clusters at low redshift university of durham university of waterloo (canada) university of durham...

Clusters at low redshiftClusters at low redshift

University of Durham University of Waterloo (Canada)

University of Durham

Michael Balogh

Bob Nichol, Chris Miller

& Alex GrayCarnegie

Mellon

CollaboratorsRichard BowerDurham Ivan Baldry &

Karl GlazebrookJohns Hopkins

Ian Lewis (Oxford)and the 2dFGRS

team

GALFORM people: Baugh, Cole, Lacey, Frenk Durham

Vince EkeDurham

Outline

1. Background: Galaxy properties as a function of environment

2. Galaxy colour distributions 3. Galaxy SFR distributions4. Interpretation5. Large-scale structure dependence6. Conclusions

E

Morphology-Density Relation

The “Outskirts” of clusters

Dressler 1980

Clusters

Fie

ld

Where does the transition begin, and what causes it?

S0Spirals

Postman & Geller 1984

Dressler 1980

• 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.)

High concentration clusters

Low concentration (non-relaxed)

Groups

SFR-Density relation

R>2R200

2dFGRS: Lewis et al. 2003

SDSS: Gomez et al. 2004

criticaldensity?

Field Field

Clusters

Empirical questions

1. How best to characterise galaxy population?

• morphology, colour, SFR, or luminosity?• how to quantify distribution (mean/median

etc.)

2. How to define environment observationally?

• clustercentric distance?• projected galaxy density?• 3-dimensional density? dark matter density

(Gray et al.)?• cluster type/mass?

Outline


2.Galaxy colour distributions 3. Galaxy SFR distributions4. Interpretation5. Large-scale structure dependence6. 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,

centrally-concentrated colours• density:

– projected distance to 5th nearest neighbour– 3D density based on convolution with Gaussian

kernel– cluster velocity dispersion

Colour-magnitude relation

Baldry et al. 2003(see also Hogg et al. 2003)

Sloan DSS data

Blue Fraction

Margoniner et al. 2000 De Propris et al. 2004 (2dFGRS)

Baldry et al. 2004(u-r)

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

Density Dependence• 23520 galaxies from

SDSS DR1. magnitude limited with z<0.08

• density estimates based on Mr<-20

• keep mean and dispersion fixed at Baldry et al. (2004) values

• Fit height of two distributions to different density bins

Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters

Lowest Densities

Bri

gh

tF

ain

t

Density Dependence3X denser • 2 Gaussian model still a

good fit

• mean/dispersion of each population shows no strong dependence on density


Bri

gh

tF

ain

t

Density Dependence3X denser

Bri

gh

tF

ain

t

“Infall regions”


• Some evidence for a departure from the 2-Gaussian model


Density DependenceHighest density


• Some evidence for a departure from the 2-Gaussian model


Bri

gh

tF

ain

t

• 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


2. Galaxy colour distributions 3.Galaxy SFR distributions4. Interpretation5. Large-scale structure dependence6. Conclusions

H distribution• Use H equivalent

widths from SDSS and 2dFGRS (volume-limited samples Mr<-20)

• H distribution also shows a bimodality

• Star-forming galaxies with W(H)>4 Å

Balogh et al. 2004 (MNRAS 348, 1355)

The star-forming population

• Amongst the star-forming population, there is no trend in H 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

• The fraction of star-forming galaxies varies strongly with density

• Correlation at all densities; still a flattening near the critical value

• Fraction never reaches 100%, even at lowest densities

2dFGRS

Isolated 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.

All galaxiesBright galaxies

Outline


2. Galaxy colour distributions 3. Galaxy SFR distributions4.Interpretation5. Large-scale structure dependence6. 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

• 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 H from Lyman continuum photons, choose dust model to match observed H 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 mass-density correlation

• At highest densities, models predict fewer SF galaxies at fixed mass due to strangulation

GALFORM predictions

Observed H distribution independent of environment at all densities

5<0.2 Mpc-2

5<0.2 Mpc-2

GALFORM predictions


2. At low densities, H distribution independent of environment

GALFORM predictions


2. At low densities, H distribution independent of environment

3. In densest environments, H 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


2. Galaxy colour distributions 3. Galaxy SFR distributions4. Interpretation5.Large-scale structure

dependence6. Conclusions

Large scale structure

Contours are lines of constant emission-line fraction

• Emission-line fraction appears to depend on 1 Mpc scales and on 5.5 Mpc scales.

5.5 (

Mp

c-3)

0.050

0.010

0.005 Increasing fraction of Hemitters

2dFGRS data. Similar results for

SDSS

GALFORM predictions: LSS

5.5 (

Mp

c-3)

5.5

(Mp

c-3)

1.1 (Mpc-3)

Model Data

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

clusters at low redshift university of durham university of waterloo (canada) university of durham...

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