WINGS-SPE Analysis of substructures in nearby galaxy clusters Antonio Cava Physics Department - University of Milan INAF- Astronomical Observatory of Padua

WINGS-SPEAnalysis of substructuresin nearby galaxy clusters

Antonio CavaPhysics Department - University of MilanINAF- Astronomical Observatory of Padua

Antonio Cava

IAC - La Laguna 30-01-08

OAPd

OUTLINE

IAC, 30th January 2008

DATA

• the WINGS project (WINGS-spe)

• redshift measurements

• comparison with literature (NOAO/NED)

SUBSTRUCTURES IN GALAXY CLUSTERS

• Intro and methods

• 2D-substructure analysis – DEDICA

• (2+1)D-substructure analysis – DS method

• density and VD profiles (+ X-ray)

• A case study: A3558

CONCLUSIONS AND FUTURE WORK

INTRODUCTION


• WINGS-OPT

77 clustersB,V bandsWFC@INT north WFI@MPG south

47 clusters36007000 ǺWYFFOS@INT north 2dF@AAT south

• WINGS-SPE

~50 clustersJ,K bandsWFCAM@UKIRT

• WINGS-NIR

~20 clustersHalpha bandWFC@INT~1 square degree

• WINGS-HAL

LBC@LBT ~20 clustersU survey (~30’)

• WINGS-LBC

Wide-field Nearby Galaxy-cluster Survey

A Wide-Field Multiwavelength Survey

of Galaxy Clusters in the local Universe

WINGS


global properties of galaxy clusters in the local Universe

Radius, total luminosity, geometry

properties of cluster galaxies in the local Universe

morphology E:S0:Sp:Irr <µe>–re

Kinematical and dynamical properties of clusters and substructures

WINGS-spe

positions, local density, Lum.Func.

membership age-metallicity, SFH, environment

provide the scientific community with a local (0.04<z<0.07) benchmark for evolutionary studies

Clusters

Galaxies

B/D ratioColorColor grad.

Scientific targets of WINGSScientific targets of WINGS

Questions:

Which are the typical features of galaxy clusters in local universe? Can be considered simple objects in the cosmological context? Can be described in a simple way in term of internal kinematics?Are they dynamically relaxed and old?

WINGS-SPE

The WINGS-SPE sample is constituted by a subsample (47 clusters) of the WING-Survey (77 clusters):

• 22 clusters for the south (2df@AAT) ~ 4500 redshifts

• 25 cluster for the north (WYFFOS@WHT) ~ 1500 redshifts


The spectroscopic follow-up

The project

• multi-fibre spectra of 100-200 (brightest) galaxies in each cluster

• intermediate resolution: 3÷9Å

• spectral range: 3600÷7000Å

• selection criteria: V<20 (-16.5) 1.5/2 mag deeper than 2dFGRS/SLOAN

(B-V)<1.4

0.04 < z < 0.08

• Redshifts are measured using cross-correlation technique (Tonry&Davies,1979)

• IRAF/RVSAO xcsao/emsao packages (Kurtz&Mink, 1998)

• 15 absorption/emission templates

• Visual check of the best fit

• Skylines zero point calibration (up to ~ 50 km/s)

• Developed pipeline to produce final catalogs

• Good catalog (small errors, high reliability)• Bad catalog (rejected spectra,statistics)

Measurements


REDSHIFT : results

The results of the measusements (Cava et al.,2008a) are presented as:

• redshift histograms

• velocity diagrams (z-r)


• 22 WYFFOS@WHT

Redshift Distribution NORTH


(Cava et al.,2008a)

• 22 2dF@AAT

Redshift Distribution SOUTH

IAC, 30th January 2008(Cava et al.,2008a)

Overall redshift distribution


About ~60% of the galaxies in

the redshift catalogs have been classified

as cluster members (black

histogram)

• Redshift measured with cross-correlation + individual check high success rate and small errors

Data quality

Mean ~ 45 km/s

Median ~ 35 km/s

~ 99% of the measurements

have errors lower than 90 km/s


External comparisons – Adding more data

We have found about 1800 objects in common with the literature and 4500

objects that could be added to the final catalogs in order to increase the statistics

for dynamical analysis.

The total number of galaxies with redshift

increase to ~ 10500


REDSHIFT : results

Spectroscopic data are used to perform analysis in two main directions:

• spectro-photometric modeling (J.Fritz, B.M.Poggianti)

• kinematics and dynamics of clusters and substructures (A.Cava, in collaboration with A.Biviano and M.Ramella)


A galaxy model spectrum is computed by adding the synthetic spectra of Single Stellar Populations (SSPs) of different ages built with a Salpeter initial

mass function (IMF) with stellar masses in the range 0.15 ≤ M ≤ 120 Msun

(see Fritz, Poggianti et al., 2007)

Spectrophotometric analysis


(Fritz et al., 2007)

Spectrophotometric analysis



This will give us estimates for star formation rates and histories, as well as metallicity for the cluster galaxies from the line indices and equivalent

widths measurements .

These data are used to explore the link between the spectral properties and the morphological evolution in different density environments.


WINGS-SPE: velocity diagrams

First step in the dynamical analysis is just to look at the velocity diagrams

(i.e. redshift vs clustercentric distance) to search for particular distributions in

phase-space.


• Membership and velocity dispersion determination

Velocity diagrams - NORTH

Red points are those selected

as cluster members

according to a 3-sigma clipping

selection criteria (Yahil&Vidal,77). Estimated R200

and are given for each cluster.

