1. 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)

2. 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

3. 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.

4. 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 = /3H vac ~ <--> vac ~ (0.001 eV)4
w = -1
Undesirable for theoretical reasons

5. The Good: The Methods statistical potential

6. 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

7. 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

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

9. Apparent deflection angle α

12. Cosmic shear two point tomography 

13. Cosmic shear two point tomographyq  

14. Cosmic shear two point tomographyq

15. 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

16. The Bad: The photo-z connection

17. 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

18. 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)

19. ANNz - Artificial Neural Network
z = f(m,w)
Output:
redshift
Input:
magnitudes
Collister & Lahav 2004

20. 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, SNe out to z ~ 1.5, ground based spectroscopy
11/11/2018

21. Surveys considered: galaxies with RIZ<25 considered

22. JPL Simulated catalogueAv
Type z

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

25. Number of spectra needed

26. FOM: Results & Number of spectra needed
FOM prop 1/ dw x dw’
IR improves error on DE parameters by a factor of 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.

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

28. 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

29. The Ugly: Intrinsic Alignements

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

31. 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

32. 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

33. GI alignements:
Bridle & Abdalla

34. Different Cl contributions:
Bridle & King

35. 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.

36. 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

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

38. 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

39. 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.

40. 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?