Transcript Clusters at low redshift Michael Balogh University of Durham University of Durham University of Waterloo (Canada)

Clusters at low redshift
Michael Balogh
University of Durham
University of Durham
University
of Waterloo (Canada)
Collaborators
Richard Bower
Durham
Ivan Baldry &
Karl Glazebrook
Johns Hopkins
Bob Nichol,
Chris Miller
& Alex Gray
Carnegie Mellon
Vince Eke
Durham
GALFORM people: Baugh, Cole, Lacey, Frenk
Durham
Ian Lewis (Oxford)
and the 2dFGRS team
Outline
1. Background: Galaxy properties as a function of
environment
2. Galaxy colour distributions
3. Galaxy SFR distributions
4. Interpretation
5. Large-scale structure dependence
6. Conclusions
Morphology-Density Relation
The
“Outskirts”
of clusters
Clusters
Field
Where does the
transition begin,
and what causes
it?
E
S0
Spirals
Dressler 1980
High concentration clusters
Low concentration (non-relaxed)
Dressler 1980
Groups
Postman & Geller 1984
• Morphology-density relation holds for
irregular clusters, centrally-concentrated
clusters, and groups
• Therefore it is local galaxy density that is
of most interest, not global cluster
properties
• Possibly additional effects in innermost
regions (Whitmore et al., Dominguez et al.)
SFR-Density relation
R>2R200
Clusters
Field
Field
critical
density?
2dFGRS: Lewis et al. 2003
SDSS: Gomez et al. 2004
Empirical questions
1.
How best to characterise galaxy population?
•
•
2.
morphology, colour, SFR, or luminosity?
how to quantify distribution (mean/median etc.)
How to define environment observationally?
•
•
•
•
clustercentric distance?
projected galaxy density?
3-dimensional density? dark matter density (Gray et
al.)?
cluster type/mass?
Outline
1. Background: Galaxy properties as a function of
environment
2. Galaxy colour distributions
3. Galaxy SFR distributions
4. Interpretation
5. Large-scale structure dependence
6. Conclusions
Colours
• morphology is difficult to quantify
– Especially to distinguish E from S0
• colours simple and direct tracer of SF (also
metallicity, dust)
• Sloan Digital Sky Survey
– digital ugriz photometry and redshifts for nearby galaxies
– use “model magnitudes” which give high S/N, centrallyconcentrated colours
• density:
– projected distance to 5th nearest neighbour
– 3D density based on convolution with Gaussian kernel
– cluster velocity dispersion
Colour-magnitude relation
Sloan DSS
data
Baldry et al. 2003
(see also Hogg et al. 2003)
Blue Fraction
Margoniner et al. 2000
De Propris et al. 2004 (2dFGRS)
Analysis of colours in
SDSS data:
• Colour distribution in 0.5
mag bins can be fit with
two Gaussians
• Mean and dispersion of
each distribution depends
strongly on luminosity
• Dispersion includes
variation in dust, metallicity,
SF history, and
photometric errors
(u-r)
Baldry et al. 2004
Density Dependence
Bright
Lowest Densities
• 23520 galaxies from SDSS
DR1. magnitude limited with
z<0.08
• density estimates
Mr<-20
based
on
• keep mean and dispersion fixed
at Baldry et al. (2004) values
Faint
• Fit height of two distributions
to different density bins
Balogh, Baldry, Nichol, Miller,
Bower & Glazebrook,
submitted to ApJ Letters
Density Dependence
Faint
Bright
3X denser
• 2 Gaussian model still a good fit
• mean/dispersion
of
each
population shows no strong
dependence on density
Balogh, Baldry, Nichol, Miller,
Bower & Glazebrook,
submitted to ApJ Letters
Density Dependence
Faint
Bright
3X denser
• 2 Gaussian model still a good fit
• mean/dispersion
of
each
population shows no strong
dependence on density
Balogh, Baldry, Nichol, Miller,
Bower & Glazebrook,
submitted to ApJ Letters
Density Dependence
Bright
3X denser
“Infall regions”
• mean/dispersion of each
population shows no strong
dependence on density
Faint
• Some evidence for a
departure from the 2Gaussian model
Balogh, Baldry, Nichol, Miller,
Bower & Glazebrook,
submitted to ApJ Letters
Density Dependence
Bright
Highest density
• mean/dispersion
of
each
population shows no strong
dependence on density
Faint
• Some evidence for a departure
from the 2-Gaussian model
Balogh, Baldry, Nichol, Miller,
Bower & Glazebrook,
submitted to ApJ Letters
• Red sequence independence on environment has
been known for a long time (e.g. Sandage &
Visvanathan 1978)
• But the insensitivity of blue mean and dispersion to
environment is surprising:
 Properties of star-forming galaxies depend only
on internal structure of galaxy
 Clusters do not inhibit SF in all blue galaxies
• Fraction of red galaxies
depends strongly on density.
This is the primary influence of
environment on the colour
distribution.
• Use cluster catalogue of Miller,
Nichol et al. (C4 algorithm)
• No dependence on cluster
velocity dispersion observed.
