PIRSA:20010087

Accurately modelling extreme-mass-ratio inspirals: beyond the geodesic approximation

Pound, A. (2020). Accurately modelling extreme-mass-ratio inspirals: beyond the geodesic approximation. Perimeter Institute. https://pirsa.org/20010087

Pound, Adam. Accurately modelling extreme-mass-ratio inspirals: beyond the geodesic approximation. Perimeter Institute, Jan. 16, 2020, https://pirsa.org/20010087 

          @misc{ pirsa_PIRSA:20010087,
            doi = {10.48660/20010087},
            url = {https://pirsa.org/20010087},
            author = {Pound, Adam},
            keywords = {Strong Gravity},
            language = {en},
            title = {Accurately modelling extreme-mass-ratio inspirals: beyond the geodesic approximation},
            publisher = {Perimeter Institute},
            year = {2020},
            month = {jan},
            note = {PIRSA:20010087 see, \url{https://pirsa.org}}
          }

Adam Pound

University of Southampton

Talk number
PIRSA:20010087
Collection
Talk Type
Subject
Abstract

Recent observations of gravitational waves represent a remarkable success of our theoretical models of relativistic binaries. However, accurate models are largely restricted to binaries in which the two members have roughly equal masses; for binaries with more disparate masses, modelling is less mature. This is especially relevant for extreme-mass-ratio inspirals (EMRIs), in which a stellar-mass object orbits a supermassive black hole in a galactic core. EMRIs are uniquely precise probes of black hole spacetimes, and they will be key targets for the space-based detector LISA. They are best modelled by gravitational self-force theory, in which the smaller object generates a small gravitational perturbation that reacts back on it to exert a "self-force", accelerating the object away from geodesic motion. For LISA science, we must work to second order in this perturbative treatment. In this talk, I discuss the foundations of self-force theory, its application to EMRIs, and the current status of first- and second-order models.