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Omar L

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Bower, Lucey & Ellis, 1992 studied Virgo and Coma, the CMR was recognized as a ... Signs of Disruption. Luminosity Function. Most studies fit to Schechter ... – PowerPoint PPT presentation

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Title: Omar L


1
Cluster Identification and Galaxy
Populations Bernards Cosmic Stories Valencia,
Spain, 2006
Omar López-Cruz Instituto Nacional de
Astrofísica, Optica y Electrónica (INAOE) Sta.
María Tonantzintla, Puebla, México omarlx_at_inaoep.m
x
2
Galaxies are just a tiny fraction of the
clusters 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-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.
8
Intrisic Properties of Clusters
X-ray identification (Ettoris 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)
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 z0.3-0.5 DPOSS, APM, SDSS
  • Future surveys to z1 RCS2, LSST, Pan-STARRS
  • Detection measurement of basic properties does
    not require deep data
  • Two filters already good for rough photo-zs 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 m3ltmltm32
  • within radius of .83 h180-1Mpc
    1.5h100-
  • -Poorly correlated with modern measurements

Gal et al. 2003
12
Richness
  • ?cl - equivalent number of L galaxies within
    some radius in a cluster
  • Ltot ?cl x L
  • Correlates luminosity richness
  • Used by Kim et al., Kepner et al. on SDSS data
  • Ngal from Annis et al. BCG technique - number of
    galaxies within 2s of E/S0 CMR brighter than L1
  • -gives smaller numbers due to color limitation
  • -may vary with cluster pops
  • Measures correlated but noisy

13
Richness
  • Bgc - amplitude of galaxy - cluster correlation
    ?(r)Bgc r?
  • 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.
  • RAbell is overstimated for clusters at zgt0.1!
  • A655 (z0.18) the only R5 cluster is not that
    Rich!
  • Bgc requires knowledge of the LF and its
    evolution, and assumes spherical symmetry for
    deprojection. ?-1.77

Expected Bgc 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 GalaxiesWhat 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 LcD1013
    h50-2 Lsun ).
  • 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
Regular
Irregular
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
  • 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)
  • 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)

22
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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.
    Maurogordatos 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
  • SN(R) - projected number density profile
  • sp(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 Om (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 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 ?-test
    (Dressler Schechtman 1988), etc.

28
The Magnitude Zoo
  • Aperture Magnitudes
  • Isophotal Magnitudes
  • Petrosian radius
  • ? (mag)2.5log(5d 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 color0)

slope of the growth curve
29
NGC 3377
Surface Brightness Profile
Growth Curve
Petrosian Radius
Image from Sandage Perelmuter 1990
30
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
  • Ltot7.22?Re2Ie.
  • The growth curve is
  • F(R)LTot1-exp(-z)1?n1...7 (zn/n!),
  • where z7.67(R/Re)1/4

31
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32
Distance Modulus
Re d s h i ft
R14

R23
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 5s. The completeness limit of R21.5
    mag, 0.9m T2KA (23.2 arcmin X 23.2 arcmin FOV)
  • 9 clusters 0.02ltzlt0.04 clusters were observed
    with KPNO 0.9m telescope MOSA (1 deg X 1 deg
    FOV)
  • 1 Mpc lt ? lt 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 gt 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 forGalaxies in the Coma Cluster
GALFIT (Peng et al. 2002) Sérsic Bulge Exp.
Disk 0.0 B/Tlt0.4---Spirals 0.4 B/Tlt0.6
---S0 0.6 B/T1.0 ---E
Gutiérrez et al. (2004).
-1 Spirals, 0 S0 ,
1 E
39
CMR by galaxy types for 11 Abell Clusters zlt0.05
How do S0s form?
Christopher Añorve (GH2005, poster)
Añorve 2006, M.Sc.Thesis, INAOE
40
Galaxy Morphology
Distribution of the Sérsic Index similar to
Blanton et al. (2003)
41
Sérsic Index vs. Luminosity
42
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43
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44
CLJ1251, z1.235, inside 1 M pc (2 arcmin)
Coma
filled circles E filled squares S0
Blakelessle et al. 2003
45
An economical redshift indicator
Dispersion about the fit 0.010
46
A cluster finding tool
This is the basis for the Mike Gladders
RSCS. Background contamination important for
substructures studies and LF estimation.
X-rays
Isopleths
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 z2.3
48
The Size-Magnitude Relation
Salperter IMF
Schade, Barrientos, López-Cruz (1997)
49
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)
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
Sérsics Law
logRe1.29(log(s)0.29ltµgte)-5.8 s taken from
Jorgensen, Franx, Kjaergaart (1995).
52
The FP is a probe for galaxy evolution.
FP evolution 0 ltzlt0.6, 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
Signs of Disruption
MKW 7 Tidal Debris Plume (Feldemeier et al. 2002)
CentaurusTidal Debris Plume (Cálcaneo-Roldán et
al. 2002). The plume is 8 arcmin long (gt100 Kpc)
B-V0.9, V-R0.6, V-I1.2 Stellar Colors !!!!
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
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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
LF generated using redshifts
Christlein Zabludoff (2003)
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
Variations at the bright-end of the LF
The whole sample
Bgc (Yee López-Cruz 1999)
65
Variations at the bright-end of the LF
cD Clusters
66
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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 zgt3.
  • 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
fgal0.19fgas? Allen (2005)
Integrated light due to galaxies in CMR within 1
Mpc
70
A few details....
We use the a classifier C2 that measures the
compactness of an object, it is defined as
C2 (NA-2)-1? (mi-mi)-C0
, where NA is the adopted largest aperture mi
and mi are the instrumental magnitudes at the
ith aperture of the object and a selected
reference star, respectively, and C0 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
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