1. Omar López-Cruz Instituto Nacional de Astrofísica, Optica y Electrónica (INAOE) Sta. María Tonantzintla, Puebla, México omarlx@inaoep.mx Cluster Identification and Galaxy Populations Bernard´s Cosmic Stories Valencia, Spain, 2006

2. Galaxies are just a tiny fraction of the cluster´s mass; so, Who cares? Jerry Ostriker, CITA Seminar, ca. 1995. Galaxies are fair tracers of the dark matter... Stefano Borgani, Kona 2005, Craig Sarazin, GH2005, Roy Gal, GH2005

3. Summary How we go about finding cluster (biased towards optical searches, X-ays (Ettori) and SZE (Sunyaev)) General Properties of Clusters: Richness, Morphology, and Density Profiles. 2-D Surface Brightness modeling, B/T, andGalaxy Morphology. The Color-Magnitude Relation, The Size-Magnitude Relation and the more Complete Fundamental Plane. The Luminosity Function of Galaxies in Clusters and The Effects of the Environment on Cluster Galaxies. Conclusions.

4. Cluster Finding Techniques Density enhancements above the field galaxy counts Based on the intrinsic properties of clusters

5. Finding Overdensities in the Optical Counting galaxies: Abell ‘58, Zwicky et al. ‘68, ACO (Abell et al. ’89), APM (Dalton et al. ’92), EDSCC (Lumsden et al. ’92), PDCS (Postman et al. ’96), EIS (Olsen et al. ’99), LCDCS (surface brightness fluctuations; Gonzalez et al. ’01) NoSOCS (Gal et al.2004), Voronoi Tessalation (Kim et al. 2002, Lopes et al. 2004) Pros: easy to implement Cons: - fairly large contamination, mostly for relatively poor and distant systems  ill calibrated selection function. - loose correlation between richness and cluster mass.

6. Using Galaxy Properties Adding color infomation (color-magnitude relation) reduces the effects of contamination: RCS (Gladders & Yee 2005), SDSS (e.g. Miller et al. 2005, Wilson 2005 (Spitzer)) Pros: - cheap! Easy to cover large area with modern large CCD frames on large FOV telescopes; - color information suppress contamination and false detections: efficient cluster detection out to z  1. - acceptable correlation with cluster mass: requires accurate photometry and photometric redshifts (free); Cons: calibration of the selection function: difficult from first principles; requires calibration with simulations (Montecarlo or N- body)

7. A Desirable calibration M-L opt Popesso et al. ’05: L opt from i-band SDSS data SDSS M dyn : open squares M X from ASCA data: filled circles. L opt a is a better mass proxy than richness.

8. X-ray identification (Ettori´s talk) Pre-ROSAT: HEAO-1, EMSS (Gioia et al. ’90), Jones & Forman (1999) RASS: XBACS (Ebeling et al. ’97), BCS (Ebeling et al. ’01), REFLEX (Boehringer et al. ’04), NORAS (Boehringer et al. ’00), NEP (Gioia et al. ‘03), MACS (Ebeling et al. ‘01) ROSAT deep pointings: RDCS (Rosati et al. ’02), 160sq.deg. (Mullis et al. ’04), SHARC (Burke et al. ‘03), WARPS (Perlman et al. ‘02), BMW (Moretti et al. ’04) Several ongoing XMM-Newton and Chandra surveys. Pros: - Calibration of the selection function possible Cons: X-ray flux sensitive to details of the gas distribution  connection to mass requires external calibration or follow-up observations (e.g., T,  v, Compton-y, lensing) Intrisic Properties of Clusters

9. Intrinsic Cluster Properties SZ identification (Sunyaev) - next to come: SZA, ACT, SPT, APEX, Planck, BOLOCAM, OCRA,GTM Pros: - No redshift dimming: clusters identified virtually at any redshift; - Selection criterion essentially equivalent to a mass-selection one. Cons: - Contamination from radio sources (apply multi-frequency observations); - Contamination from fore/back-ground structures.

