Omar López-CruzInstituto Nacional de Astrofísica, Optica y Electrónica (INAOE)Sta. María Tonantzintla, Puebla, Méxicoomarlx@inaoep.mx

Cluster Identification and Galaxy Populations

Bernard´s Cosmic StoriesValencia, Spain, 2006

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

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.

Cluster Finding Techniques

Density enhancements above the field galaxy counts

Based on the intrinsic properties of clusters

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.

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)

A Desirable calibration M-Lopt

Popesso et al. ’05:

Lopt from i-band SDSS data

SDSS Mdyn: open squares

MX from ASCA data: filled circles.

Lopt a is a better mass proxy than richness.

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

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.

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

RichnessSimple 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 h180-1Mpc = 1.5h100

-

-Poorly correlated with modern measurements

Gal et al. 2003

RichnessΛcl - equivalent number of L* galaxies within some radius in a clusterLtot = Λcl x L*

Correlates luminosity & richnessUsed by Kim et al., Kepner et al. on SDSS data

Ngal from Annis et al. BCG technique - number of galaxies within 2σ of E/S0 CMR brighter than L*+1-gives smaller numbers due to color limitation-may vary with cluster pops

Measures correlated but noisy

RichnessBgc - amplitude of galaxy - cluster correlation ζ(r)=Bgc rγ

Taken from radio studies (Longair & Seldner 1979)Yee & López-Cruz (1999) measured Bgc 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!Bgc requires knowledge of the LF and its evolution, and assumes spherical

symmetry for deprojection. γ=-1.77

Expected

Bgc vs. R

Vel. Disps require

10 zs.

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.

Classification of Galaxies

What is useful and what is not...

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=1013 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

Classification of Clusters of Galaxies

IrregularRegular

All the proposed schemes underline a sequence from irregular to regular

The Rood-Sastry Classification Scheme

The Hercules Clusteran example of an irregular cluster

Irregular Regular

Coma, entire cluster

Two Classification Schemes

Bautz-Morgan Types (1970)

I -clusters 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)

Cluster classifications:

A1213A194,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

A Simplified SchemeFrom 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))

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)

Morphology

Radial profilesIdeally, 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 ΣN(R) - projected number density profile σp(R) -projected velocity dispersion profile

Fit to projection of Hernquist profileCould also use NFW

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

MorphologySubstructure

Merging clusters, infalling groups

Rate predicted by CDM, related to Ωm (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 tHubble

More substructure at high z (Jeltema 2004)2dF results show high rate of substructure in poor clusters (Burgett et al.2004) -supports long relaxation timesAlignment 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.

The Magnitude Zoo

Aperture MagnitudesIsophotal MagnitudesPetrosian radius (mag)=2.5log(5d log r/dmag) 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

NGC 3377

Surface Brightness Profile

Growth Curve

Petrosian Radius

Image from Sandage & Perelmuter 1990

Surface Brightness Profilesand 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=7.22Re2Ie.

The growth curve isF(R)=L

Tot[1-exp(-z){1+

n=1...7 (zn/n!)}],

where z=7.67(R/Re)1/4

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)

R=14 R=23

Redshift

Distance Modulus

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?

Pointed Observations of low-z Clusters.

Over 160 000 photometrical measurements (galaxies, stars, and garbage). 63 350 galaxies at the 5σ. 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)

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

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

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

Galaxy Morphology forGalaxies 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---Spirals0.4≤ B/T<0.6 ---S00.6≤ B/T≤1.0 ---E

Gutiérrez et al. (2004).

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?

Galaxy Morphology

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

Sérsic Index vs. Luminosity

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

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)

CLJ1251, z=1.235, inside 1 M pc (~2 arcmin)

Blakelessle et al. 2003

Coma

filled circles Efilled squares S0

An economical redshift indicator

Dispersion about the fit 0.010

A cluster finding tool

Isopleths

This is the basis for the MikeGladders´ RSCS.

Background contamination important for substructures studies and LF estimation.

X-rays

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 atz~2.3

The Size-Magnitude Relation

Salperter IMF

Schade, Barrientos, & López-Cruz (1997)

Kormendy Relation

μe=(3.5 +/-0.17)log(Re) + (19.4+/-0.11)

Añorve (2006)

μe=(3.5 +/-0.2)log(Re) + (19.4+/-0.4)

Coenda et al.(2005)

It is universal for cluster galaxies

Changes can be explained assuming passive evolution.Data from Jorgensen et al. (1999)

Fundamental Plane for Coma Galaxies using Sérsic´s Law

logRe=1.29(log(σ)+0.29<μ>e)-5.8σ taken from Jorgensen, Franx, & Kjaergaart (1995).

The FP is a probe for galaxy evolution.

FP evolution 0 <z<0.6, K band.

Pahre, Djorgovski, & de Carvalho (2005)

Dynamical Effects in Clusters

Cluster Mean Tidal FieldMergers Collisional Tidal StrippingDynamical Friction CannibalismHarassments

dEs trace the cluster better!!

dIr & dSph are missing in the center

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~1.2Stellar Colors !!!!

Signs of Disruption

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

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 maintypes: cD, spiral poor, and spiral rich. Clear variationswere 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.

Observers have messed up!!

Driver et al. (1994) found a steep faint-end slope 1.8. Suggesteda 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 Propiset 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.

We do not detect an universal pattern

Christlein & Zabludoff (2003)

LF generated using redshifts

We have identified a group of clusters that we have termed flat-LF clusters.

Rich clusterscD galaxies (B-M I, I-II)Very luminous X-rays clusters, single-picked

Variations at the bright-end of the LF

Variations at the bright-end of the LF

The whole sample

Bgc (Yee & López-Cruz 1999)

Variations at the bright-end of the LF

cD Clusters

Variations at the bright-end of the LF

non-cD Clusters

<M*>=-22.32+2.5logh50

=0.26

Variations at the bright-end of the LF

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?

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)

A few details....

We use the a classifier C2 that measures the compactness of an

object, it is defined as

C2= (N

A-2)-1(m*

i-m

i)-C

0 ,

where NA

is the adopted largest aperture; mi and m*

i are the

instrumental magnitudes at the ith aperture of the object and aselected 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 Kpcapertures.

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