Finding structures in photometric redshift surveys [Elektronische Ressource] / submitted by Christine S. Botzler

Botzler, Christine S.

Sujets

• Astronomie

Informations

• Publié par: ludwig-maximilians-universitat_munchen
• Publié le: 01 janvier 2004
• Nombre de lectures: 5
• Langue: English
• Poids de l'ouvrage: 8 Mo

Extrait

Finding Structures in Photometric
Redshift Surveys
Christine S. BotzlerFINDING STRUCTURES IN
PHOTOMETRIC REDSHIFT
SURVEYS
Ph.D. Thesis
at the Faculty of Physics & Astronomy
of the
Ludwig Maximilian University Munich
submitted by
Christine S. Botzler
from Munich
Munich, February 20041. Evaluator: Prof. Dr. Ralf Bender
2. Ev Dr. Hans Bohringer¨
Date of the oral exam: 28.05.2004We shall not cease from exploration, and the end of all our exploring will be to arrive
where we started and know the place for the first time.
– T. S. Eliot
to my parents and grandmothers –
for their endless supportContents
1 Introduction 1
1.1 Galaxy groups and clusters . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Photometric redshifts . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 The Munich Near Infrared Cluster Survey (MUNICS) . . . . . . . . . 8
1.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Structure Finding – An Overview 13
2.1 Complications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Choice of the right technique . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Differentiating the techniques . . . . . . . . . . . . . . . . . . . . . . 14
2.4 General conspectus . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3 Friends of Friends Algorithms 19
3.1 The Huchra & Geller friends of friends (FOF) algorithm . . . . . . . 19
3.2 The extended (EXT FOF) . . . . . . . . 21
3.3 The linking parameters . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3.1 Spectroscopic surveys . . . . . . . . . . . . . . . . . . . . . 26
3.3.2 Simulated datasets . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.3 Photometric surveys . . . . . . . . . . . . . . . . . . . . . . 27
4 Testing the Extended Friends of Friends 29
4.1 Application to the CFA1 redshift survey . . . . . . . . . . . . . . . . 29
4.1.1 The galaxy catalog . . . . . . . . . . . . . . . . . . . . . . . 29
4.1.2 Creation of the group catalogs . . . . . . . . . . . . . . . . . 30
4.1.3 Comparison of the group catalogs . . . . . . . . . . . . . . . 32
4.1.4 Analysis of the discrepancies . . . . . . . . . . . . . . . . . . 34
4.2 Application to the LCRS redshift survey . . . . . . . . . . . . . . . . 40
4.2.1 The galaxy catalog . . . . . . . . . . . . . . . . . . . . . . . 40
4.2.2 Creation of the group catalogs . . . . . . . . . . . . . . . . . 41
4.2.3 Comparison of the group catalogs . . . . . . . . . . . . . . . 44
4.2.4 Analysis of the discrepancies . . . . . . . . . . . . . . . . . . 45
4.3 LCRS with simulated photometric redshifts . . . . . . . . . . . . . . 47
4.3.1 Creation of the galaxy dataset with simulated photometric red
shifts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.3.2 Creation of the group catalog . . . . . . . . . . . . . . . . . . 47x CONTENTS
4.3.3 Comparison of the group catalogs . . . . . . . . . . . . . . . 48
4.3.4 Analysis of the discrepancies . . . . . . . . . . . . . . . . . . 49
5 Authenticity of Structures 51
5.1 Spectroscopic redshifts . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.2 Voronoi tessellation . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2.1 The definition of Voronoi tessellation . . . . . . . . . . . . . 54
5.2.2 Application to structure finding . . . . . . . . . . . . . . . . 56
5.3 Likelihood approach . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.3.1 Bayesian statistics and the likelihood . . . . . . . . . . . . . 60
5.3.2 Application to structure finding . . . . . . . . . . . . . . . . 61
5.4 Color magnitude diagrams . . . . . . . . . . . . . . . . . . . . . . . 66
5.4.1 The cluster red sequence . . . . . . . . . . . . . . . . . . . . 67
5.4.2 Application to structure finding . . . . . . . . . . . . . . . . 68
6 Structures in the MUNICS Survey 71
6.1 The galaxy catalog . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.1.1 The field geometry . . . . . . . . . . . . . . . . . . . . . . . 72
6.1.2 The spectroscopic sample . . . . . . . . . . . . . . . . . . . 72
6.1.3 Classification of all galaxies . . . . . . . . . . . . . . . . . . 76
6.2 Finding the structures . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.2.1 The algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 78
6.2.2 Optimizing the linking parameters . . . . . . . . . . . . . . . 79
6.3 The structure catalog . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.3.1 The field S6F5 . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.3.2 Examples of clusters in S6F5 . . . . . . . . . . . . . . . . . . 87
6.4 Spectroscopic verification . . . . . . . . . . . . . . . . . . . . . . . . 96
7 Summary 101
A Maximizing the Likelihood 115
B MUNICS Structures 119
B.1 The field S2F1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
B.2 The field S2F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
B.3 The field S3F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
B.4 The field S5F1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
B.5 The field S5F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
B.6 The field S6F1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
B.7 The field S7F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137