10. Optical Measurements Optical observations are extremely efficient At low-moderate redshift, ground based telescopes sufficient Current surveys to z=0.3-0.5: DPOSS, APM, SDSS Future surveys to z~1+: RCS2, LSST, Pan-STARRS Detection & measurement of basic properties does not require deep data Two filters already good for rough photo-z’s and CMDs Can detect poor systems & groups where most galaxies reside

11. Richness Simple galaxy counting -in what radius -what mag limits? -color cuts? Observationally & computationally inexpensive - but can it be a proxy for mass, which is what we want? Abell (1958) - # of galaxies with m3<m<m3+2 within radius of.83 h 180 -1 Mpc = 1.5h 100 - - Poorly correlated with modern measurements Gal et al. 2003

12. Richness Λ cl - equivalent number of L* galaxies within some radius in a cluster L tot = Λ cl x L* Correlates luminosity & richness Used by Kim et al., Kepner et al. on SDSS data N gal from Annis et al. BCG technique - number of galaxies within 2 σ of E/S0 CMR brighter than L*+1 -gives smaller numbers due to color limitation -may vary with cluster pops Measures correlated but noisy

13. Richness B gc - amplitude of galaxy - cluster correlation ζ(r)=B gc r γ Taken from radio studies (Longair & Seldner 1979) Yee & López-Cruz (1999) measured B gc for 47 Abell clusters, previous work by Prestage & Peacock(1988,1989) Robust against magnitude cuts and radial coverage. R Abell is overstimated for clusters at z>0.1! A655 (z=0.18) the only R=5 cluster is not that Rich! B gc requires knowledge of the LF and its evolution, and assumes spherical symmetry for deprojection. γ=-1.77 Expected B gc vs. R Vel. Disps require 10 zs.

14. A few Words on Galaxy and Cluster Classifications ¨There is a mask of theory over the phase of Nature Schemes should be based on a quantifiable variable property. Useful schemes strike a fundamental property that is related to physical processes. Categories: first kind (purely descriptive), second kind (quantifiable varying properties, but no physical mechanism), third kind (quantifiable varying property rooted on physical processes, e.g. MK classification of stars), Fourth kind (rooted on a fundamental physical process, e.g., the Periodic Table of the elements) For galaxies and clusters our schemes are only second kind!!! Our cosmic inventory is not complete. And we do not have a compelling theory for galaxy formation, yet.

15. Classification of Galaxies What is useful and what is not...

16. What is a cD galaxy? cD are supergiant galaxies up to 4 mags. brighter than M*. They concentrate almost half of the total cluster light ( in the R-band L cD =10 13 h 50 -2 L sun ). cD galaxies are only found in clusters, independent of cluster richness. They can have blue cores and multiple nuclei cD are often powerful radio-galaxies (WAT), in fact the term cD galaxy was introduced in a study of optical counterparts of luminous radio-galaxies (Matthews,Morgan, & Schmidt, 1964). The first 10 cD galaxies discovered: A389, A401, A754, A787, A1775, A1795, A1904, A2029, A2199, & A2670

17. Classification of Clusters of Galaxies Irregular Regular All the proposed schemes underline a sequence from irregular to regular The Rood-Sastry Classification Scheme

18. The Hercules Cluster an example of an irregular cluster

19. Irregular Regular

20. Coma, entire cluster

21. Two Classification Schemes Bautz-Morgan Types (1970) I - c lusters containing a centrally located cD galaxy (A2199, A2029) I-II - Intermediate II - Between cD and Virgo gEs (A2197) II-III Intermediate (A426, A400) III No dominant galaxy (Virgo, A2065) III-E (with ellipticals) III-S (with spirals) Rood-Sastry(1971, Struble & Rood 1982) cD – cluster that contain a cD (A401) B – (binary) two BCG of similar brightness (A1656) L –(line) three or more galaxies line up (A426) C- (core-halo) the 4 BCG located near the center (A2065) F (flat) galaxies in flattened configuration (A397) I (irregular) (A400), Is (smooth), Ic (clumpy)

22. Cluster classifications: A1213 A194,A539A2199Examples Very littleModerateHighCentral concentration IrregularIntermediateSphericalSymmetry 1:2:31:4:23:4:2E:S0:S ratio Spiral-richSpiral-poorE/S0 richContent IIIII-IIII,I-II,IIBautz-Morgan IrregularIntermediateRegularProperty/Class

23. A Simplified Scheme From X-ray observations it seems that cooling core cluster have different properties from those without them, as seen by their morphological structure, temperature structure and metallicity (De Grandi et al. 2004) It has been recently recognized that cluster-cluster merger are frequent. (see S. Maurogordato´s talk) RS B- clusters are clusters are merging clusters (Tremaine 1989). cooling core clusters are cD clusters (BM I, I-II). Three classes: cD, non-cD and Mergers (RS B-clusters, presence radio relics, and halos (Feretti 2006))

