Tesi etd-11212020-194853 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
NICOLINI, GIORGIO
URN
etd-11212020-194853
Titolo
"TIMeR": a time-domain waveform model for testing General Relativity with gravitational waves
Dipartimento
FISICA
Corso di studi
FISICA
Relatori
relatore Dott. Ghosh, Abhirup
relatore Prof. Del Pozzo, Walter
relatore Prof. Del Pozzo, Walter
Parole chiave
- black holes
- General Relativity
- gravitational waves
- modified gravity
- test
Data inizio appello
07/12/2020
Consultabilità
Non consultabile
Data di rilascio
07/12/2060
Riassunto
Since the first detection of gravitational waves General Relativity can be tested in its highly-dynamical and non-linear regime via several methods which are sensitive to specific kinds of deviations. For instance, the IMR consistency test probes the energy and angular-momentum loss equations by analysing the intrinsic parameters underlying individually the first and second part of gravitational-wave events produced by the inspiral, merger and ringdown of coalescing compact binaries and detected by the LIGO/Virgo interferometers. The current thesis project devises a new version of such a test in order to solve some of its open issues. In particular, it provides modified time-domain waveform models which are composed of pieces of General-Relativity waveforms which are smoothly connected to each other in such a way as to minimise spectral leakage effects and uses such models as templates to catch possible departures from General Relativity in gravitational-wave events. In this new formulation, the method also ensures a greater compactness since it uses one single global waveform model instead of two for testing deviations in the intrinsic parameters of the source binary, i.e. its component masses and spins, and lends itself to more general scenarios where the transition time separating the two parts of the waveform is parameter-dependent or even a free parameter of the model. The method has been validated for simulations of non-spinning equal-mass General-Relativity waveforms by estimating its intrinsic parameters via Bayesian-inference techniques in the zero-noise realisation.
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