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Cosmology with Photometric redsfhits

Cosmology with Photometric redsfhits. Filipe Batoni Abdalla. M. Banerji, S. Bridle, E. Cypriano, O. Lahav, J Tang, J Weller (UCL), A. Amara (Saclay), P. Capak, J. Rhodes (Caltech/JPL), H. Lin (Chicago). Outline:. Quick pass over photo-z & weak lensing The DUNE mock catalogues

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Cosmology with Photometric redsfhits

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  1. Cosmology with Photometric redsfhits Filipe Batoni Abdalla M. Banerji, S. Bridle, E. Cypriano, O. Lahav, J Tang, J Weller (UCL), A. Amara (Saclay), P. Capak, J. Rhodes (Caltech/JPL), H. Lin (Chicago)

  2. Outline: • Quick pass over photo-z & weak lensing • The DUNE mock catalogues • Results from the Fisher analysis on the mocks • More problems: Intrinsic alignements • Sensitivity of weak lensing to w(z)

  3. Photometric Redshifts • Photometric redshifts (photo-z’s) are determined from the fluxes of galaxies through a set of filters • May be thought of as low-resolution spectroscopy • Photo-z signal comes primarily from strong galaxy spectral features, like the 4000 Å break, as they redshift through the filter bandpasses • All key projects depend crucially on photo-z’s • Photo-z calibrations will be • optimized using both simulated catalogs and images. Galaxy spectrum at 3 different redshifts, overlaid on griz and IR bandpasses

  4. Hyper-z (Bolzonella et al. 2000) BPZ (Benitez 2000) Training Set Methods Template Fitting methods • Use a set of standard SED’s - templates (CWW80, etc.) • Calculate fluxes in filters of redshifted templates. • Match object’s fluxes (2 minimization) • Outputs type and redshift • Bayesian Photo-z • Determine functional relation • Examples Nearest Neighbors (Csabai et al. 2003) Polynomial Nearest Neighbors (Cunha et al. in prep. 2005) Polynomial (Connolly et al. 1995) Neural Network (Firth, Lahav & Somerville 2003; Collister & Lahav 2004) Cross correlations (Newman)

  5. Background sources Background sources Background sources Background sources Dark matter halos Dark matter halos Dark matter halos Dark matter halos Dark matter halos Observer Observer • Statistical measure of shear pattern, ~1% distortion • Radial distances depend on geometry of Universe • Foreground mass distribution depends on growth of structure

  6. Background sources Background sources Background sources Background sources Dark matter halos Dark matter halos Dark matter halos Dark matter halos Dark matter halos Observer Observer • Statistical measure of shear pattern, ~1% distortion • Radial distances depend on geometry of Universe • Foreground mass distribution depends on growth of structure

  7. DUNE: Dark UNiverse Explorer • Mission baseline: • 1.2m telescope • FOV 0.5 deg2 • PSF FWHM 0.23’’ • Pixels 0.11’’ • GEO (or HEO) orbit • Surveys (3-year initial programme): • WL survey: 20,000 deg2 in 1 red broad band, 35 galaxies/amin2 with median z ~ 1, ground based complement for photo-z’s • Near-IR survey (Y,J,H). Deeper than possible from ground. Secures z > 1 photo-z’s • Changes are currently being discussed at ESA: i.e. merging of DUNE and SPACE • (we will hear more about this in Talks thurs Rassat/Guzzo), inclusing of a small spectrograph on the near-IR plane

  8. Surveys considered: galaxies withRIZ<25 considered

  9. JPL Simulated catalogue Av Type z

  10. A case study: the DUNE satellite I have performed analysis within the DES framework as well: VDES Catastrophic outliers Biases Uninformative region Know the requirements: Abdalla et al. astro-ph:0705.1437

  11. Mock dependence: comparison to DES mocks. DES (grizY) DES+VISTA(JHK) M. Banerji, F. B. Abdalla, O. Lahav, H. Lin et al. In regions of interest photo-z are worst by 30%

  12. FOM: Results &Number of spectra needed • FOM prop 1/ dw x dw’ • IR improves error on DE parameters by a factor of 1.3-1.7 depending on optical data available • If u band data is available improvement is minimal • Number of spectra needed to calibrate these photo-z for wl is around 10^5 in each of the 5 redshift bins • Fisher matrix analysis marginalizing over errors in photo-z.

  13. Intrinsic alignements. Additional contributions What we measure Cosmic shear

  14. Intrinsic-shear correlation (GI) Galaxy at z1 is tidally sheared Hirata & Seljak Dark matter at z1 Net anti-correlation between galaxy ellipticities with no preferred scale High z galaxy gravitationally sheared tangentially

  15. Removing intrinsic alignments: • Finding a weighting function insensitive of shape-shear correlations. (P. Schneider) - Is all the information still there? • Modelling of the intrinsic effects (Bridle & King.) - FOM definitely will decreased as need to constrain other parameters in GI correlations. • Using galaxy-shear correlation function. • In any case there will be the need of a given photometric redshift accuracy.

  16. Different Cl contributions: Bridle & King

  17. Are photo-zs good enough? • The FOM is a slow function of the photo-z quality if we consider only the shear-shear term. • If we consider modelling the shape-shear correlations this is not the case anymore. • This does not include the galaxy-shear correlation function so “reality” is most likely in between this “pessimistic” result and the optimistic result of neglecting GI Abdalla, Amara, Capak Cypriano, Lahav & Rhodes High demand on photo-z for intrinsic alignement calibration Bridle & King

  18. PCA and Fisher Information Matrix • Fisher Information Matrix is an efficient method to measure the covariance of the random variables • Fisher information matrixF is defined as • To combine different experiments F=F_1+F_2 • To marginalize over parameters • We include a parameter set combined with cosmological parameters, w and other nuisance parameters • In the e-vector basis, w is reconstructed as For more details see posted by Tang, Where she reproduced all the DETF report work using w binning + e-modes formalism

  19. Redshift information in e-modes:

  20. Conclusions • Today dw=1/10prospect: dwxdw’=1/160 but there is a big demand on photometric redshifts, specially for future surveys such as DUNE alone. • Need of around 10^5 spectra in ~5 redshift bins • Removing poor photo-z is possible, removes systematic effects and does not hit the statistical limits of certain surveys. • IR data can significantly improve FOM form 1.3 to 1.7 • Importance of the u band filter, potentially being as important as the IR. • It is possible to measure intrinsic alignments with spectroscopic redshift surveys, need to assess it that is possible with photo-z. • Map the redshift sensitivity to w for future wl surveys.

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