Citation
Haasteren, R. van. (2011, October 11). Gravitational wave detection and data analysis for pulsar timing arrays. Retrieved from https://hdl.handle.net/1887/17917
Version: Corrected Publisher’s Version
License: Licence agreement concerning inclusion of doctoral thesis in the Institutional Repository of the University of Leiden
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Gravitational Wave detection
&
data analysis for Pulsar Timing Arrays
Rutger van Haasteren
Gravitational Wave detection
&
data analysis for Pulsar Timing Arrays
Proefschrift
ter verkrijging van
de graad van Doctor aan de Universiteit Leiden,
op gezag van de Rector Magnificus prof.mr. P.F. van der Heijden, volgens besluit van het College voor Promoties
te verdedigen op dinsdag 11 Oktober 2011 klokke 11:15 uur
door
Rutger van Haasteren
geboren te Den Haag in 1983
Prof. dr. M. Kramer (Max-Planck-Institut f¨ur Radioastronomie Bonn, University of Manchester) Prof. dr. L. S. Finn (Penn State University) Dr. B.W. Stappers (University of Manchester)
Contents
1 Introduction 1
1.1 General Relativity and GR tests . . . 2
1.2 Pulsars and pulsar timing . . . 7
1.3 Current GW experiments, and PTAs . . . 10
1.4 Bayesian PTA data analysis . . . 14
1.5 Thesis summary . . . 19
2 Bayesian data analysis of Pulsar Timing Arrays 23 2.1 Introduction . . . 24
2.2 The Theory of GW-generated timing residuals . . . 26
2.3 Bayesian approach . . . 29
2.4 Numerical integration techniques . . . 34
2.5 Tests and parameter studies . . . 38
2.6 Conclusion . . . 51
3 Gravitational-wave memory and Pulsar Timing Arrays 55 3.1 Introduction . . . 56
3.2 The signal . . . 56
3.3 Single-source detection by PTAs. . . 58
3.4 Detectability of memory jumps . . . 62
3.5 Tests using mock data . . . 65
3.6 Discussion . . . 74
4 Limiting the gravitational-wave background with EPTA data 77 4.1 Introduction . . . 78
4.2 EPTA data analysis . . . 79
4.3 EPTA observations . . . 82
4.4 Overview of data analysis methods . . . 83
4.5 Bayesian PTA data analysis . . . 87
4.6 Results . . . 93
4.7 Implications and outlook . . . 99
4.8 Conclusion and discussion . . . 103
5 Marginal likelihood calculation with MCMC methods 109 5.1 Introduction . . . 109
5.2 Bayesian inference . . . 111
5.3 Markov Chain Monte Carlo . . . 112
5.4 Comparison to other methods . . . 120
5.5 Applications and tests . . . 123 vii
viii
