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Semi-analytics and mock catalogues as tools to observe ideas

Semi-analytics and mock catalogues as tools to observe ideas. 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.

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Semi-analytics and mock catalogues as tools to observe ideas

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

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

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

  4. Galaxies @ z = 0.4 Galaxies @ z = 2.6 From low to high redshifts 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) Driver et al. 1998

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

  6. Last but not least … • 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)

  7. 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. Structure formation : N-body simulations

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

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

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

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

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

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

  16. Tidal stipping Example of a Cluster’s tree

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

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

  19. “cold accretion” (rapid cooling) Quasi-static contraction (inefficient cooling) Cooling (source term…) Transition at ~ 1012Msun (with some redshift dependency) Kravtsov et al.

  20. Star formation & feedbacks Star formation rate : (highl redshifts ?) SSFR Supernovae feedback : (highly uncertain) or not … Kennicutt (1998) Metal enrichment : (hyper-highly uncertain) Sgas Fixed yield ? Instantaneous recycling ? Instantaneous mixing ?

  21. 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. Bulge formation Disrupted disk (m1 = m2) 100 % Major mergers Fraction of progenitor disk mass tranfered to descendent’s bulge. 50 % Minor mergers No bulge (m1 >> m2) 0 % m2 / m1 0 1

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

  23. THE result … spirals ellipticals Stellar mass Gas+stars SFR

  24. THE result …

  25. Frequently asked questions • Do you “resolve” galaxies ? • NO ! Galaxies in a SAM are “vectors” : {Mstar, 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. SAM Cinema … D.M. density Semi-analytic galaxies John Helly (Durham : http://www.virgo.dur.ac.uk/)

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

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

  29. galics 3 2dF galics 1 Effects of mass resolution (1/3) • completeness limit galaxies in small mass haloes are missing. Halo mass resolution “Galics 1” : 1.6 1011Msun “Galics 3” : 2.8 109Msun

  30. Effects of mass resolution (2/3) • completeness limit galaxies in small mass haloes are missing. 1010 MO 1011 MO • redshift limit beyond zlim, there are no resolved haloes. 1012 MO 1013 MO

  31. galics 3 Mh = 3 109 Msun galics 1 Mh = 2 1011 Msun Effects of mass resolution (3/3) • completeness limit galaxies in small mass haloes are missing. • redshift limit beyond zlim, there are no resolved haloes. • history resolution properties of new galaxies are not realistic

  32. 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. Layout I. Implementation of the “hybrid” approach II. Limitations of SAMs III. Example : Brightest cluster galaxies

  34. Brightest Cluster Galaxies (BCGs) Brightest (and central) galaxies of the most massive haloes of the Universe (typically Mhalo~ 1015 Msun) Selection of clusters (e.g. with LX), so far possible up to z ~ 1 BCGs are the galaxies with the richest merger trees

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

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

  37. Brightest Cluster Galaxies (BCGs) : 2 x 2 Mpc (comoving)

  38. 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. Brightest Cluster Galaxies (BCGs) 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. 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. SAMs & mock catalogues for interpreting observations Jérémy Blaizot (MPA)

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

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

  43. Galaxies @ z = 0.4 Galaxies @ z = 2.6 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)

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

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

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

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

  48. Tiling boxes … basics

  49. dec. r.a. Tiling boxes … replications

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

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