Abstract Details
| Name: ABHIRUP GHOSH Affiliation: INTERNATIONAL CENTRE FOR THEORETICAL SCIENCES, TIFR Conference ID: ASI2017_668 Title : Testing general relativity using gravitational wave signals from the inspiral, merger and ringdown of binary black holes Authors and Co-Authors : Archisman Ghosh, Nathan K. Johnson-McDaniel, Chandra Kant Mishra, Parameswaran Ajith, Walter Del Pozzo, Alex B. Nielsen, Christopher P. L. Berry, Lionel London Abstract Type : Oral Abstract Category : General Relativity and Cosmology Abstract : We have developed a method of testing General Relativity (GR) using gravitational waves (GWs) from the coalescence of a binary black hole (BBH) system by inferring the mass and spin of the remnant black hole, using two different parts of the observed signal, the initial inspiral and the final merger-ringdown phases, and then comparing these independent estimates. The initial masses and spin obtained from the inspiral part of the GW signal, are used to obtain the final mass and spin using appropriate fitting formulae. If the two independent estimates are consistent with each other, it would mean the signal observed is consistent with the predictions of BBH coalescences in GR. This was one of the tests used to establish that GW150914 was consistent with a BBH merger in GR. We tested the robustness of the method against signals with energy and angular momentum evolution differing from that predicted by GR, and showed that the inspiral-merger-ringdown consistency test can can exclude GR with high confidence for sufficiently large departures from the theory. We now demonstrate the robustness of the test using an astrophysically motivated population of binaries as well as the sensitivity of the test to various ingredients or choices used in its construction, e.g., the GR waveform models used and the frequency used to separate inspiral from merger-ringdown. We also apply the test to a population of signals differing from GR's predictions to check the test's ability to discern smaller deviations from GR by combining together a large number of observations. |