1. Institute for Computational Cosmology Durham University Shaun Cole for Carlos S. Frenk Institute for Computational Cosmology, Durham Cosmological simulations and forecasts for future surveys

2. Institute for Computational Cosmology Durham University Outline Baryonic Acoustic Oscillations Origin Measurements Distortion due to non-linear evolution, peculiar velocities and galaxy bias Future Surveys and Forecasts Spectroscopic and Photometric Surveys EUCLID and Pan-STARRS1

3. Institute for Computational Cosmology Durham University CMB anisotropies and large-scale structure Meiksin etal 99

4. Institute for Computational Cosmology Durham University Baryon oscillations in the power spectrum Comoving sound horizon at t rec (depends mostly on  m h 2 and weakly on  b h 2 ) “wavenumber” of acoustic oscillations: k BAO = 2  /s Comoving distance/redshift: (depends on  m h 2 and w) Apparent size of standard ruler depends on cosmology  dark energy eqn of state parameter w (e.g. Eisenstein & HU 1998; Blake & Glazebrook 2003, 2005; Seo & Eisenstein 2003; 2005……)

5. Institute for Computational Cosmology Durham University The final 2dFGRS power spectrum 2dFGRS P(k) well fit by  CDM model convolved with window function Cole, Percival, Peacock, Baugh, Frenk + 2dFGRS ‘05

6. Institute for Computational Cosmology Durham University The final 2dFGRS power spectrum  CDM model  CDM convolved with window 2dFGRS P(k) well fit by  CDM model convolved with window function Cole, Percival, Peacock, Baugh, Frenk + 2dFGRS ‘05

7. Institute for Computational Cosmology Durham University The final 2dFGRS power spectrum  CDM model  CDM convolved with window Baryon oscillations conclusively detected in 2dFGRS!!! Demonstrates that structure grew by gravitational instability in  CDM universe P(k) / P ref (  baryon =0) Also detected in SDSS LRG sample (Eisenstein etal 05) Cole, Percival, Peacock, Baugh, Frenk + 2dFGRS ‘05

8. Institute for Computational Cosmology Durham University SDSS LRG correlation function Eisenstein et al. 05 Again, CDM models fit the correlation function adequately well (although peak height is slightly too large; assuming n s =1, h=0.72)  b h 2 =0.024,  m h 2 =0.133±0.011,   b /  m = 0.18  s)

9. Institute for Computational Cosmology Durham University Constraints on w from SnIa, WMAP and LSS Spergel et al 2006 w w mm mm Cosmological constant Assume w=const (cosmological constant) w=-0.9  0.1

10. Institute for Computational Cosmology Durham University s = comoving sound horizon at t rec Depends on physical parameters “Wavenumber” of acoustic oscillations is: k BAO = 2  /s Measure k A in redshift survey: conversion z  k in P(k) depends on geometry and expansion history and so on w Estimate of w from P(k) Consider idealized case: All cosmological parameters apart from w known

11. Institute for Computational Cosmology Durham University s = comoving sound horizon at t rec Depends on physical parameters “Wavenumber” of acoustic oscillations is: k BAO = 2  /s Measure k A in redshift survey: conversion z  k in P(k) depends on geometry and expansion history and so on w Estimate of w from P(k) Consider idealized case: Angular size of sound horizon at last scattering kept fixed Stretch factor

12. Institute for Computational Cosmology Durham University Surveys: The Next Generation

13. Institute for Computational Cosmology Durham University Springel etal 05

14. Institute for Computational Cosmology Durham University linear theory The non-linear mass power spectrum is accurately determined by the Millennium simulation over large range of scales The mass power spectrum z=0 z=1 z=3.05 z=7 z=14.9  2 (k) k [h/Mpc] L box =500Mpc/h

15. Baryon wiggles in the galaxy distribution Springel et al 2005 Power spectrum from MS divided by a baryon-free  CDM spectrum Galaxy samples matched to plausible large observational surveys at given z z=0 z=1 z=3 z=7 DM gals Millennium simulation The effective k eq changes as does k Silk, but does k BAO ?

16. Institute for Computational Cosmology Durham University N-body simulations of large cosmological volumes BASICC L=1340/h Mpc N=3,036,027,392 20 times the Millennium volume Halo resolution: (10 particle limit) 5.5 e+11/h Mpc 130,000 cpu hours on the Cosmology Machine Angulo, Baugh, Frenk & Lacey ‘07

17. Institute for Computational Cosmology Durham University The hierarchical growth of structure 1000/h Mpc 500/h Mpc 200/h Mpc

18. Institute for Computational Cosmology Durham University BASICC simulation dark matter real space P(k) divided by linear theory P(k), scaling out growth factor Non-linear evolution of matter fluctuations P(k)/P linear (k) Angulo, Baugh, Frenk & Lacey ‘07

19. Institute for Computational Cosmology Durham University Non-linear evolution of matter fluctuations Log (P(k)/P linear (k)) at z=1 Angulo, Baugh, Frenk & Lacey ‘07

