Cosmology with Photometric redsfhits Filipe Batoni Abdalla M

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 • Results from the Fisher analysis on the mocks • More problems: Intrinsic alignements • Sensitivity of weak lensing to w(z)

Photometric Redshifts • Photometric redshifts (photo-z’s) are determined from the fluxes of galaxies through a set of filters • May be thought of as lowresolution 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

Training Set Methods • Determine functional relation • Examples Nearest Neighbors (Csabai et al. 2003) Polynomial (Connolly et al. 1995) Template Fitting methods • Use a set of standard SED’s • • Polynomial Nearest Neighbors (Cunha et al. in prep. 2005) Neural Network (Firth, Lahav & Somerville 2003; Collister & Lahav 2004) • templates (CWW 80, etc. ) Calculate fluxes in filters of redshifted templates. Match object’s fluxes ( 2 minimization) Outputs type and redshift • Bayesian Photo-z Hyper-z (Bolzonella et al. 2000) BPZ (Benitez 2000) Cross correlations (Newman)

Weak Lensing: Cosmic Shear Background sources Dark matter halos Observer n n n Statistical measure of shear pattern, ~1% distortion Radial distances depend on geometry of Universe Foreground mass distribution depends on growth of structure

Weak Lensing: Growth Background sources dependence Dark matter halos Observer n n n Statistical measure of shear pattern, ~1% distortion Radial distances depend on geometry of Universe Foreground mass distribution depends on growth of structure

DUNE: Dark UNiverse Explorer Mission baseline: • 1. 2 m telescope • FOV 0. 5 deg 2 • PSF FWHM 0. 23’’ • Pixels 0. 11’’ • GEO (or HEO) orbit Surveys (3 -year initial programme): • WL survey: 20, 000 deg 2 in 1 red broad band, 35 galaxies/amin 2 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 9/17/2020 spectrograph on the near-IR plane 7

Surveys considered: galaxies with RIZ<25 considered

JPL Simulated catalogue Av Type z

Know the requirements: Catastrophic outliers Biases Uninformative region Abdalla et al. astro-ph: 0705. 1437 • A case study: the DUNE satellite • I have performed analysis within the DES framework as well: VDES

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

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.

Intrinsic alignements. Additional What we contributions measure Cosmic shear

Galaxy at z 1 is tidally sheared Dark matter at z 1 Hirata & Seljak Intrinsic-shear correlation (GI) Net anti-correlation between galaxy High z galaxy gravitationally ellipticities with no sheared tangentially preferred scale

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.

Bridle & King Different Cl contributions:

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

PCA and Fisher Information Matrix • Fisher Information Matrix is an efficient method to measure the • • • covariance of the random variables Fisher information matrix F 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

Redshift information in e-modes:

• Today dw=1/10 prospect: dwxdw’=1/160 but there is a big demand on photometric redshifts, specially for future Conclusions 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.