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Published May 15, 2021 | Published + