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Formation hiérarchique des structures à grande échelle de l’Univers:

Formation hiérarchique des structures à grande échelle de l’Univers:. Les observations face aux modèles Sophie Maurogordato. En collaboration avec:. M. Arnaud, E. Belsole, F. Bernardeau, M.Lachièze-Rey, J.L. Sauvageot, R.Schaeffer, R. Teyssier (CEA-CEN Saclay) F. Bouchet (IAP)

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Formation hiérarchique des structures à grande échelle de l’Univers:

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  1. Formation hiérarchique des structures à grande échelle de l’Univers: Les observations face aux modèles Sophie Maurogordato

  2. En collaboration avec: M. Arnaud, E. Belsole, F. Bernardeau, M.Lachièze-Rey, J.L. Sauvageot, R.Schaeffer, R. Teyssier (CEA-CEN Saclay) F. Bouchet (IAP) C. Benoist, A. Bijaoui, H. Bourdin, C.Ferrari, E. Slezak (OCA) C. Balkowski, V. Cayatte, P. Felenbok, D. Proust (Obs. Paris- Meudon) R. Pello, J.P. Kneib (OMP, Toulouse) A.Cappi, P. Vettolani, L. Feretti (Obs. & CNR Bologna, I) M. Plionis, S. Basilakos (Obs. Athenes, Gr) D. Batuski, C. Miller, T. Beers, J. Kriessler (USA) The ESP team

  3. CfA2 9325 galaxies mB<15.5 150 h-1Mpc SSRS2 From da Costa et al. 1994

  4. General Framework Big Bang theory General Relativity Cosmological Principle Primordial fluctuations (infinitesimal) Growth by gravitational instability Large scale structure of the Universe observed today

  5. Cosmological Scenario • Density parameters:Wm + WL + Wk = 1 (Einstein equations) Wm: total matter WL: dark energy Wk: curvature Wb: baryonic matter • Hubble constant : H0 = 100 h km/sec/Mpc • Normalization: s8 mass fluctuations in 8h-1 Mpc spheres + Nature of dark matter

  6. Cosmological parameters from observations

  7. Statistical Analysis of galaxy and cluster distribution 2-pt indicators ESP Galaxy/matter « Bias » Luminosity segregation SSRS, SSRS2, ESP High order Moments of galaxies and clusters

  8. How to constrain P(k) from LSS ? • Primordial P(k) matter Theoretical Predictions Nature of density fluctuations (gaussian, non gaussian) Mechanism (inflation, texture, cosmic strings) Linear evolution • Evolved P(k) matter bias : relation galaxies/matter distribution linear bias approximation: dr/r g = dr/r m • Evolved P(k) galaxies in real space modelling the clustering distortion redshift space/real space • Evolved P(k) galaxies in redshift space Observations

  9. Modelling P(k) P(k) = B k (1+{ak+(bk)3/2+(ck)2}n)2/n a,b,c functions of G = W h (Bond and Efstathiou 1984) CMB Normalisation: B LSS via s8 and model of bias (variance in spheres of 8h-1 Mpc) and linear evolution Coherence of large and small scales normalisation bias Shape : characteristical of the nature and amount of dark matter Standard CDM : G = W h = 0.5

  10. The evolution of the clustering pattern with z for different cosmological scenarios

  11. 2nd order statistics on galaxy catalogs 2D APM: Shape of w(q) and P(k) disagrees with SCDM (G=0.5) From Maddox et al. 1990 3D catalogs: Large uncertainty on normalisation (bias) Problem of Fair Sample From Efstathiou et al. 1992

  12. The ESO Slice Project European Project (Vettolani et al. 1998) at the ES0 3.6m telescope Slice of 23 square degrees near SGP bJ < 19.4 3342 redshifts Large structure : 50 x 100 h-1 Mpc @z=0.1 From Vettolani et al. 1998

  13. 3D correlation function in 2000’s The power excess at large scales detected by the 2D APM is confirmed SCDM with G=0.5 ruledout. Best agreement G = 0.2-0.3 From Guzzo et al. 1999

  14. Scaling relations in the galaxy/matter distribution Observations: The distribution of galaxies today is highly non gaussian. Hierarchical relation between correlation functions which can be modelized by: Hierarchical model Schaeffer 1984, Fry 1984 Sum over graphes Sum over labelling of graphes More generally: Scale invariant models (Balian and Schaeffer 1989) SJ are independent of scale

