Cosmological simulations for Euclid

Transcription

1 Cosmological simulations for Euclid Pablo Fosalba (Barcelona) Robert Smith (Zürich) Stéphane Colombi (Paris) Carlton Baugh (Durham) Marco Baldi (Münich) and many others 1

2 Outline 1. Computing the matter power spectrum with < 1% accuracy 2. Understanding the impact of baryonic physics 3. SAM and mock catalogs generation 4. Beyond standard gravity models 2

3 Cosmological simulations Codes 1. Initial conditions generators 2. N body and hydro codes 3. Statistical tools 4. Semi-Analytical Models 5. Mock catalogs generation Interface with instrument simulations? Products 1. Large scale N body simulations of LCDM model 2. N body runs for alternative viable models 3. Intermediate scale hydro simulations 4. Small scale hydro simulations of galaxy clusters Cosmo Sim WG: coordinator Pablo Fosalba with 37 people in the mailing list 3

4 Computing the power spectrum below 1% accuracy Requirement: better than 1% from < k < 50 h/mpc From < k < 0.1: Lagrangian Perturbation Theory k>0.01: N body simulations L=50 Gpc/h Courtesy of Stéphane Colombi 4

5 Computing the power spectrum below 1% accuracy Millenium XXL 5

6 The impact of baryons on the matter power spectrum Some history: Jing et al. 2006; Rudd et al Modify the total power spectrum wrt pure DM spectrum up to 20 percent! between l=10 3 and 10 4 Rudd et al Rudd et al Ideas: - design halo models that account for baryons - validate the halo model on numerical data - use the halo model to fit real data 6

7 AGN feedback regulates the mass distribution Without AGN feedback, overcooling leads to a strong mass concentration in the center. With AGN feedback, we see a small adiabatic expansion of the dark halo. Teyssier et al Semboloni et al

8 The impact of baryons on the matter power spectrum Guillet et al Semboloni et al

9 The impact of baryons on the matter power spectrum Adding baryons parameters to the model will reduce the bias without degrading error bars. Semboloni et al Guillet et al

10 Why build a mock catalogue? A synthetic galaxy can catalogue be used to: Test estimators: e.g. Galaxy clustering, photo-z, group-finders Uncover systematic effects (we already know the right answer) Estimate errors Generate predictions for different dark energy cosmologies 10

11 The physics of galaxy formation Combination of simulations, analytic results and differential equations (sometimes with parameters ) to follow processes which affect baryons Baugh 2006 Rep. Prog. Phys. Cole et al

12 Predict star formation histories Model builds up a composite SED for each galaxy Tracks bulge and disk components: light, stellar mass and radii. SED & SFH gives Lyman continuum photons Emission line luminosity and EW. 12

13 Predict where galaxies should be H-a selection z=1 H-band selection Orsi et al

14 What is a mock catalogue? STEP 1 STEP 2 N-body simulation Combine with galaxy formation model Millennium, Horizon, MICE, MXXL pringel et al. 2005, Teyssier et al. 2009; EUCLID Fosalba 2011 Bologna et al 2008; Angulo et al A priori calculation of baryon physics e.g. GALFORM; GALICS, LGALAXIES Cole et al. 2000; Hatton etal; de Lucia et al. 14

15 Constructing observer s past lightcone STEP 3 Build galaxy catalogue on observer s past lightcone. e.g. Blaizot et al 2005; Fosalba et al. Kitzbichler & White; Merson et al. 15

16 Step 4: apply survey window function Apply angular footprint Apply scanning mask Incorporate sampling Redshift completeness (synergy with OU-SIM) Steps 1, 2, 3 generic Step 4 is survey specific and needs dedicated effort 16