24. Morphology Modern methods: PA, ellipticity (Binggeli 1982) - alignments along filaments Moments of galaxy distribution (Rhee et al. 1989, Plionis et al. 1991, Basilakos et al. 2000, de Theije et al.1995) Flatness - inverse of ellipticity or elongation (Struble & Ftaclas 1994) Fitting  -models (Strazzullo et al. 1995)

25. Morphology Radial profiles Ideally, we would like 3-d mass distributions X-ray temperature, surface brightness profiles can be used for comparison to simulations (Loken et al. 2002, Arnaud et al. 2002, Markevitch et al. 1999) Optical data requires lots of spectroscopy (such as CNOC, SDSS) Carlberg et al. 1997 derive Σ N (R) - projected number density profile σ p (R) -projected velocity dispersion profile Fit to projection of Hernquist profile Could also use NFW

26. Morphology Radial profiles in cluster cores -CDM models make specific prediction of universal mass profile -Lensing (strong + weak) can be used to test mass profile, compare with light, x-rays -Need for multiple radial + tangential arcs to distinguish NFW vs. isothermal (Gavazzi et al. 2003) -Arcs useful for accessing central density profiles - NFW r -1, Moore r -1.5 or other (Molikawa & Hattori 2001) -Inner slope may be as low as 0.5 (Sand et al. 2004) suggesting complex mass-light relationship in cluster centers -NFW profiles are only for collisionless CDM particles; baryons can behave differently - need to add to simulations (See Session 7) cD/BCG galaxies may have an appreciable effect A383 mass model Sand et al. 2004

27. Morphology Substructure Merging clusters, infalling groups Rate predicted by CDM, related to Ω m (Buote 1998) Detailed studies in optical are “recent” (Geller & Beers 1982,West & Bothun 1990) Lensing contraints (Natarajan & Springel 2005) Evolution with time - dynamical times comparable to t Hubble More substructure at high z (Jeltema 2004) 2dF results show high rate of substructure in poor clusters (Burgett et al.2004) -supports long relaxation times Alignment with filaments stronger for dynamically active clusters (Plionis & Basilakos 2002) X-ray and optical substructure are correlated (Kolokotronis et al. 2001, Rosati et al. 2002) Different measures but need to compare observations & simulations Wavelets (2-d, Girardi et al. 1999), Lee statistic (Fitchett 1988), skewness/kurtosis (Bird & Beers 1993), subclumps via Δ-test (Dressler & Schechtman 1988), etc.

28. The Magnitude Zoo Aperture Magnitudes Isophotal Magnitudes Petrosian radius  (mag)=2.5log(5 d log r/ d  mag) Total or asymptotic magnitudes (parametric or non-parametric) Vega base magnitudes (based on the SED of Vega) AB magnitudes (same zero point for all filters. A source with a flat SED will have color=0) slope of the growth curve

29. NGC 3377 Surface Brightness Profile Growth Curve Petrosian Radius Image from Sandage & Perelmuter 1990

30. Surface Brightness Profiles and Curve of Growth The surface brightness profile and the growth curve are related, e.g., for the de Vaucouleurs profile I(R)=Ioexp{-7.67([(R/Re) 1/4 -1]}; L tot =7.22  Re 2 Ie. The growth curve is F(R)=L Tot [1-exp(-z){1+  n=1...7 (z n /n!)}], where z=7.67(R/Re) 1/4

31. The Color-magnitude Relation The CMR was first discovered by W. Baum (1959). Back then, globular clusters and elliptical galaxies (ETG) were though to be Pop II. But ETG were much redder; hence ETG were different from globular cluster. At that time a revolutionary idea was in the air: the Progressive metal enrichment of a given population by successive generations of stars (Fowler & Greenstein 1956, Struve 1956). Baum proposed ETG are made of old Pop I stars. The work of Sandage (1972), Faber (1971, 1972) and Sandage & Visvanathan (1977, 1978), established that the CMR is a linear relationship. They didn´t see any difference for the CMR for field and cluster galaxies. Bower, Lucey & Ellis, 1992 studied Virgo and Coma, the CMR was recognized as a probe of galaxy formation. The CMR has been found at every possible redshift (e.g., Ellis et al. 1997, Stanford et al. 1998, van Dokum et al. 1998, Barrientos et al. 2003, Blakessle et al. 2003)

32. R=14 R=23 RedshiftRedshift Distance Modulus

33. A few questions: " Can the CMR be seen in every nearby cluster? " Is the CMR affected by the environment ( i.e., cooling flows), temperature gradients, AGNs (radio, X-ray) ? " When did ETG form? " When are the effects of the environment important? " Are the optical and Cluster X-ray properties related?