  15. Predictions for the matter distribution: SJ’s: Mildly non linear regime:Perturbation theory (Juskiewicz, Bouchet and Colombi 1993, Bernardeau 1994) Case of power laws: SJ are constants Highly non linear regime: numerical simulations (Baugh, Gaztanaga and Efstathiou 1995) Scale invariance of the Void Probability function: SJ = f(g1,…,gJ-1)

  16. Scaling relations in 3D galaxy catalogs Void probability function Correlation functions SSRS2 SSRS Counts probabilities SSRS Maurogordato, Schaeffer and da Costa 1992 Benoist et al. 1999

  17. Galaxy/Mass distributions • Does light trace mass ? • Linear bias hypothesis: dr/r g = b dr/r m • Biased galaxy Formation (Kaiser 1984, Bardeen et al. 1986) galaxies form at the location of high density peaks in an initial gaussian random field: d > ns x(r) > ns = A x(r) A = k n2/s2 more massive objects more clustered Bias relation at small scales: more complicated (gaz cooling, supernovae feedback, galaxy fusions within halos) Distribution of galaxies within the halos: Semi-analytical models (Mo and White 1996, Benson et al. 2000, …)

  18. Luminosity bias in the SSRS2 Strong enhancement of correlation amplitude for very bright galaxies: M > -20.0 From Benoist et al. 1996

  19. Luminosity bias in the ESP redshift space From Guzzo et al. 1999 Real space (projected)

  20. The next generation catalogs: Colless et al. 2002 106688 galaxies 2dF Galaxy Redshift Survey

  21. Luminosity bias in 3D galaxy catalogs in the 2000’s From Norberg et al. 2001

  22. Test of the linear bias hypothesis dg(x)= bgdm(x) xgJ(r)=bgJxmJ(r) SgJ = SmJbgJ-2 Expected from luminosity segregation on x(r) Observed Second-order term for high luminosities From Benoist et al. 1999 Inconsistence between 2nd order and high order moments results for linear bias hypothesis at small scales.

  23. Cluster clustering 3D: Correlation function: power law with large correlation radius: x(r)=(r/r0)g -g 19.3 < r0 < 20.6 h-1 Mpc Good agreement with Postman et al. 1992 Power up to 40-50 h-1 Mpc 2D: Scale-invariance of cumulants :hierarchical relation for clusters S3 cl~ S3 gal inconsistent with r0 and linear bias hypothesis. 3D x(r) ACO North and South with bII > 40 z<0.08 Cappi and Maurogordato 1992 2D: ACO Projected high order correlation functions and cumulants Cappi and Maurogordato 1995

  24. AQUARIUS SUPERCLUSTER American-French program Percolation on the ACO catalog: dcc < 25 h-1 Mpc supercluster candidates 110 h-1 Mpc Aquarius supercluster: Exceptionally dense and extended ! n=8<n> over 110 h-1 Mpc n=150<n> in the core (6 clusters) From Batuski et al. 1999

  25. Conclusions • Galaxy distribution: hierarchical relations of high order correlations (cumulants, VPF, count probabilities) • Predicted in the frame of models with hierarchical formation of structures • Success of gravity to form the structure pattern observed today from initial gaussian fluctuations • Luminosity bias constant with scale (analysis of SSRS, SSRS2 and ESP, confirmed now by 2dFGRS and SDSS) Problems with the linear bias hypothesis at small scales from the combined analysis of cumulants/ 2pt correlation function (galaxy and cluster distribution)

  26. Today: multiple evidences for a «concordant » LCDM hierarchical model: Wm = 1 – WL = 0.3, Wb=0.02, h=0.70, n=1. Combining CMB and LSS analysis gives a better determination of the parameters New generation of 3D surveys (SDSS, 2dFGRS, …) + CMB experiments at different angular scales (COBE, Boomerang, WMAP, Planck,…) Soon : good knowledge of cosmological parameters But still need to improve our understanding of the bias relation and physics of galaxy formation From Lahav et al. 2002

  27. Analysis of currently forming clusters In the hierarchical model, galaxy clusters form by merging of smaller mass units Irregular, morphologically complex clusters are still forming. Insights on the formation process before virialisation Cosmological interest: n(z) is W dependant Combined X-Ray/ Optical analysis allows to follow separately the distribution of gas and of galaxies.

