14% ±20% ±76% ±15% ±14% QSO ±0.4% ±0.3% ±1% ±2% ±9% ±4% ±2% Red ±0.5% ±0.5% ±2% ±4% ±16% ±19% ±3% Normal M Ω Λ Ω M B Ω Ω b σ 8 n h simultaneous determination (marginalized) ±69% ±360% ±75% ±170% QSO ±0.9% ±33% ±10% ±9% Red ±2% ±51% ±57% ±14% Normal M Ω Λ Ω M B Ω Ω b
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
1.0 0.8 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 _ M
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
Accessing the Data Two databases Skyserver (MS SQL) Skyserver.fnal.gov SDSSQT Download from www.sdss.org Lab astro.uchicago.edu/~subbarao/chautauqua.html