34. Pointed Observations of low-z Clusters. Over 160 000 photometrical measurements (galaxies, stars, and garbage). 63 350 galaxies at the 5 σ. The completeness limit of R=21.5 mag, 0.9m + T2KA (23.2 arcmin X 23.2 arcmin FOV) 9 clusters 0.02<z<0.04 clusters were observed with KPNO 0.9m telescope + MOSA (1 deg X 1 deg FOV) 1 Mpc < ⊘ < 4 Mpc), with a resolution of 0.68''/pixel. X-ray selected (Jones & Forman 1999) Abell Clusters (ARC ≥ 0) and 7 control fields in R and B. Star/galaxy classifications and photometry using PPP (Picture Processing Program, Yee (1991), Yee et al. (1996) López-Cruz, Barkhouse, & Yee (2004)

35. The CMR was found for every cluster in the sample. The CMR extends down to 8 mag. No breaks in the CMR were observed.

36. The CMR are fitted using an a robust scheme based on the biweight, the errors are derived by bootstraping.  mag

37. Galaxies with B/T > 0.7 for a sample of 28 clusters of galaxies with varying richness from Barrientos et al. 2004. If you classify the galaxies your CMR are cleaner.

38. Galaxy Morphology for Galaxies in the Coma Cluster -1 Spirals, 0 S0, 1 E GALFIT (Peng et al. 2002) Sérsic Bulge + Exp. Disk 0.0 ≤B/T<0.4---Spirals 0.4≤ B/T<0.6 ---S0 0.6≤ B/T≤1.0 ---E Gutiérrez et al. (2004).

39. CMR by galaxy types for 11 Abell Clusters z<0.05 Christopher Añorve (GH2005, poster) Añorve 2006, M.Sc.Thesis, INAOE How do S0s form?

40. Galaxy Morphology Distribution of the Sérsic Index similar to Blanton et al. (2003)

41. Sérsic Index vs. Luminosity

42. The slope of the CMR The variation in the slope of the CMR are due to k-corrections. After correcting for k- corrections and distance we found the CMR are the same within the photometric errors (0.01 to 0.1). Dispersion about the CMR 0.05 mag Kodama & Arimoto (1997) model

43. The slope of the CMR can be used as a tool for galaxy evolution. This indicator does not have troubles with calibrations since it is a ratio of colors. Conservative ETG formation z>2.5 HST Archive Gladders, López-Cruz, Yee, & Kodama 1998)

44. CLJ1251, z=1.235, inside 1 M pc (~2 arcmin) Blakelessle et al. 2003 Coma filled circles E filled squares S0

45. An economical redshift indicator Dispersion about the fit 0.010

46. A cluster finding tool Isopleths This is the basis for the Mike Gladders´ RSCS. Background contamination important for substructures studies and LF estimation. X-rays

47. Improved Cluster Finding using DTFE A690, DTFE map generated by Pablo Arayn da. Technique due to Bernardeau & van de Weygaert 1996, Shaap & van de Weygaert 2000) See posters by Platen et al. Background cluster at z~2.3

48. The Size-Magnitude Relation Salperter IMF Schade, Barrientos, & López-Cruz (1997)

49. Kormendy Relation μ e =(3.5 +/-0.17)log(Re) + (19.4+/-0.11) Añorve (2006) μ e =(3.5 +/-0.2)log(Re) + (19.4+/-0.4) Coenda et al.(2005)

50. It is universal for cluster galaxies Changes can be explained assuming passive evolution. Data from Jorgensen et al. (1999)

51. Fundamental Plane for Coma Galaxies using Sérsic´s Law logRe=1.29(log(σ)+0.29 e)-5.8 σ taken from Jorgensen, Franx, & Kjaergaart (1995).

