1 / 73

Small Galaxy Groups Clustering and the Evolution of Galaxy Clustering

Small Galaxy Groups Clustering and the Evolution of Galaxy Clustering. Leopoldo Infante Pontificia Universidad Católica de Chile. Bonn, June 2005. Talk Outline. Introduction The Two-point Correlation Function Clustering of Small Groups of Galaxies – SDSS results

jun
Download Presentation

Small Galaxy Groups Clustering and the Evolution of Galaxy Clustering

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Small Galaxy Groups Clusteringand the Evolution of Galaxy Clustering Leopoldo Infante Pontificia Universidad Católica de Chile Bonn, June 2005

  2. Talk Outline Introduction The Two-point Correlation Function Clustering of Small Groups of Galaxies – SDSS results Evolution of Clustering – MUSYC results Conclusions

  3. Rich Clusters Bias Groups Bias Galaxies Bias

  4. How do we characterizeclustering? Correlation Functions and/or Power Spectrum

  5. Random Distribution 1-Point 2-Point N-Point dV1 Clustered Distribution r 2-Point dV2

  6. Continuous Distribution Fourier Transform Since P depends only on k

  7. In Practice 2-Dimensions - Angles Estimators B A

  8. r0 vs dc On the one hand, The Two point Correlation Function is an statistical tool that tells us how strongly clustered structures are. • Amplitud (A), or • Correlation length (r0) On the other, we need to characterize the structure in a statistical way • Number density (nc) • Inter-system distance (dc)

  9. The co-moving Correlation Length

  10. Assumed Power Law 3-D Correlation Function Proper Correlation distance Clustering evolution index Proper Correlation length Assumed Power Law Angular Correlation Function

  11. To go from r • Must do a 2D  3D de-projection • Limber in 1953 developed the inversion tool • Two pieces of information are required: • A Cosmological Model • The Redshift Distribution of the Sample

  12. Proper Correlation Length and Limber’s inversion

  13. With z information • Redshift space correlation functions • Given sky position (x,y) and redshift z, one measures s • Sky projection, p, and line of sight, , correlation functions • Given an angle, , and a redshift, z, one measures rp,   Problem; choose upper integration limit

  14. Inter-system distance, dc

  15. Proper Volume Space density of galaxy systems Mean separation of objects As richer systems are rarer, dc scales with richness or mass of the system

  16. CLUSTERINGMeasurements from Galaxy CatalogsandPredictions from Simulations

  17. Galaxy Clustering: Two examples APM angular clustering SDSS spatial clustering

  18. APM

  19. Sloan Digital Sky Survey • 2.5m Telescope • Two Surveys • Photometric • Spectroscopic • Expect • 1 million galaxies withspectra • 108 galaxies with 5 colors • Current results • DR2 • 2500 deg.2 • 200,000 galaxies, r<17.7 • Median z  0.1

  20. SDSS DR2

  21. Zehavi et al., 2004

  22. Clustering of Galaxy Clusters Richer clusters are more strongly clustered. Bahcall & Cen, 92, Bahcall & West, 92  However this has been disputed: • Incompleteness in cluster samples (Abell, etc.) • APM cluster sample show weaker trend

  23. Galaxy Groups Clustering Simulations 2dFGG clustering LCDCS clustering SDSS DR2 clustering

  24. N body simulations • Bahcall & Cen, ‘92, ro dc • Croft & Efstathiou, ‘94, ro dc but weaker • Colberg et al., ‘00, (The Virgo Consortium) • 109 particles • Cubes of 3h-1Gpc (CDM) CDM =0.3 =0.7 h=0.5 =0.17 8=0.9

  25. CDM dc = 40, 70, 100, 130 h-1Mpc Dark matter

  26. 2dF data, 2PIGG galaxy groups sample Ecke et al., 2004 19,000 galaxies 28,877 groups of at least 2 members <z> = 0.11

  27. Padilla et al., 2004 Groups 2PIGG Galaxies 2dFGRS

  28. Las Campanas Distant Cluster Survey Gonzalez, Zaritsky & Wechler, 2002 • Drift scan with 1m LCO. • 1073 clusters @ z>0.3 • 69 deg.2 • 78o x 1.6o strip of the southern sky (860 x 24:5 h-1 Mpc at z0.5 for m=0.3 CDM). • Estimated redshifts based upon BCG magnitud redshift relation, with a 15% uncertainty @ z=0.5.

