Simon Lilly (ETH Zurich), Angela Iovino, Valentina Presotto (INAF Brera)+ zCOSMOS Team

COSMOS Meeting, Honolulu10.06.2010

Christian Knobel (ETH Zurich)

Basic strategy

x

z

spec

phot

IAB ≤ 22.5

Friends-of-friends (FOF)

Voronoi (VDM)

Basic group catalog

1-way-matched sample (1WM)

pure but less complete subset

Spectroscopic component

Published & publicly available:

Knobel, Lilly, Iovino, Cucciati + zCOSMOS team et al. (2009)

Applications of the catalog:

• Role of groups in the density field (Kovac et al. 2010)

• Color as a function of environment (Iovino et al. 2010)

• Morphology as function of group environment (Kovac et al. 2009)

• AGN in groups (Silverman et al. 2009)

• Contribution to lensing analysis (Anguita et al. 2009, Faure et al. in preparation)

Sample:

10k catalog

• 800 groups, 2310 group galaxies• 502 groups for N ≥ 5

20k spectroscopic catalog

10k

1WM20k FOF

20k mocks

10k 20k Groups: 800 1681Members: 2310 5102N ≥ 5: 102 213

N ≥ 10

N ≥ 2 N ≥ 5

for N ≥ 3: ≳ 85 % complete

≳ 80 % pure

for N = 2 completeness & purity ~5-10 % lower

group purity parameter (GRP) 1WM

group robustness

velocity dispersion (for N ≥ 5)

flux (abs. mag.) limited richness

mock calibrated mass („fudge mass“)

20k spectroscopic catalog

very high confidence subsamples

Properties/features:

Δz = |zgr – zphot|

Δr

σz

rgr

Including photo-zPhotometric component

empirical fraction f( , ,N)

|Δz| / σphot

Δr / rgr

Including photo-z

|Δz| / σphot

2 ≤ N ≤ 4

N ≥ 10

Δr / rgr

Assigning probabilties

|Δz| / σphotΔr / rgr

5 ≤ N ≤ 9

fΔrrgr

Δzσz

f

f

1. Estimate fraction f( , ,N) empirically by the mocks using only galaxies associated to a single group

2. Assign probabilities to all galaxies: p = f( , ,N)

3. For galaxies associated to more than one group, the probability must be modified:

Including photo-z

Scheme of estimating probabilities:

Assigning probabilties

Δrrgr

Δzσz

Δrrgr

Δzσz

Including photo-z

Nreal

Nes

t

rel.

med

ian

rel.

quar

tiles

Nreal

Nreal

real groups

Estimated richness:

Basic strategy

Including photo-zMost massive galaxy

Introduce probability of a spectroscopic member to be associated to a group

Straightforward scheme to compute probability of each member (spec AND phot) to be the most massive:

Sort galaxies in descending order after M such that Mi-1 ≥ Mi ≥ Mi+1 :

How to determine the most massive (= central?, dominant?) galaxy in a group?

Most massive galaxyMost massive galaxy

5 ≤ N ≤ 9

3 ≤ N ≤ 4

N ≥ 10

pM

pM

pM

# ga

laxi

es

# ga

laxi

es#

gala

xies

Most groups have a clearly identifiable „most massive galaxy“

Group centerGroup center

voronoi vol. & stellar mass weighted

stellar mass weightedgeometrical mean

voronoi vol. weighted

Voronoi vol. & stellar mass weighted

Stellar mass weightedgeometrical mean

Voronoi vol. weighted

Only spectroscopic component:

Spec + phot components:

Used by Alexis

Group center

Spec + phot components:

Selecting the position of the galaxy with the largest…

voronoi volumeprobability * stellar mass

voronoi volumeprobability

Future work/applications within zCOSMOS

If you have other ideas/suggestions you are welcome to bring them in!

Analyzing central/satellite/isolated galaxies

Optical/Xray group selection comparison

Masses of optical groups (group-galaxy cross-correlation, weak

lensing, N(z)-σ relation,…)

Optical/spectroscopic properties of Xray selected group members

Investigating passives (and actives?) around log M = 10.2 as f(env) distinction between mass‐quenching and environment quenching

"Super‐group" stacked spectra, looking for radial dependence etc of

quenching ages etc.

Future work (zCOSMOS)

20k group catalog with ~1,600 groups and ~5,100 spectroscopic members Overall high completeness and high purity We are able to select extremely pure subsamples

We are able to assign probabilities to photometric galaxies with IAB < 22.5 (or IAB < 24) to be members of spectroscopic groups Complete membership for IAB < 22.5

We are able to find for each group the most massive member at high confidence We can investigate the central-satellite issues

Combining spec and phot components yields improved group properties such as group centers

Summary

Appendix

≥ probability

≥ probability

≥ probability

com

plet

enes

s

com

plet

enes

s

com

plet

enes

s

Completeness

2 ≤ N ≤ 4 5 ≤ N ≤ 9

N ≥ 10

Completeness for phot

Group robustness

One-to-one correspondence

„too big“ (over-merged)

No association

„too small“ (fragmented)

Method to find robust groups:

increase or decrease linking length by 20%

Consider the increase or decline of the richness N

Group robustness

Group robustness

1-1 correspondence

„too big“ (over-merged)

No association

„too small“ (fragmented)Subsample of groups…exhibiting less than 40% change in N by the 20% change of the linking lenght … GRP ≥ 0.8

Group robustness

g2

completeness purity

Interloper fraction10k20k

20k spectroscopic catalog

Mass completeness 20k