52. The FP is a probe for galaxy evolution. FP evolution 0 <z<0.6, K band. Pahre, Djorgovski, & de Carvalho (2005)

53. Dynamical Effects in Clusters Cluster Mean Tidal Field Mergers Collisional Tidal Stripping Dynamical Friction Cannibalism Harassments

54. dEs trace the cluster better!!

55. dIr & dSph are missing in the center

56. CentaurusTidal Debris Plume (Cálcaneo-Roldán et al. 2002). The plume is 8 arcmin long (>100 Kpc) MKW 7 Tidal Debris Plume (Feldemeier et al. 2002) B-V~0.9, V-R~0.6, V-I~1.2 Stellar Colors !!!! Signs of Disruption

57. Luminosity Function Most studies fit to Schechter (1976) function  (L)dL =  *(L/L*)  exp(-L/L*)d(L/L*) Need to determine: L* : The characteristic luminosity  : The faint end slope  *: The normalization number per unit volume Do these vary among clusters, and between cluster & field ? Cluster LF from Schechter 1976

58. The CMR applied to generate LF Since the concept of LF was introduced for galaxies. There has been nothing, but troubles. It is just a histogram. i.e., the probability density for the luminosity of galaxies. The state of the art is reviewed superbly by Bingeli, Sandage, and Tammann (1988). Highlights: Oemler 1974 first study with 15 clusters. Three main types: cD, spiral poor, and spiral rich. Clear variations were pointed out. Schechter 1976, the Schechter function of course. He combined everything and universality was hinted. Dressler 1978, 9 clusters and signaled departures from universality. Dressler (1984): the variations are induced by the environment. Lugger (1986) & Colles (1989) LF is universal.

59. Observers have messed up!! Driver et al. (1994) found a steep faint-end slope  1.8. Suggested a universal trend. Trentham (1997), De Propris et al. (1997), etc, they all got steep faint-end slopes. But Driver et al. (1998) and Trentham (2002) have changed their minds. Goto et al. (2002) and De Propis et al. (2003) do not see variations Lopez-Cruz et al. (1997), Barkhouse et al. (2006), Paolillo et al. (2001), Mercurio et al. (2003) Hansen et al. (2005) find LF variations.

60. We do not detect an universal pattern

61. Christlein & Zabludoff (2003) LF generated using redshifts

62. We have identified a group of clusters that we have termed flat-LF clusters. Rich clusters cD galaxies (B-M I, I-II) Very luminous X-rays clusters, single-picked

63. Variations at the bright-end of the LF

64. The whole sample Bgc (Yee & López-Cruz 1999)

65. Variations at the bright-end of the LF cD Clusters

66. Variations at the bright-end of the LF non-cD Clusters =-22.32+2.5logh 50  =0.26

67. Variations at the bright-end of the LF

68. Conclusions Clusters can be found searching for overdensities in the optical, color information improves the success rate. Clusters can be found by other intrinsic properties (X- rays, SZ effect, lensing, etc.) Quantitative galaxy morphology possible through 2-D surface brightness modelling The CMR is a useful property for cosmological studies. It provides us with a galaxy formation clock. It can be used to find clusters and get their redshifts. Changes in the CMR and the SMR can be explained under passive evolution. It is very likely that the epoch of ETG formation happened at z>3. The fundamental plane(universal) also indicates passive evolution. The LF is not universal but shows a clear dependence with the environment. i.e., dynamical effects are important: Gus Oemler, Alan Dressler and BST were right. Suggestive differences between cD and non-cD clusters. M* for non-cD clusters could be used as a distance indicator. Do dwarf galaxies help in the formation of cD galaxies?

69. Where is it? Mgas as measured by Jones & Forman (1999) within 1 Mpc Integrated light due to galaxies in CMR within 1 Mpc fgal=0.19fgas? Allen (2005)

70. A few details.... We use the a classifier C 2 that measures the compactness of an object, it is defined as C 2 = (N A -2) -1  (m* i -m i )-C 0, where N A is the adopted largest aperture; m i and m* i are the instrumental magnitudes at the i th aperture of the object and a selected reference star, respectively, and C 0 is a normalization constant. The magnitudes that PPP try to measure are asymptotic or total magnitudes they are based on Growth-Curve analysis using circular apertures. Galaxy colors are determined using 11 h -1 Kpc apertures.

71. And we do not try to fall in the temptation of trying smart corrections. Well... just a little one