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Simon Lilly (ETH Zurich), Angela Iovino, Valentina Presotto (INAF Brera)+ zCOSMOS Team
COSMOS Meeting, Honolulu10.06.2010
Christian Knobel (ETH Zurich)
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:
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
≥ 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