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Clustering Discussion Session

Clustering Discussion Session. Understanding LAE - Heidelberg 09/oct/08 Chair: Harold Francke, U. de Chile. Why do we care?. Clustering measurements allows the testing of DM hosting halos (of LAEs) Masses, number densities Can constrain evolution of LAE

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Clustering Discussion Session

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  1. Clustering Discussion Session Understanding LAE - Heidelberg 09/oct/08 Chair: Harold Francke, U. de Chile

  2. Why do we care? • Clustering measurements allows the testing of DM hosting halos (of LAEs) • Masses, number densities • Can constrain evolution of LAE • Evolution of DM halos is known in CDM cosmology. • Gives clean measurement of the occupation fraction of LAE on their hosting halos • Using LAEs as tracers of LSS, we can constrain cosmological parameters Talks by Ouchi san & Gawiser san Talk by Blanc kun in the morning…

  3. Basics • Two-point autocorrelation function: P=n2(1 + (12))d1d2 (angular) P=2(1 + (r12))dV1dV2 (spatial) Corresponds to excess probability of finding two points in areas (volumes) d1 and d2 (dV1 and dV2) separated by 12 (r12). • Correlation lengthr0 , slope : (r) = ( r / r0 )-

  4. What does this look like? • Real galaxies show a power law correlation function

  5. Weird case… just for fun

  6. Halo Mass and clustering Dark matter halos are biased tracers of the matter field. Autocorrelation function in a numerical simulation…

  7. More massive DM halos cluster more strongly together…

  8. And are rarer….

  9. So measuring clustering tell us how massive and common are the DM halos hosting our galaxies…

  10. From 2D into 3D… dist observer  SPATIAL correlation function ANGULAR correlation function Beware of narrow redshift distributions! (Simon 2006) distance distribution

  11. Cosmic variance? • If in a survey we detect N galaxies with angular corr. (), the variance in this number is: • LAE surveys are spatially thin • there are less projection effects • () is considerable

  12. From statistics to halos • Bias factor: relates halos to mass • At large scales, the bias is a constant (linear regime) • Short scales (~Rvir) non-linear collapse and non-gravitational effects play a role

  13. From statistics to halos • bias(M) can be calculated from theoretical prediction for halo collapse (Mo&White ‘96 and revisions) • Number densities can be calculated from the halo mass function (Press&Schechter ‘74 and revisions by Sheth&Tormen, 1999-2002)

  14. From statistics to halos • Having a simulation at hand (semianalytical or hydro), you can compare (r) directly and measure masses and number densities in the simulation. (C. Lacey’s & A. Orsi’s talks yesterday) These models should also reproduce the clustering measurements that exist!!

  15. LAE clustering issues • What are the masses of the halos containing them? • How many halos are occupied? • How does LAE clustering relate to Lya and continuum luminosity? • How do LAEs at redshift X relate to galaxy type Y at redshift Z?

  16. LAE clustering issues • How do LAEs at redshift X relate to galaxy type Y at redshift Z? New samples: at z~3: McLinden poster at z~2: talks by Nilsson, Reddy)

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