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Zawartość zarchiwizowana w dniu 2024-06-20

Construction of realistic initial data coalescing binary black holes in circular orbits


The beginning of the activities of ground-based interferometric gravitational observatories for detecting gravitational waves, urgently demands the existence of realistic and detailed theoretical predictions for the expected gravitational radiation.

This proves to be essential both for the analysis of the received signals (physical information plus noise) and for their interpretation. Black hole coalescence is the astrophysical source which stands as the most promising candidate for early detection. The detailed theoretical description of this phenomenon and its corresponding gravitational waves demand the use of numerical techniques.

The first step in such a numerical study is the determination of the so-called initial data. In the context of quasi-circular inspiraling binary black holes, the match of these numerical techniques with recently developed analytical methods can be crucial in order to identify the relevant initial data. This defines the fundamental objective of this project: constructing a set of astrophysically realistic initial data for highly detailed numerical evolutions.

In order to achieve this goal, the work is structured in two parts:
- setting of a well-posed system of elliptic partial differential equations which constrain the physical data; and
- numerically solving this system of equations by applying spectral methods in which the Meudon group represents a world-leading team.

The two parts will be carried out consecutively and will demand the use of very different strategies. For the first one, the employed methods will be basically analytical, while the second one will consist in the construction of a C++ code. The Meudon group represents a perfect environment to develop both stages.

The final outcome of the project will provide the necessary input for high quality predictions of gravitational waveforms, that could be performed by collaborating (European) teams.

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