Galaxy formation and evolution [Elektronische Ressource] : the local galaxy population as a cosmological probe / vorgelegt von Darren J. Croton

Darren Croton

Sujets

• Astronomie

Informations

• Publié par: ludwig-maximilians-universitat_munchen
• Publié le: 01 janvier 2005
• Nombre de lectures: 3
• Langue: English
• Poids de l'ouvrage: 7 Mo

Extrait

Galaxy Formation and Evolution:
the Local Galaxy Population as a
Cosmological Probe
Dissertation der Fakultät für Physik
der
Ludwig-Maximilians-Universität München
vorgelegt von Darren J. Croton
aus Melbourne, Australia
Datum der mündlichen Prüfung: 3. November 2005
1. Gutachter: Prof. Dr. Simon D. M. White
2. Prof. Dr. Andreas BurkertContents
Summary 1
1 Introduction 3
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Essential cosmology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 The “dark” universe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Large scale structure, galaxy clustering and bias . . . . . . . . . . . . . . 7
1.5 Observed and mock galaxy populations . . . . . . . . . . . . . . . . . . 8
1.6 Thesis overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Hierarchical galaxy clustering in the 2dFGRS 15
MNRAS, 351, 44, 2004
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 Data and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.1 Counts-in-cells . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3 Higher order clustering in the 2dFGRS 25
MNRAS, 352, 1232, 2004
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2 Counts-in-cells statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.1 Estimating the p-point volume averaged correlation functions . . 28
3.2.2 Scaling of the higher order moments . . . . . . . . . . . . . . . . 29
3.2.3 Systematic eects: biased estimators . . . . . . . . . . . . . . . . 29
3.2.4 Galaxy biasing . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3 Application to the 2dFGRS . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.3.1 Construction of volume limited catalogues . . . . . . . . . . . . 32
3.3.2 Correcting for incompleteness . . . . . . . . . . . . . . . . . . . 34
3.3.3 Error estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.4.1 Volume-averaged correlation functions . . . . . . . . . . . . . . 38
3.4.2 Hierarchical clustering . . . . . . . . . . . . . . . . . . . . . . . 39
3.4.3 Systematic eects: the influence of superclusters . . . . . . . . . 42
iContents
3.5 Interpretation and the implications for galaxy bias . . . . . . . . . . . . . 47
3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4 Voids and hierarchical scaling models 53
MNRAS, 352, 828, 2004
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2 Void statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2.1 The void probability function . . . . . . . . . . . . . . . . . . . 54
4.2.2 Hierarchical scaling . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2.3 Phenomenological models . . . . . . . . . . . . . . . . . . . . . 56
4.3 The data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3.1 The 2dFGRS data set . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3.2 Volume limited catalogues . . . . . . . . . . . . . . . . . . . . . 60
4.4 Measuring the galaxy distribution . . . . . . . . . . . . . . . . . . . . . 62
4.4.1 Error estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5 Luminosity functions by density environment and galaxy type 71
MNRAS, 356, 1155, 2005
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.2.1 The 2dFGRS survey . . . . . . . . . . . . . . . . . . . . . . . . 72
5.2.2 Local density measurement . . . . . . . . . . . . . . . . . . . . . 73
5.2.3 Measuring the luminosity function . . . . . . . . . . . . . . . . . 74
5.2.4 Comparison to previous 2dFGRS results . . . . . . . . . . . . . . 76
5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.3.1 Luminosity functions . . . . . . . . . . . . . . . . . . . . . . . . 77
5.3.2 Evolution with environment . . . . . . . . . . . . . . . . . . . . 80
5.4 Comparison to previous work . . . . . . . . . . . . . . . . . . . . . . . . 85
5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
6 Cooling flows, black holes and the luminosities and colours of galaxies 97
MNRAS, submitted, 2005
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.2 The dark matter skeleton: the Millennium Run . . . . . . . . . . . . . . . 100
6.2.1 Simulation characteristics . . . . . . . . . . . . . . . . . . . . . 100
6.2.2 Haloes, substructure, and merger tree construction . . . . . . . . 101
6.3 Building galaxies: the semi-analytic model . . . . . . . . . . . . . . . . 104
6.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
6.3.2 Gas infall and cooling . . . . . . . . . . . . . . . . . . . . . . . 105
6.3.3 Reionization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
iiContents
6.3.4 Black hole growth, AGN outflows, and cooling suppression . . . 109
6.3.5 Star formation . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
6.3.6 Supernova feedback . . . . . . . . . . . . . . . . . . . . . . . . 114
6.3.7 Galaxy morphology, merging and starbursts . . . . . . . . . . . . 117
6.3.8 Spectroscopic evolution and dust . . . . . . . . . . . . . . . . . . 119
6.3.9 Metal enrichment . . . . . . . . . . . . . . . . . . . . . . . . . . 119
6.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
6.4.1 The suppression of cooling flows . . . . . . . . . . . . . . . . . 119
6.4.2 Galaxy properties with and without AGN heating . . . . . . . . . 121
6.5 Physical models of AGN feedback . . . . . . . . . . . . . . . . . . . . . 125
6.5.1 Cold cloud accretion . . . . . . . . . . . . . . . . . . . . . . . . 125
6.5.2 Bondi-Hoyle . . . . . . . . . . . . . . . . . . . . . . . 127
6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
7 Concluding remarks 133
7.1 Summary of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
7.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
Acknowledgements 139
Bibliography 141
iiiSummary
We take a multi-faceted approach to study galaxy populations in the local universe, us-
ing the completed Two Degree Field Galaxy Redshift Survey (2dFGRS), the “Millennium
Run”CDM N-body simulation, and a semi-analytic model of galaxy formation. Our in-
vestigation covers both small and large scale aspects of the galaxy distribution. This work
can be broken into three sections, outlined below.
Using the 2dFGRS we explore the higher-order clustering properties of local galaxies
to quantify both (i) the linear and non-linear bias of the distribution relative to the under-
lying matter field, and (ii) the nature of hierarchical scaling in the clustering moments of
the galaxy distribution. This last point is the expected signature of an initially Gaussian
distribution of matter density fluctuations that evolved under the action of gravitational
instability. We show in Chapters 2, 3, and 4 that the 2dFGRS higher-order clustering mo-
ments are indeed hierarchical, which we measure up to sixth order for galaxies brighter
than M 5 log h= 17 and which sample the survey volume out to z 0:3. ThebJ 10
moments are found to be well described by the negative binomial probability distribution
function, and we rule out, at high significance, other models of galaxy clustering, such as
the lognormal distribution. This result holds in redshift space on all scales where we obtain
1a good statistical signal, typically 0:5< R (h Mpc)< 30 (i.e. from strongly non-linear
to quasi-linear regimes). Interestingly, we find that the moments on larger scales can be
significantly altered by two massive superclusters present in the 2dFGRS. The skewness
of the galaxy distribution is found to have a weak dependence on galaxy luminosity. We
show that a simple linear biasing model provides an inadequate description of the higher
order results, suggesting that non-linear biasing is present in the clustering moments of the
2dFGRS.
The large-scale distribution of structure within the 2dFGRS allows us to study the proper-
ties of the galaxy population as a function of local environment. In Chapter 5 we measure
the luminosity function of early and late-types galaxies in survey regions ranging from
sparse voids to dense clusters to reveal the dominant population in each. Fitting each lu-
minosity function with a Schechter function allows us to quantify how the bright and faint
populations transform with changing density contrast. We find that (i) the population in
voids is dominated by late types, with a noticeable deficit of intermediate and bright galax-
ie