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 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

Template Fitting methods Training Set 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) Hyper-z (Bolzonella et al. 2000) BPZ (Benitez 2000) Neural Network (Firth, Lahav & Somerville 2003; Collister & Lahav 2004) Cross correlations (Newman)

Statistical measure of shear pattern, ~1% distortion 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 Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes). Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect. Statistical measure of shear pattern, ~1% distortion Radial distances depend on geometry of Universe Foreground mass distribution depends on growth of structure

Statistical measure of shear pattern, ~1% distortion 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 Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes). Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect. 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: 1.2m 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 4/14/2019

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 (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%

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 contributions What we measure Cosmic shear

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

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.

Different Cl contributions: Bridle & King

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 Cypriano, Lahav & Rhodes Abdalla, Amara, Capak High demand on photo-z for intrinsic alignement calibration Bridle & King

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:

Conclusions Today dw=1/10 prospect: 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.