Peculiar cases


• Membership and velocity dispersion determination

Velocity diagrams - SOUTH

Peculiar cases


Some details : substructures

Distributions can be multi peakedeven in more “standard cases”

Observed clusters appear as complex structures in phase-space.

This kind of observations stimulate and motivate the investigation of

substructures in clusters.


• Spatial substructuresThe main advantage of searching for substructures in the two-dimensional distribution of galaxies is the availability of large data sets, reaching thousand of positions for nearby clusters. The main drawback is its possible contamination from fore/background objects

• Velocity substructuresIn relaxed systems, the velocity distribution is expected to be Maxwellian. Therefore a non-gaussian distribution of the observed line-of-sight velocities is indicative of a non-relaxed dynamical state. However fore/background galaxies can still contaminate the velocity distribution

• Spatial-velocity substructuresThe existence of correlations between the positions and the velocities ofcluster galaxies is a footprint of real substructures. Methods that make use of both positions and velocities are certainly the most reliable but also the most demanding in terms of observational data

The methods which are commonly used to detect substructures can be grouped in three classes, detecting different kind of substructures.


Substructure detection

DEDICA-2D

Modified DS

FoF

The DEDICA procedure

The search for substructures in 2D space using DEDICA (Ramella et al.,2007) has the following advantages:

• DEDICA gives a total description of the clustering pattern

• DEDICA is scale invariant

• DEDICA does not assume any property of the clusters, i.e. it is completely non- parametric

Subclustering 2D - DEDICA

27 % without substructures

40 % with Nsub=1

19 % with Nsub=2

11 % with Nsub=3

3 % with Nsub=4

Another interesting result is that the magnitude difference between BCG1s

and BCG2s is significantly larger in clusters without substructures than in

clusters with substructures.


Subclustering (2+1)D : Dressler-Shectman method

A general problem in the detection of substructures is that observations only provide the (2+1)D

projection of the (3+3)D phase-space

We have used a modified version of the test devised by Dressler and Shectman (1988) sensitive to compact systems which have:

• an average velocity that differs from the cluster mean

• a velocity dispersion that differs from the global one


Subclustering (2+1)D : Dressler-Shectman method

We find that about 42% of clusters have a value of the DS

parameter that indicates presence of substructures (Cava et

al.,2008c).

Using only ‘red’ galaxies for the analysis we find a

significantly reduction (28%) of the number of substructured clusters.


Subclustering (2+1)D : Dressler-Shectman maps

DS maps show the presence ofSubstructures in velocity space

Using a slightly modified version of the code of Biviano&Katgert (developed to

analyse ENACS) I am now analysing the velocity dispersion and density profiles for

clusters and substructures.

Cluster profiles


NFW projected density profile (Lokas&Mamon,01) Fitted NFW

Theoretical VDP (Buote, 07)

Next step is to generate a stacked cluster to investigate

more in detail the mean properties of the clusters with varying environment (Cava et

al.,2008b)

Subcluster detection

Density, mean velocity and velocity dispersion profiles are useful tools in

subclustering detection as they provide direct evidence of the dynamical influence

of substructures on the galaxy clusters.

About 65% of the clusters in the sample present deviations in the velocity, vdp and density profiles.

Velocity dispersions: SIGMA vs LX


I’m now using the obtained global parameters (M,,R) also in

connection with morphology (MORPHOT,

G.Fasano & E.Pignatelli) to look at cluster segregation properties in different

cases (in subs and clusters).

Velocity dispersions: SIGMA vs LX


X-ray data can give important insights on the dynamical state

of galaxy clusters and subclustering properties. We

are now comparing the results from optical analisys of substructures with X-ray

oservations.

• sigma-Lx relation

• thermodynamics maps

X-ray data : thermodynamics maps


A754 (left) and A1644 (right) thermodynamics maps: Upper left: EPIC flux image of A1644; Upper right: Best fit temperature map (keV) from XMM images; Lower left : Pseudo-pressure map; Lower right: Pseudo-entropy map. (Coutesy of M.Rossetti, Milano Univ., PhD thesis)

SBTP


Gathering all togheter, a case study: A3558

E


Gathering all togheter, a case study: A3558

SUMMARY

• reduction of the spectra• measurements of all the redshifts for WINGS-SPE

• data quality check and comparison with literature

• investigation of substructures:

Analysis of V, VD and density profiles

2D-analysis with DEDICA (density

maps)

Study of substructural

properties of the clusters in (2+1)D space (DS maps)


Comparison with X-ray observations

(TD maps)


CONCLUSIONS

Questions:What is a typical cluster in nearby universe? Is it a simple object in the cosmological context? Can be described in a simple way in term of internal kinematics?Is it dynamically relaxed and old?

From our analysis we have strong indications tha galaxy clusters are very complex structures where subclustering

assumes a relevant role.

Comparing the analisys with different methods we infer that the presence of subs is higher in local universe then found in earlier works (tipical values of 30%-50%, Girardi and Biviano

2002 and refs therein):

• 73% from 2D-analysis• 42% from (2D+1)-analisys• 65% investigating radial profiles

These observations pull us toward a view of local clusters as still dynamically ‘hot’ and young

objects.A so high fraction of subs is also

indicative of a ‘low density’ universe at early stages of its

evolution (e.g.Buote 07) and consistent with a hierarchical evolution in the last 5-8 Gys

Ongoing and Future Work

• FoF analysis of substructures in 3D phase-space

• determinations of global parameters characterizing subs in relation to their parent cluster

• comparison with simulations

• comparison with distant clusters

THE END


Documents

WINGS-SPE Analysis of substructures in nearby galaxy clusters Antonio Cava Physics Department - University of Milan INAF- Astronomical Observatory of Padua