Bayesian inference for compact binary sources of gravitational waves
Description
The first detection of gravitational waves in 2015 has opened a new window for the study of the astrophysics of compact binaries. Thanks to the data taken by the ground-based detectors advanced LIGO and advanced Virgo, it is now possible to constrain the physical parameters of compact binaries using a full Bayesian analysis in order to increase our physical knowledge on compact binaries. However, in order to be able to perform such analysis, it is essential to have efficient algorithms both to search for the signals and for parameter estimation. The main part of this thesis has been dedicated to the implementation of a Hamiltonian Monte Carlo algorithm suited for the parameter estimation of gravitational waves emitted by compact binaries composed of neutron stars. The algorithm has been tested on a selection of sources and has been able to produce better performances than other types of MCMC methods such as Metropolis-Hastings and Differential Evolution Monte Carlo. The implementation of the HMC algorithm in the data analysis pipelines of the Ligo/Virgo collaboration could greatly increase the efficiency of parameter estimation. In addition, it could also drastically reduce the computation time associated to the parameter estimation of such sources of gravitational waves, which will be of particular interest in the near future when there will many detections by the ground-based network of gravitational wave detectors. Another aspect of this work was dedicated to the implementation of a search algorithm for gravitational wave signals emitted by monochromatic compact binaries as observed by the space-based detector LISA. The developed algorithm is a mixture of several evolutionary algorithms, including Particle Swarm Optimisation. This algorithm has been tested on several test cases and has been able to nd all the sources buried in a signal. Furthermore, the algorithm has been able to nd the sources on a band of frequency as large as 1 mHz which was not done at the time of this thesis study. (author)
Abstract (French)
La premiere detection des ondes gravitationnelles en 2015 a ouvert un nouveau plan d'etude pour l'astrophysique des etoiles binaires compactes. En utilisant les donnees des detections faites par les detecteurs terrestres advanced LIGO et advanced Virgo, il est possible de contraindre les parametres physiques de ces systemes avec une analyse Bayesienne et ainsi approfondir notre connaissance physique des etoiles binaires compactes. Cependant, pour pouvoir etre en mesure d'obtenir de tels resultats, il est essentiel d'avoir des algorithmes performants a la fois pour trouver les signaux de ces ondes gravitationnelles et pour l'estimation de parametres. Le travail de cette these a ainsi ete centre autour du developpement d'algorithmes performants et adaptees au probleme physique a la fois de la detection et de l'estimation des parametres pour les ondes gravitationnelles. La plus grande partie de ce travail de these a ainsi ete dediee a l'implementation d'un algorithme de type Hamiltonien Monte Carlo adapte a l'estimation de parametres pour les signaux d'ondes gravitationnelles emises par des binaires compactes formees de deux etoiles a neutrons. L'algorithme developpe a ete teste sur une selection de sources et a ete capable de fournir de meilleures performances que d'autres algorithmes de type MCMC comme l'algorithme de Metropolis-Hasting et l'algorithme a evolution differentielle. L'implementation d'un tel algorithme dans les pipelines d'analyse de donnees de la collaboration pourrait augmenter grandement l'efficacite de l'estimation de parametres. De plus, il permettrait egalement de reduire drastiquement le temps de calcul necessaire, ce qui est un facteur essentiel pour le futur ou de nombreuses detections sont attendues. Un autre aspect de ce travail de these a ete dedie a l'implementation d'un algorithme de recherche de signaux d'ondes gravitationnelles pour les binaires compactes monochromatiques qui seront observees par la future mission spatiale LISA. L'algorithme est une mixture de plusieurs algorithmes evolutionnistes, avec notamment l'inclusion d'un algorithme de Particle Swarm Optimisation. Cet algorithme a ete teste dans plusieurs cas tests et a ete capable de trouver toutes les sources gravitationnelles comprises dans un signal donne. De plus, l'algorithme a egalement ete capable d'identier des sources sur une bande de frequence aussi grande que 1 mHz, ce qui n'avait pas ete realise au moment de cette etude de these. (auteur)
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Additional details
Additional titles
- Original title (English)
- Inference Bayesienne pour les sources compactes binaires d'ondes gravitationnelles
Publishing Information
- Imprint Pagination
- 195 p.
- Report number
- FRNC-TH--13196
INIS
- Country of Publication
- France
- Country of Input or Organization
- France
- INIS RN
- 53086854
- Subject category
- S79: ASTROPHYSICS, COSMOLOGY AND ASTRONOMY;
- Resource subtype / Literary indicator
- Thesis
- Descriptors DEI
- ALGORITHMS; BINARY STARS; CONVERGENCE; CORRELATIONS; FOURIER ANALYSIS; GRAVITATIONAL WAVE DETECTORS; GRAVITATIONAL WAVES; HAMILTONIANS; MARKOV PROCESS; MAXIMUM-LIKELIHOOD FIT; MONTE CARLO METHOD; NEUTRON STARS; WAVE FORMS
- Descriptors DEC
- CALCULATION METHODS; MATHEMATICAL LOGIC; MATHEMATICAL OPERATORS; MATHEMATICAL SOLUTIONS; MEASURING INSTRUMENTS; NUMERICAL SOLUTION; QUANTUM OPERATORS; RADIATION DETECTORS; STARS; STOCHASTIC PROCESSES
Optional Information
- Notes
- 157 refs.; Available from the INIS Liaison Officer for France, see the INIS website for current contact and E-mail addresses