WL and BAO Surveys and Photometric Redshifts

Transcription

1 WL and BAO Surveys and Photometric Redshifts Lloyd Knox University of California, Davis Yong-Seon Song (U Chicago) Tony Tyson (UC Davis) and Hu Zhan (UC Davis) Also: Chris Fassnacht, Vera Margoniner and David Wittman

2 Outline How WL Probes Cosmology Photometric Redshift Challenge Strategies for Meeting the Challenge Combined WL + BAO Forecasts

3 Distance (and growth) reconstructed from LSST WL survey + Planck Knox, Song & Tyson (2005) With the parameters of the highz Universe pinned down by Planck, only thing left to measure is g(z) and D A (z) (here called r(z)) in the dark energy-dominated era *. They can both be reconstructed from tomographic cosmic shear data. D.E. constraints come almost entirely from D A (z) constraints (Simpson & Bridle 04, KST05). Sensitivity to D A comes from matter-radiation equality feature at k = H EQ -1. * exceptions: e.g. neutrinos

4 Need Redshifts WL shear power spectra do not depend on any redshifts (neither sources nor lenses) only on D os, D ol and D ls. But we want to constrain cosmology via D(z)! Without source z s we are stuck with weak constraints that would come from D(g) (Zhan & Knox 05). The need for z (and for photo-z) exposes otherwise very clean WL probe to risks of astrophysical modeling.

5 Bernstein and Jain 2003, Ma et al. 2005, Huterer et al Redshift Error Tolerance Can tolerate large errors on z for individual objects, but need to know P(z) very well. For D A (z) / z want σ(<z>)/<z> < 0.3 σ(d)/d (in order to be subdominant) If σ(d)/d = 0.01 need σ(<z>) < <z> <z> is not exactly desired quantity, but some weighted average of z. This means need to also know σ(z) just as well as we know <z>. Training set size: σ(σ(z)) = σ(z) (1/(2N spec )) 1/2 and σ(<z>) = σ(z)/(1/n spec ) 1/2 N ~ 1000 per z bin or ~10 4 total (for 1% distance errors, assuming σ(z) = 0.1). N larger due to non-gauss.

6 Spectroscopy of Faint Objects is Very Hard Steidel et al. (2003) get redshifts down to m=25.5 on Keck (but these are pre-selected to have strong emission lines fair sample concerns) LSST high source densities achieved by going to m=26.5 Due to night sky lines, we need Subaru + WFMOS in space (to solve the problem directly)!

7 Strategies for Meeting the Photoz Challenge Indirectly Use sub samples of galaxies as the source redshift population (to address fair sample concerns). Use galaxy clustering to control errors.

8 Strategies for Meeting the Photoz Challenge Indirectly Use cross-correlation cosmography (Jain & Taylor 2003, Bernstein & Jain 2004, Hu & Jain 2004, Song & LK 2004) since highly sensitive to z errors. Obtain subsample with spectroscopy and a subsample with ~15 bands stretching into IR to test extrapolation of ~ 5-band observations into magnitudes with no spectra. Plan shallower surveys.

9 Galaxy power spectra alone do not self-calibrate the photo-z s Forecasts for LSST photo-z BAO Zhan & Knox 2005 Contours of constant error on w 0 Contours of constant error on w a

10 Consistency Test Using Galaxy Using galaxy power spectra from 7 redshift bins, with a prior on the photo-z rms, the data can then constrain the mean redshift of each of the 7 galaxy groupings. Results for four of them are shown. Power Spectra Zhan & Knox 05

11 Effect on Cross-Correlation w (θ) W (θ) M. Quilici & H. Zhan With redshift errors Without redshift errors Angular separation Cross-correlation between two bins: z= and z= based on Virgo Hubble Volume simulation θ Photo-z errors lead to correlations across photo-z bins that would otherwise be much smaller. P(z) assume Gaussian with σ(z)=0.1(1+z). Have not yet quantified utility of this signature.

12 Subsampling Strategies (and Curvature Comment) Tomographic WL 2-pt function for 40 gal/arcmin^2, half-sky, <z>=1.5 ( Deep ) bao survey high-z n reducer σ(w piv ) σ(w a ) σ(ω k ) none

13 Subsampling Strategies (and Curvature Comment) Tomographic WL 2-pt function for 40 gal/arcmin^2, half-sky, <z>=1.5 ( Deep ) bao survey high-z n reducer σ(w piv ) σ(w a ) σ(ω k ) none none

14 Subsampling Strategies (and Curvature Comment) Tomographic WL 2-pt function for 40 gal/arcmin^2, half-sky, <z>=1.5 ( Deep ) bao survey high-z n reducer σ(w piv ) σ(w a ) σ(ω k ) none none none none none Note on importance of high z decreases d.e. model dependence of Ω k determination. See Knox 2005.

15 Subsampling Strategies (and Curvature Comment) Tomographic WL 2-pt function for 40 gal/arcmin^2, half-sky, <z>=1.5 ( Deep ) bao survey high-z n reducer * σ(w piv ) σ(w a ) σ(ω k ) none none none none none ,000sq. deg.pessimistic ,000sq. deg.optimistic *reduce n at z > 1.6 by this factor bao surveys at z>1.6 only

16 Shallower Surveys Deep : Tomographic WL 2-pt function for 40 gal/arcmin^2, half-sky, <z>=1.5) Shallow : Tomographic WL 2-pt function for 12 gal/arcmin^2, half-sky, <z>=0.5) WL survey bao survey σ(w piv ) σ(w a ) σ(ω k ) Deep none Shallow none Shallow 20,000sq.deg. pessimistic Shallow 20,000sq.deg.optimistic Dramatic degradation of WL survey quality factor of 2 to 3 worse errors Shallow survey significantly improved by high-z bao survey

17 2000 sq. deg WL Survey Deep : Tomographic WL 2-pt function for 40 gal/arcmin^2, half-sky, <z>=1.5) Shallow : Tomographic WL 2-pt function for 12 gal/arcmin^2, half-sky, <z>=0.5) 2000 : Tomographic WL 2-pt function for 12 gal/arcmin^2, 1/20 th -sky, <z>=0.5) WL survey bao survey σ(w piv ) σ(w a ) σ(ω k ) Deep none Shallow none none sq.deg spec phot4000o phot4000p

18 Summary WL is a distance-redshift probe (like bao and SNe) Mean distances to sources can be measured very well need mean redshifts to be measured very well also. Understanding photo-z redshift errors sufficently well is a great challenge for photometric BAO and WL. We are pursuing a number of strategies.