20. Institute for Computational Cosmology Durham University Coherent bulk flows boost large scale power (Kaiser 1987) Redshift space distortions Peculiar motions distort clustering pattern Motions of particles inside virialised structures damp power at high k

21. Institute for Computational Cosmology Durham University Peculiar motions distort clustering pattern Boost in power on large scales due to coherent flows Damping at higher k affects DM but not the halos In z-space, halo bias is scale-dependent Redshift space distortions Halos M> 10 12 M o Dark matter Redshift space

22. Institute for Computational Cosmology Durham University Absolute Magnitude limited sample. Galaxy clustering boosted relative to mass in real space Galaxy bias in real space Angulo, Baugh, Frenk & Lacey ‘07

23. Institute for Computational Cosmology Durham University Boost in clustering approximates to a constant bias factor on large scales. Galaxy bias in real space Angulo, Baugh, Frenk & Lacey ‘07

24. Institute for Computational Cosmology Durham University Galaxy P(k) cannot be reproduced by multiplying mass P(k) by constant factor in redshift space. Galaxy bias in redshift space  In z-space, galaxies have a scale-dependent bias out to k~0.1

25. Institute for Computational Cosmology Durham University Comparison of different selections e.g. colour, emission line strength Galaxy bias in redshift space Angulo et al ‘07

26. Institute for Computational Cosmology Durham University Fit BAO oscillations Remove effect of scale dependent bias by fitting a smooth spline and then take ratio R(k)=P(k)/P smooth (k). Fit ratio, R(k), to determine both the stretch factor,  and the damping scale, k nl. ( Percival et al 2008)

27. Institute for Computational Cosmology Durham University Recovered values k nl treated as a nuisance parameter. Small offsets in  but are they significant?

28. Institute for Computational Cosmology Durham University Sample Variance 50 lower resolution “L-BASICC” simulations used to determine the sample variance. Particle mass 30 times larger. Bias not significant

29. Institute for Computational Cosmology Durham University Fractional error in P(k) (Feldman, Kaiser & Peacock 1994)

30. Institute for Computational Cosmology Durham University Angulo et al (2008) tabulate rms error in   for different fiducial samples within the BASICC simulation Survey Forecasts

31. Institute for Computational Cosmology Durham University Scaling error forecasts to different surveys: delta(w) ~ 1/sqrt (V) x [1 + 1/(n P)] Error on the measured power BAO method: virtually free of systematics (c.f. lensing, SNIa) (i)Sample variance (ii)Shot noise P = power n = mean no density

32. Institute for Computational Cosmology Durham University EUCLID: DMD-based NIR spectra See poster 21: Andrea Cimatti

33. Institute for Computational Cosmology Durham University EUCLID: Spectroscopy CategoryItemRequirement Spectroscopic BAO Survey Redshift Accuracyσ z < 0.001 Spectral Range0.9-1.7 micron Number of spectra 1.65x10 8 galaxies Sampling > 33% Limiting magnitude and redshift distribution H(AB)=22 mag corresponding to 0<z<2 Deep spectroscopic sample Photo-z calibration 10 5 redshifts down to H(AB)=24 mag

34. Institute for Computational Cosmology Durham University Projected BAO data for planned surveys at z=1 Projections based on mock catalogues made from large N-body simulation plus semi-analytic galaxy formation model EUCLID

35. Institute for Computational Cosmology Durham University Main future BAO surveys NameN(z) / 10 6 Stretch DatesStatus SDSS/2dFGRS0.83.5%NowDone WiggleZ0.42%2007-2011Running FastSound0.62.8%2009-2012Proposal BOSS1.51%2009-2013Proposal HETDEX11.5%2010-2013Part funded WFMOS>20.8%2013-2016Part funded ADEPT>1000.2%2012+JDEM EUCLID>100 0.15%2017+ESA SKA>1000.2%2020+Long term

36. Institute for Computational Cosmology Durham University Pan-STARRS1 3  Survey g r i z y

37. Institute for Computational Cosmology Durham University 3  size and depth More than galaxies! Detected in griz and y

38. Institute for Computational Cosmology Durham University 3  size and depth More than galaxies! Detected in griz only More details will be presented by Stephanie Phleps later

39. Institute for Computational Cosmology Durham University Photo-z accuracy Initial estimates indicate that for red/early type galaxies

40. Institute for Computational Cosmology Durham University Damping effect on BAO Yan-chuan Cai et al (2008)

41. Institute for Computational Cosmology Durham University Future photo-z BAO surveys NameN(z) / 10 6 Stretch DatesStatus PS1>1000.5% 2009-2013Funded DES50<1% 2010-2014Funded PAU(BAO)140.4%2014+planned

42. Institute for Computational Cosmology Durham University Summary BAO have an important future as a cosmic standard ruler used to constrain D(z) and hence Dark Energy. Systematic errors are not yet dominant, but more theoretical work is needed to ensure this remains true for the forthcoming generation of surveys In advance of the long term space projects, photo- z surveys promise interesting constraints