The SDSS is Two Surveys

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3 The SDSS is Two Surveys The Fuzzy Blob Survey The Squiggly Line Survey

4 The Site

5 The telescope 2.5 m mirror

6 Digital Cameras 1.3 MegaPixels $ Megapixels $ GigaPixels $10,000,000

7 CCDs

8 CCDs: Drift Scan Mode

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11 NGC 450

12 NGC 1055

13 NGC 4437

14 NGC 5792

15 NGC 1032

16 NGC 4753

17 NGC 60

18 NGC 5492

19 NGC 936

20 NGC 5750

21 NGC 3521

22 NGC 2967

23 NGC 5719

24 UGC 01962

25 NGC 1087

26 NGC 5334

27 UGC 05205

28 UGC 07332

29 UGCA 285

30 Arp 240

31 UCG 08584

32 NGC 799 NGC 800

33 NGC 428

34 UGC 10770

35 Measuring Quantities From the Images: The Photo pipeline

36 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)]

37 OK, but how do you measure flux? Isophotal magnitudes: What we don t do

38 OK, but how do we measure flux? Petrosian Radius: Surface brightness Ratio =0.2 Petrosion flux: Flux within 2 Petrosian Radii

39 Some Other Measures PSF magnitudes Fiber magnitudes

40 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

41 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

42 Other Image Parameters Size Type psfmag expmag > Many hundreds of others

43 SPECTRA

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45 O B h e A F G K M ine irl/guy iss e L T ong ime

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59 Galaxy Spectra Galaxies = Star+gas

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61 Z=0.1 QSO spectra

62 Z=1 QSO spectra

63 Z=2 QSO spectra

64 Z=3 QSO spectra

65 Z=4 QSO spectra

66 Z=5 QSO spectra

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69 Types of Maps Main Galaxy Sample LRG sample Photo-z sample QSO sample QSO absorption systems Galactic Halo Ly-_ systems Asteroids Space Junk

70 EDR PhotoZ Tamás Budavári The Johns Hopkins University István Csabai Eötvös University, Budapest Alex Szalay The Johns Hopkins University Andy Connolly University of Pittsburgh

71 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

72 Empirical Methods Nearest neighbor Assign redshift of closest calibrator Polynomial fitting function Quadratic fit, systematic errors Kd-tree Quadratic fit in cells z = z = z = 0.023

73 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

74 Trained LRG Template Great calibrator set up to z = ! Reconstructed SED redder than CWW Ell

75 Trained LRG Template

76 Photometric Redshifts 4 discrete templates Red sample z = z > 0.2 z = Blue sample z = 0.05 Continuous type Red sample z = z > 0.2 z = 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?

77 z = z = 0.05 z = z = 0.04

78 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

79 Plate 672 The first results Galaxies are indeed blue and higher redshift! LRGs z = Scatter is big but that s why needed the photoz plates

80 Plate 672 Redshift distributions compare OK# of g = 519 Photometric redshifts (Run 752 & 756) Spectroscopic redshifts (Histogram scaled)

81 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

82 Constraining Cosmological Parameters from Apparent Redshift-space Clusterings Taka Matsubara Alex Szalay

83 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)

84 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)

85 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

86 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

87 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

88 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

89 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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91 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

92 Accessing the Data Two databases Skyserver (MS SQL) Skyserver.fnal.gov SDSSQT Download from Lab astro.uchicago.edu/~subbarao/chautauqua.html