1. Cargèse - August 2006 Semi-analytics and mock catalogues as tools to observe ideas I.Semi-analytic modelling of galaxy formation The long way from first principles to the distribution of galaxy properties II.Mocking the Universe Construction, limitations and examples of mock catalogues

2. Cargèse - August 2006 Semi-analytic modelling of galaxy formation Jérémy Blaizot (MPA) “Y a des progrès à faire du côté de la gastrophysique” … F. R. Bouchet

3. Cargèse - August 2006 Colless et al., 2001 To what extent are galaxies tracers of DM Physical “sampling” (bias) + observational selection Large-scale surveys

4. Cargèse - August 2006 From low to high redshifts Driver et al. 1998 SAMs and mocks provide a means to connect populations of galaxies selected in different ways at different redshifts (e.g. LBGs/BXs/etc. from Steidel’s group) Galaxies @ z = 0.4 Galaxies @ z = 2.6

5. Cargèse - August 2006 The ISO-HDF Project (Mann et al.) Sources 15  m Sources 6.7  m ISO HST SAMs and mocks help establish the connection between populations of galaxies selected at different wavelengths Observations at different wavelengths

6. Cargèse - August 2006 On top of these motivations, there is the increasing need to produce “realistic” catalogues that can be used: - to prepare forthcoming observations - to validate analysis techniques used on real obs. - to check/understand biases & uncertainties (e.g. cosmic variance) Last but not least …

7. Cargèse - August 2006 Structure formation Dark matter hierarchical structure formation Given initial conditions and a cosmological model, we know how to describe the formation of dark matter structures with N-body simulations.

8. Cargèse - August 2006 Structure formation : N-body simulations

9. Cargèse - August 2006 It all happens in haloes… Semi-analytics neglect the impact of baryons on the formation of large scale structures, and can thus be described a posteriori within the hierarchy of haloes and their evolution. The hybrid approach exploits our best way to describe structure formation : N-body simulations.

10. Cargèse - August 2006 Cooling (metallicity, structure, …) Star formation (threshold, efficiency, IMF, …) Dust (formation, distribution, heating & cooling, …) Winds (IGM heating, enrichment, SN feedback, etc…) AGNs (BH growth, feedback, …) Galaxy interactions (morphological transformations, starbursts, intracluster stars, … Stellar evolution (spectro- photometric evolution, yields, SN I/II rates,…) Galaxy formation & evolution Galaxy formation : relevant processes

11. Cargèse - August 2006 Layout I. Implementation of the “hybrid” approach II. Limitations of SAMs III. Example : Brightest cluster galaxies

12. Cargèse - August 2006 Layout I. Implementation of the “hybrid” approach II. Limitations of SAMs III. Example : Brightest cluster galaxies

13. Cargèse - August 2006 z=0 z=3 z=1 From particles to « haloes » Halo identification (FOF) and characterisation (Mass, Spin, Energies, etc.) From particles to haloes

14. Cargèse - August 2006 SUBFIND (Springel et al. 2001) ADAPTAHOP (Aubert et al. 2004) Identification of sub- structures from the density field (only) (Sub-)Halo finders …

15. Cargèse - August 2006 z=0 z=3 z=1 From particles to « haloes » From density evolution to merger trees Halo identification (FOF) and characterisation (Mass, Spin, Energies, etc.) Construction of a full merger tree (mergers, accretion, fragmentation, evaporation) From particles to halo merger trees

16. Cargèse - August 2006 Example of a Cluster’s tree Tidal stipping

17. Cargèse - August 2006 Spin ( ) Hot gas (T vir ) Galaxy mergers cooling Disc formation Star formation Feedback Metal enrichment (ICM & IGM) Stellar evolution Metal enrichment (ISM) + model of simple stellar population evolution (w/ dust) Semi-analytics

18. Cargèse - August 2006 Cooling (source term…) White & Rees (1978) Binney (1977), Silk (1977) Assume hydrostatic equilibrium (+ isothermal) : temperature and density profile. Note : cooling rates are sensitive to the heavy elements content of the gas (Z). Cooling time (function of radius) : Mass of gas that actually cools : Free-fall radius

19. Cargèse - August 2006 Cooling (source term…) “cold accretion” (rapid cooling) Quasi-static contraction (inefficient cooling) Transition at ~ 10 12 M sun (with some redshift dependency) Kravtsov et al.

20. Cargèse - August 2006 Star formation rate : (highl redshifts ?) Star formation & feedbacks Kennicutt (1998)  gas  SFR Supernovae feedback : (highly uncertain) or not … Metal enrichment : (hyper-highly uncertain) Fixed yield ? Instantaneous recycling ? Instantaneous mixing ?

