The optimal detection statistic for precessing black-hole binaries in gravitational wave data

QUEST Center event
No
Speaker
Jonathan Mushkin, Weizmann Institute of Science
Date
12/01/2023 - 15:30 - 14:00Add to Calendar 2023-01-12 14:00:00 2023-01-12 15:30:00 The optimal detection statistic for precessing black-hole binaries in gravitational wave data As a part of the ongoing search for inspiraling black hole binaries in gravitational-waves data, we derive a scheme to obtain the optimal detection test-statistic (the evidence ratio) in a way that is efficient to compute both on the target signal and compute its exact properties using time-slides (time-shifting the detectors with respect to each other). This detection statistic, for the first time, includes the effects of both precession and high emission modes. Due to computational limitations, we employ the test for the 10^4 most promising candidates from the regular (fast) match-filtering pipeline. This is an improvement in both statistical significance and run-time compared to a previous scheme by the GW@IAS collaboration (Zackay et al. 2019). A key component of the new scheme is posterior probability estimation, commonly performed today by sampling algorithms (e.g. MCMC, Nested Samplers). Time permitting, we'll discuss recent developments in this area. Physics Building (202) Seminar Room 303 Department of Physics physics.dept@mail.biu.ac.il Asia/Jerusalem public
Place
Physics Building (202) Seminar Room 303
Abstract

As a part of the ongoing search for inspiraling black hole binaries in gravitational-waves data, we derive a scheme to obtain the optimal detection test-statistic (the evidence ratio) in a way that is efficient to compute both on the target signal and compute its exact properties using time-slides (time-shifting the detectors with respect to each other). This detection statistic, for the first time, includes the effects of both precession and high emission modes. Due to computational limitations, we employ the test for the 10^4 most promising candidates from the regular (fast) match-filtering pipeline. This is an improvement in both statistical significance and run-time compared to a previous scheme by the GW@IAS collaboration (Zackay et al. 2019). A key component of the new scheme is posterior probability estimation, commonly performed today by sampling algorithms (e.g. MCMC, Nested Samplers). Time permitting, we'll discuss recent developments in this area.

Last Updated Date : 08/11/2022