  29. Gonzalez, Zaritsky & Wechler, 2002

  30. Clustering of Small Groups of Galaxies from SDSS

  31. Objective: Understand formation and evolution of structures in the universe, from individual galaxies, to galaxies in groups to clusters of galaxies. • Main data: SDSS DR1 • Secondary data: Spectroscopy to get redshifts. • Expected results: dN/dz as a function of z, occupation numbers (HOD) and mass. • Derive ro and d=n-1/3  Clustering Properties

  32. Bias • The galaxy distribution is a bias tracer of the matter distribution. • Galaxy formation only in the highest peaks of density fluctuations. • However, matter clusters continuously. • In order to test structure formation models we must understand this bias.

  33. Halo Occupation Distribution, HOD Bias, the relation between matter and galaxy distribution, for a specific type of galaxy, is defined by: • The probability, P(N/M), that a halo of virial mass M contains N galaxies. • The relation between the halo and galaxy spatial distribution. • The relation between the dark matter and galaxy velocity distribution. This provides a knowledge of the relation between galaxies and the overall distribution of matter, the Halo Occupation Distribution.

  34. In practice, how do we measure HOD? • Detect pairs, triplets, quadruplets etc. n2 in SDSS catalog. • Measure redshifts of a selected sample. • With z and N we obtain dN/dz • Develop mock catalogues to understand the relation bewteen the HOD and Halo mass

  35. OUR PROJECT: We are carrying out a project to find galaxies in small groups using SDSS data. Collaborators: M. Straus N. Padilla G. Galaz N. Bahcall & Sloan consortium

  36. The Data • Seeing  1.2” to 2” • Area = 1969 deg2 • Mags. 18 < r < 20

  37. Selection of Galaxy Systems • Find all galaxies within angular separation between2”<<15” (~37h-1kpc) and 18 < r < 20 • Merge all groups which have members in common. • Define a radius group: RG • Define distance from the group o the next galaxy; RN • Isolation criterion: RG/RN  3 Sample 3980 groups with 3 members pairs68,129 Mean redshift = 0.22  0.1

  38. Galaxy pairs, examples Image inspection shows that less than 3% are spurious detections

  39. Galaxy groups, examples

  40. Results arcsec arcsec A = 13.54  0.07  = 1.76 A = 4.94  0.02  = 1.77

  41. Results pairs triplets galaxies • Triplets are more clustered than pairs • Hint of an excess at small angular scales

  42. Space Clustering Properties-Limber’s Inversion- Calculate correlation amplitudes from () Measure redshift distributions, dN/dz De-project () to obtain ro, correlation lengths Compare ro systems with different HODs

  43. The ro - d relation Amplitude of the correlation function Correlation scale Mean separation As richer systems are rarer, d scales with richness or mass of the system

  44. Rich Abell Clusters: • Bahcall & Soneira 1983 • Peacock & West 1992 • Postman et al. 1992 • Lee &Park 2000 • APM Clusters: • Croft et al. 1997 • Lee & Park 2000 Galaxy Triplets EDCC Clusters: Nichol et al. 1992 • X-ray Clusters: • Bohringer et al. 2001 • Abadi et al. 1998 • Lee & Park 2000 LCDM (m=0.3, L=0.7, h=0.7) SCDM (m = 1, L=0, h=0.5) Governato et al. 2000 Colberg et al. 2000 Bahcall et al. 2001 • Groups of Galaxies: • Merchan et al. 2000 • Girardi et al. 2000

More Related