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Studying neutron-star and black-hole binaries with gravitational-waves

Vinciguerra, Serena (2018)
Ph.D. thesis, University of Birmingham.

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Abstract

The revolutionary discoveries of the last few years have opened a new era of astronomy. With the detection of gravitational-waves, we now have the opportunity of investigating new phenomena, such as mergers of black-holes. Furthermore, multi-messenger observations now allow us to combine information from different channels, providing insight into the physics involved. With this rapid evolution and growth of the field, many challenges need to be faced.

In this thesis we propose three data analysis strategies to efficiently study the coalescences of compact binaries. First we propose an algorithm to reduce the computational cost of Bayesian inference on gravitational-wave signals. Second we prove that machine-learning signal classification could enhance the significance of gravitational-wave candidates in unmodelled searches for transients. Finally we develop a tool, saprEMo, to predict the number of electromagnetic events, which according to a specific emission model, should be present in a particular survey.

Type of Work:Ph.D. thesis.
Supervisor(s):Mandel, Ilya and Vecchio, Alberto
School/Faculty:Colleges (2008 onwards) > College of Engineering & Physical Sciences
Department:School of Physics and Astronomy
Additional Information:

Publications resulting from research:

Vinciguerra, S., Drago, M., Prodi, G. A., Klimenko, S., Lazzaro, C., Necula,
V., Salemi, F., Tiwari, V., Tringali, M. C., and Vedovato, G. (2017a). Enhancing the significance of gravitational wave bursts through signal classification. Classical and Quantum Gravity, 34(9):094003.
http://dx.doi.org/10.1088/1361-6382/aa6654

Vinciguerra, S., Veitch, J., and Mandel, I. (2017b). Accelerating gravitational wave parameter estimation with multi-band template interpolation. Classical and Quantum Gravity, 34(11):115006.
http://dx.doi.org/10.1088/1361-6382/aa6d44


Subjects:QB Astronomy
QC Physics
Institution:University of Birmingham
ID Code:8159
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
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