21. Cargèse - August 2006 Bulge formation Fraction of progenitor disk mass tranfered to descendent’s bulge. 50 % 100 % 0 % Major mergers Minor mergers m 2 / m 1 10 Disrupted disk (m 1 = m 2 ) No bulge (m 1 >> m 2 ) Galaxy mergers - galaxy morphologies Galaxies spiral down haloes’ potential wells due to dynamical friction. When they reach the center they merge with the central galaxy.

22. Cargèse - August 2006 Spectral energy distributions Final SED is the sum of SEDs of stars formed all along the hierarchical history … - stellar evolutionary tracks (Padova tracks, Genova,  -enhancement ? ) - stellar spectra library - IMF … (Chabrier, Kennicutt, Salpeter …) - Extinction/emission by dust.

23. Cargèse - August 2006 spirals ellipticals Gas+stars SFR Stellar mass THE result …

24. Cargèse - August 2006 THE result …

25. Cargèse - August 2006 Frequently asked questions - Do you “resolve” galaxies ? NO ! Galaxies in a SAM are “vectors” : {M star, etc, …} - How many parameters do you fit ? I wish I knew… Lucky we don’t “fit” … - What do you get that you didn’t put in by hand ? A quantitative estimate of the coupled evolution of a set of processes (each “put by hand”) within a complex system of boundary conditions (merger trees).

26. Cargèse - August 2006 John Helly (Durham : http://www.virgo.dur.ac.uk/) Semi-analytic galaxies D.M. density SAM Cinema …

27. Cargèse - August 2006 Layout I. Implementation of the “hybrid” approach II. Limitations of SAMs III. Example : Brightest cluster galaxies

28. Cargèse - August 2006 Chosing a simulation Trade-off between : - Mass resolution (ability to describe history + faint objects) - Volume (ability to describe rare objects)

29. Cargèse - August 2006 galics 1 galics 3 2dF Halo mass resolution “Galics 1” : 1.6 10 11 M sun “Galics 3” : 2.8 10 9 M sun Effects of mass resolution (1/3) completeness limit galaxies in small mass haloes are missing.

30. Cargèse - August 2006 10 10 M O 10 11 M O 10 12 M O 10 13 M O Effects of mass resolution (2/3) completeness limit galaxies in small mass haloes are missing. redshift limit beyond zlim, there are no resolved haloes.

31. Cargèse - August 2006 history resolution properties of new galaxies are not realistic galics 1 M h = 2 10 11 M sun galics 3 M h = 3 10 9 M sun redshift limit beyond zlim, there are no resolved haloes. completeness limit galaxies in small mass haloes are missing. Effects of mass resolution (3/3)

32. Cargèse - August 2006 Other limitations … Each step of the post-processing involve approximations that do not disapear even if the results fit the observations ! - halo finder : N-body describes exactly the (non-linear) evolution of a density field … haloes are not so exact… - halo merger trees : following sub-structures is a delicate business … - galaxy mergers : largely unknown … (both when & how) - metals : production, transport … - SEDs : if you don’t believe in BC03 or Chabrier’s IMF …

33. Cargèse - August 2006 Layout I. Implementation of the “hybrid” approach II. Limitations of SAMs III. Example : Brightest cluster galaxies

34. Cargèse - August 2006 Brightest Cluster Galaxies (BCGs) Brightest (and central) galaxies of the most massive haloes of the Universe (typically M halo ~ 10 15 M sun ) Selection of clusters (e.g. with L X ), so far possible up to z ~ 1 BCGs are the galaxies with the richest merger trees

35. Cargèse - August 2006 Brightest Cluster Galaxies (BCGs) De Lucia & Blaizot (2006)

36. Cargèse - August 2006 Brightest Cluster Galaxies (BCGs) De Lucia & Blaizot (2006)

37. Cargèse - August 2006 Brightest Cluster Galaxies (BCGs) : 2 x 2 Mpc (comoving)

38. Cargèse - August 2006 Brightest Cluster Galaxies (BCGs) Mass growth ~ 3 since z=1 (along the “main branch”) Infered mass growth ~ 3 since z=1 (“total”) High-z BCGs are do not end up in local BCGs…

39. Cargèse - August 2006 The monolithic approximation (isolated evolution or “one-branch tree”) is wrong in general and should not be used to try to assess evolutionary links between galaxy populations observed at different redshifts. Brightest Cluster Galaxies (BCGs) The proper way to go is to reproduce observational selections on the model galaxies, using mock catalogues, and then go back to the model to understand the (hierarchical) links between galaxies selected in different ways.