  28. Evolution with time of the density and velocity distribution of galaxies during the merger event From Schindler and Bohringer 1993

  29. Evolution of the density and temperature of the gas with time during the merging event From Takizawa 1999

  30. Abell 521: a cluster forming at the crossing of LSS filaments? - Severe gas-galaxy segregation - X-Ray: well fitted by a 2-component b-model: cluster + group - Privilegiated axes - Huge velocity dispersion: 1450 km/s (40 z) - BCG offset from the main cluster, in the group region N W W From Arnaud, Maurogordato, Slezak and Rho, 2000

  31. The Brightest Cluster Galaxy Extremely bright: L = 13 L* Arc structure embedding knots at z cluster Located near the X-Ray group center Profile: de Vaucouleurs without the cD tail BCG in formation within a group, by cannibalism of merging galaxies From Maurogordato et al. 2000

  32. Dynamical Analysis New observational data: 150 z Variation of v and s along the general axis of the cluster: In the central ridge: very high velocity dispersion, low mean velocity. Signatures of an « old » collision. In the X-Ray group: low velocity dispersion, higher mean velocity. Probably infalling group towards the main cluster. s <v> Velocity distribution: non gaussian. Well fitted by a mixture of three gaussian distributions. v From Ferrari et al. 2003

  33. Witnessing the collision of the Northern group with the main cluster From Ferrari et al. 2003 From Arnaud et al. 2003 Compression of the gas by the colliding group: Increase of Temperature in between the colliding units (detected by Chandra) Triggering of star-formation (excess of younger population in the compression region)

  34. MUSIC: the program MUlti-wavelength Sample of Interacting Clusters S. Maurogordato, C. Ferrari, C.Benoist, E. Slezak, H. Bourdin, A. Bijaoui (OCA) J.L. Sauvageot, E. Belsole, R. Teyssier, M. Arnaud (CEA-CEN Saclay) L.Feretti, G.Giovannini (IRA Bologne) 10 clusters at different stages of the merging process, 0.05 < z < 0.1 X-Ray: XMM/EPIC Optical: 3-bands (V,R,I) wide-field imaging (ESO: Wfi@2.2m, CFHT: Cfh12K@3.6m) Multi-Object Spectroscopy (ESO: Efosc2@3.6m, next VIMOS@UT2, CFHT: MOS@3.6m) Radio: VLA

  35. MUSIC: Scientific Objectives • Characterize the merging process: velocity field and mass ratio of the components, axis and epoch of collision. Reconstruction of the merging scenario by numerical simulation. • Compare the respective distribution of galaxies, gas and dark matter according to the dynamical stage of the merging process. • Test for correlation between Star Formation Rate and gas compression • Large scale environnement. Do merging clusters preferentially occur at the crossing of filaments as predicted by hierarchical scenarios of structure formation ?

  36. MUSIC: the targets A 1750 Pre XMM/ESO A 2933 Pre A 3921 Mid XMM/ESO A 2440 Pre A 2384 Mid A 2142 Post XMM/CFH A 2065 Post XMM/CFH A 4038 Post

  37. Alignments effects in galaxy clusters PAI Platon: OCA (S.Maurogordato), NOA (M. Plionis) 300 Abell clusters Strong alignment effect for clusters within superclusters: BCG / cluster 10 brightest galaxies / cluster The case of Abell 521 Strong alignment of groups with the main orientation of the cluster

  38. The fundamental plane of galaxy clusters: another evidence for hierarchical clustering Galaxy clusters: L = K Ras2b a= 0.89, b=0.64 Galaxy clusters Elliptical galaxies Dwarfs galaxies Globular clusters From Schaeffer, Maurogordato, Cappi and Bernardeau 1993

  39. Future: Analysis of the cluster distribution in the CFHTLS Galaxy catalog Cluster catalog by identification of the Red Sequence of ellipticals

  40. Constraining the hierarchical model: I: Evolution of cluster counts with redshift: Slice of 10°x10° From Evrard et al. 2003

  41. II- Evolution of correlation length with richness From Colberg et al. 2000

  42. CONCLUSIONS Multiple evidences for the hierarchical model: Scale invariance in the galaxy and cluster distribution, Fundamental plane for structures of very different masses, Properties of merging clusters. « Concordant model »: LCDM with Wm=0.3, WL=0.7 agrees with most results of observational cosmology but still room for other alternatives… Next future: Theory + Numerical simulations + Observations: Which hierarchical model ? Better understanding of the bias relation Nature of primordial fluctuations Test of the Cosmological Principle

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