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Environmental Effect on Mock Galaxy Quantities

2007/02/21. Environmental Effect on Mock Galaxy Quantities. Juhan Kim, Yun-Young Choi, & Changbom Park Korea Institute for Advanced Study. Content. A model to make mock galaxies from N-body simulation Model test & Justification Model prediction. A Roadmap for Galaxy World.

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Environmental Effect on Mock Galaxy Quantities

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  1. 2007/02/21 Environmental Effect on Mock Galaxy Quantities Juhan Kim, Yun-Young Choi, & Changbom Park Korea Institute for Advanced Study

  2. Content • A model to make mock galaxies from N-body simulation • Model test & Justification • Model prediction

  3. A Roadmap for Galaxy World SDSS GALAXIES SIMULATION Luminosity Function Choi & Park Halo-to-galaxy model Spin Distribution Choi & Park Morphology/ Velocity dispersion Park & Park MOCK GALAXIES Environmental Effect Velocity Correlations Park & Park Topology of LRG & Galaxy Choi & Park Cosmological Model

  4. How to build Mock Galaxies • Directly implements algorithms & parameters for hydrodynamics. • (SAM) • Uses merging tree built by random realizations • Merging mass growth : M(t) M’(t’) • Uses galaxy formation recipe • mass growth  star-formationL & chemical evolution • Parameters: IMF, SF rate, metal enrichment…. • (HOD) • P(N|M): probability number of galaxies in an FoF halo of mass M • Galaxy distribution inside a halo to satisfy observed gg • (MOC) • Subhalo galaxy: every subhalo can host a galaxy • Subhalo Mass  galaxy Luminosity Hydro Simulation Semi-Analytic Model Halo Occupation Distribution Monotonic One-to-one Correspondence

  5. Pros & Cons • Direct Hydro Simulation • Can directly follow complex nonlinear evolution of gas particles. • But uses ambiguous parameters for complicated nonlinear phenomena (IMF,SF). • Lack in resolution  needs much more computer resources than currently available (Small-scale phenomena in Large-scale environments). • SAM • Can reproduce observables by introducing parameters. • But needs too many parameters. • Some parameter values can be degenerated in parameter space. • HOD • Can parameterize the spatial distribution of galaxies in clusters. • Is a kind of descriptive methods and, therefore, restricted. • Cannot predict phase-space distributions inside clusters. • MOC • Is simple & straightforward: very few parameters are needed. • Because recently developed, it is not seriously tested in various fields.

  6. MOC implementation to PSB halos • Two (simple & reasonable) assumptions • One subhalo may host only one galaxy • One-to-one correspondence • A more massive subhalo has a more luminous galaxy • Luminosity of a galaxy is a monotonic function of its host subhalo mass • If halo mass is given, the luminosity of the inside galaxy is obtained. • SDSS : • PSB :

  7. Subhalos in a halo

  8. Subhalos in halos • Cloud in Cloud

  9. Mass Function of Dark Halos Press & Schechter Sheth & Tormen

  10. Mass-to-light relation M<-20 M<-18

  11. Model Test • Local density distribution • -21<Mr<-20 galaxies are used for density seeds. • Variable size Spline kernel is used to measure local density. • Luminosity functions of various sub-samples divided by local density criteria

  12. M<-21 M<-20

  13. Luminosity Function Total crowded Void

  14. Schechter Parameters with Local Density

  15. Spin Distributions • Spin parameter: l • =1(rotation-supported) • =0(pressure-supported) • Spin distribution • Log-normal • Gamma

  16. Universality of the Spin Shape

  17. Characteristics of Spin distributions • Shape (k) of spin distributions: nearly constant • Origin of spins: off-center impact & inhomogeneous infall • Depends on the number of local filament branches • More massive halo: smaller spin • In more crowded region: higher spin

  18. Spin Dependence on Galaxy Mass Less massive galaxies: more anisotropic merging more massive galaxies: more isotropic merging

  19. Spin Dependence on Local Density Overdense region: merging dominated Under dense region: accretion dominated

  20. Summary • MOC is more powerful than other traditional methods. • Simple implementation to create mock galaxies • A model with less parameters is more powerful!!!!!! • SDSS density distributions & LF’s are well recovered. • Spin distributions of mock galaxies • Distribution shape is constant and shift parameter depends on local & merging environments.  hints at a possible statistical explanation on the spin & merging history of halos?

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