Weak lensing tomography: the good, the bad and the ugly Photo-z The Method Intrinsic Alignements Filipe Batoni Abdalla Leverhulme Fellow M. Banerji, E. Cypriano, S. Bridle, O. Lahav (UCL), Chris Blake (Swinburne), Rachel Mandelbaum (IAS) , A. Amara (Saclay), P. Capak, J. Rhodes (Caltech/JPL), S. Rawlings (Oxford)

Cosmology: Concordance Model Heavy elements 0.03% Neutrinos 0.3% Stars 0.5% H + He gas 4% Dark matter 20% Dark Energy 75% Outstanding questions: initial conditions (inflation?) nature of the dark matter nature of the dark energy  Science goals for any weak lensing project 11/11/2018

It has been ~10 years! -> LCDM Universe is flat(ish), dark energy exists: Empty DA~10 kpc/arcsec Flat DA~0.05 kpc/arcsec Angle q = s / DA CMB -> Universe is flat High z supernovae -> accelerated expansion Other probes such confirm this standard model: - Integrates Sachs Wolf Effect - Galaxy power spectrum - Clusters - Weak lensing results.

Dark Energy: Stress Energy vs. Modified Gravity Stress-Energy: G = 8G [T(matter) + T(new)] Gravity: G + f(g) = 8G T(matter) To distinguish between these choices, we must have probes of both the geometry and the growth of large-scale structure. Vacuum Energy: (special case, c.f. Einstein) vac =  / 8G pvac = – vac vac = /3H02 vac ~ 0.7 <--> vac ~ (0.001 eV)4 w = -1 Undesirable for theoretical reasons

The Good: The Methods statistical potential

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

Just one equation from GR ^ ^ O ^  = 4 G M / (c2 b) NB. Independent of light wavelength ^ ^

Apparent deflection angle α

Cosmic shear two point tomography  

Cosmic shear two point tomography q  

Cosmic shear two point tomography q

Cosmic Shear & Weak Lensing Tomography • Measure shapes for millions source galaxies with z ~ 0.8 • Shear-shear & galaxy-shear correlations probe distances & growth rate of perturbations • Requirements: Sky area, depth, photo-z’s, image quality & stability Photo-z connection Huterer

The Bad: The photo-z connection

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 Determine functional relation 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 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) Hyper-z (Bolzonella et al. 2000) BPZ (Benitez 2000)

ANNz - Artificial Neural Network z = f(m,w) Output: redshift Input: magnitudes Collister & Lahav 2004 http://www.star.ucl.ac.uk/~lahav/annz.html

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 SNe survey: 2 x 60 deg2, observed for 9 months each every 4 days in 6 bands, 10000 SNe out to z ~ 1.5, ground based spectroscopy 11/11/2018

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

Number of spectra needed

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.

Cleaned catalogues: Method: Motivation: Remove systematic effects associated to catastrophic outliers

Effect on the dark energy measurements: Can clean a catalogue without degrading dark energy measurements In a cleaned catalogue systematic effects such as intrinsic alignments will be smaller An error of dw x dw’=1/160 can be achieved

The Ugly: Intrinsic Alignements

Intrinsic alignements. Additional contributions What we measure Cosmic shear

Intrinsic Alignments (IA) Intrinsic Alignments (IA) Effect on cosmic shear of changing w by 1% What we measure Cosmic shear Additional contributions To remove these we need good photometric redshfits Cosmic Shear Intrinsic Alignments (IA) Intrinsic Alignments (IA) Intrinsic Alignments (IA) Could bias w results by 100% Normalised to Super-COSMOS Heymans et al 2004

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

GI alignements: Bridle & Abdalla

Different Cl contributions: Bridle & King

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.

Intrinsic-shear correlation (GI) and the galaxy-shear correlation Galaxy at z1 is tidally sheared Dark matter at z1 With position shear correlation one can know how much alignement there is High z galaxy gravitationally sheared tangentially

Measurements of intrinsic alignments using photo-z: Can measure intrinsic alignments with shear-position correlation function. Currently: 13000 2SLAQ gals Proposal: 1400000 MegaZ-LRG gals Probe z evolution Collaborating with S. Bridle, C. Blake and R. Mandelbaum. Mandelbaum et al. 05

Another way -> Modelling: Are photo-zs good enough? Cypriano, Lahav & Rhodes Abdalla, Amara, Capak High demand on photo-z for intrinsic alignement calibration Bridle & King

Blake, Abdalla, Bridle, Rawlings 04 Explore other routes to weak lensing: Blake, Abdalla, Bridle, Rawlings 04 PSF known. Redshifts are spectroscopic Given spectroscopy: Intrinsic alignments easier to remove, smaller systematic effect. But: is it feasible in practice. Requires: (i) good image quality and low systematics for measuring shear; (ii) source density (iii) wide-field to beat down cosmic variance (particularly away from strongly non-linear scales); (iv) lensing tomography.

Conclusions Weak lensing is an important probe of cosmology. Today dw=1/10 prospect: dwxdw’=1/160 but there is a big demand on photometric redshifts, specially for future surveys such as DUNE. 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. Future radio surveys will have much lass problems, i.e. no photo-z issues, less GI - II issues. But is this feasible?