Local density is the main driver
Outline
1. Background: Galaxy properties as a function of
environment
2. Galaxy colour distributions
3. Galaxy SFR distributions
4. Interpretation
5. Large-scale structure dependence
6. Conclusions
Ha distribution
• Use Ha equivalent widths from
SDSS and 2dFGRS (volumelimited samples Mr<-20)
• Ha distribution also shows a
bimodality
• Star-forming
W(Ha)>4 Å
galaxies
Balogh et al. 2004 (MNRAS 348, 1355)
with
The star-forming population
• Amongst the star-forming
population, there is no
trend in Ha distribution
with density
• Trends of mean or median
with density can be
misleading
• Hard to explain with
simple, slow-decay models
(e.g. Balogh et al. 2000)
Correlation with density
2dFGRS
• The fraction of starforming galaxies
varies strongly with
density
• Correlation at all
densities; still a
flattening near the
critical value
• Fraction never
reaches 100%, even
at lowest densities
Isolated Galaxies
All galaxies
Bright galaxies
• Selection of isolated
galaxies:
– non-group members,
with low densities on
1 and 5.5 Mpc scales
• ~30% of isolated
galaxies show
negligible SF
– environment must not
be only driver of
evolution.
Outline
1. Background: Galaxy properties as a function of
environment
2. Galaxy colour distributions
3. Galaxy SFR distributions
4. Interpretation
5. Large-scale structure dependence
6. Conclusions
• Departures from 2-Gaussian
model in dense regions might
indicate a transforming
population
• Start with colour distribution in
the lowest density regions
• Transform galaxies from blue to
red at uniform rate over a
Hubble time
Instantaneous truncation
•
If SFR is truncated instantly,
result is similar to 2-Gaussian
model
•
This is because:
•
1.
Colour evolution is
rapid after truncation
2.
Number of galaxies
caught in transition at
present day is small
Short-timescale truncation
could be important at all
luminosities and densities
Strangulation models
• Slower SFR decay begins to
populate intermediate colour
regime
Strangulation models
• Slower SFR decay begins to
populate intermediate colour
regime
Strangulation models
• Slower SFR decay begins to
populate intermediate colour
regime
• 2 Gyr timescale approximately
what is expected if hot gas is
stripped and galaxy allowed to
consume cold gas supply at
normal rate (Larson, Tinsley &
Caldwell 1980; Balogh, Navarro
& Morris 2000)
• Not the only interpretation, but
a successful model nonetheless
GALFORM model
• GALFORM is Durham model of galaxy formation (Cole et
al. 2000)
– parameters fixed to reproduce global properties of galaxies at z=0
(e.g. luminosity function) and abundance of SCUBA galaxies at high
redshift
• Use mock catalogues of 2dFGRS which include all selection
biasses
• Predict Ha from Lyman continuum photons, choose dust
model to match observed Ha distribution
• Assume hot gas is stripped from galaxies when they merge
with larger halo (i.e. groups and clusters) which leads to
strangulation of SFR (gradual decline)
GALFORM predictions
1.
Fraction of SF galaxies declines with increasing density as in data
GALFORM predictions
• Over most of the density range,
correlation between stellar mass
and SFR fraction is invariant
 Therefore
SFR-density
correlation is due to massdensity correlation
• At highest densities, models
predict fewer SF galaxies at
fixed mass due to strangulation
GALFORM predictions
S5<0.2 Mpc-2
S5<0.2 Mpc-2
Observed Ha distribution
independent of environment at all
densities
GALFORM predictions
1.
2.
Fraction of SF galaxies declines with increasing density as in data
At low densities, Ha distribution independent of environment
GALFORM predictions
1.
2.
Fraction of SF galaxies declines with increasing density as in data
At low densities, Ha distribution independent of environment
GALFORM predictions
1.
2.
3.
Fraction of SF galaxies declines with increasing density as in data
At low densities, Ha distribution independent of environment
In densest environments, Ha distribution skewed toward low values
GALFORM predictions
Kauffmann et al. (2004) work with SDSS suggests correlation between SFR
and stellar mass depends on environment. However this is not directly
comparable in this form.
Outline
1. Background: Galaxy properties as a function of
environment
2. Galaxy colour distributions
3. Galaxy SFR distributions
4. Interpretation
5. Large-scale structure dependence
6. Conclusions
Large scale structure
r5.5 (Mpc-3)
0.050
0.010
0.005
Contours are lines of constant
emission-line fraction
• Emission-line fraction appears to
depend on 1 Mpc scales and on 5.5
Mpc scales.
Increasing fraction of Ha emitters
2dFGRS data.
Similar results for SDSS
GALFORM predictions: LSS
Data
r5.5 (Mpc-3)
r5.5 (Mpc-3)
Model
r1.1 (Mpc-3)
GALFORM predictions: LSS
• Fraction of star-forming galaxies depends
primarily on local density, but there is a
further weak correlation with large scales
• Not expected in CDM models because
halo merger history depends only on local
environment (Kauffmann et al. 1994)
• Should be independently confirmed but
suggests an important element missing
from these models
Conclusions
• SFR/colour distribution among active population is
independent of environment
• Fraction of SF/blue galaxies decreases with
increasing density
• At low densities this trend may be due to change in
mass function with environment
• At high densities (~infall regions of clusters) there
is evidence for a slowly transforming population.
Details differ from GALFORM models
• Evidence for dependence on large-scale densities
that is not anticipated by models