40. Cargèse - August 2006 SAMs & mock catalogues for interpreting observations Jérémy Blaizot (MPA)

41. Cargèse - August 2006 Colless et al., 2001 To what extent are galaxies tracers of DM Physical “sampling” (bias) + observational selection Selections …

42. Cargèse - August 2006 The ISO-HDF Project (Mann et al.) Sources 15  m Sources 6.7  m ISO HST Selections, selections … SAMs + Mocks help establish the connection between populations of galaxies selected at different wavelengths

43. Cargèse - August 2006 Selections, selections, hierarchical evolution … SAMs and mocks provide a means to connect (statistically) populations of galaxies selected in different ways at different redshifts (e.g. LBGs/BXs/etc. from Steidel’s group) Galaxies @ z = 0.4 Galaxies @ z = 2.6

44. Cargèse - August 2006 Observations Theoretical Framework General framework Physical model (“ingredients” & “Recipes”) Hybrid implementation Some comparison to obs. Surveys Galaxy samples @ diff. z & Mock Catalogues

45. Cargèse - August 2006 Layout I. Construction of mock catalogues II. Limitations of mock catalogues III. Example 2 : Lyman Break Galaxies IV. Just do it …

46. Cargèse - August 2006 Layout I. Construction of mock catalogues II. Limitations of mock catalogues III. Example 2 : Lyman Break Galaxies IV. Just do it …

47. Cargèse - August 2006 Inputs for mock catalogues Series of napshots at z snap = z i (i = 1, …, N) - Observer-frame (z snap ) absolute magnitudes and their derivative : - positions / velocities - size(s), inclination - IDs

48. Cargèse - August 2006 Tiling boxes … basics

49. Cargèse - August 2006 dec. r.a. Tiling boxes … replications

50. Cargèse - August 2006 “Random tiling” dec. r.a. Tiling boxes … random tiling Supresses replication effects … and some of the signal (see later)

51. Cargèse - August 2006 18 < r < 19 21 < r < 22 20 < r < 21 19 < r < 20 Example 1 : mock SDSS stripe

52. Cargèse - August 2006 Flatten grid for inclination Rotate grid for orientation Scale to galaxy size discs Spheroids Scale to galaxy size Projection on the final grid Pre-observation maps

53. Cargèse - August 2006 3 arcmin 6 arcmin Johnson V filter HDF Example 2 : mock V-band deep field

54. Cargèse - August 2006 SkyMaker (E. Bertin)

55. Cargèse - August 2006 Layout I. Construction of mock catalogues II. Limitations of mock catalogues III. Example 2 : Lyman Break Galaxies IV. Just do it …

56. Cargèse - August 2006 Correlation functions Excess probability of finding a pair of galaxies at a given separation, relative to a random distribution. Data-Data Random-Random Field-to-field variance (in counts) ~ average of  over field-size

57. Cargèse - August 2006 Random pairs Negative bias typically peaking around r 0, with amplitude : Random tiling bias

58. Cargèse - August 2006 Finite Volume R.T. bias Random tiling bias 100 Mpc/h 12 Mpc/h Analytic estimate R.T. bias present around r 0, but well understood. Finite volume effects (integral constraint) comes in at larger scales…

59. Cargèse - August 2006 100 Mpc/h 20 Mpc/h Finite-volume effects & correlation function A simulation does not contain fluctuations (clustering) on scales larger than L box

60. Cargèse - August 2006 Finite-volume effect & cosmic variance Simulation volume should be >> light-cone volume …

61. Cargèse - August 2006 Layout I. Construction of mock catalogues II. Limitations of mock catalogues III. Example 2 : Lyman Break Galaxies IV. Just do it …

62. Cargèse - August 2006 (e.g.) Adelberger et al. (1998) : LBG selection (at z=3) Blaizot et al. (2004) Pure photometric selection : good test for the model and mock- catalogue methodology

63. Cargèse - August 2006 1.1 Gyr 1.3 Gyr 0 Gyr galics 3 LBG counts and cosmic variance Clustering of LBGs dominates cosmic variance up to (at least) 1 deg.

64. Cargèse - August 2006 C’est « ca va » ! Steidel’s team 30% of LBGs’ intense SF is triggered by mergers LBGs : physical properties

65. Cargèse - August 2006 The Epoch of Galaxy Formation, Baugh et al. 1998 zz LBGs Link to local galaxies (1/2)

66. Cargèse - August 2006 E + S0 with LBG prog. at z=3 Other E + S0 Sp with LBG prog. at z=3 LBGs at z=3 z = 3z = 0 77% of z=3 LBGs end up in E or S0 at z = 0 35% of local E or S0 have a LBG progenitor at z = 3 Link to local galaxies (2/2)

67. Cargèse - August 2006 IV. Just do it … Layout I. Construction of mock catalogues II. Limitations of mock catalogues III. Example 2 : Lyman Break Galaxies

68. Cargèse - August 2006 Online stuff … boxhalogalaxy cone Cosmological quantities at each snapshot (e.g. redshift, number of halos, mass of stars) Physical props. Hierarchical links, Spatial information. Physical props. Hierarchical links, Rest-frame magnitudes. Spatial information, Apparent magnitudes. Observer-frame spectra Rest-frame spectra Mock Images

69. Cargèse - August 2006 Online stuff … http://www.g-vo.org/Millennium/ (Gerard Lemson)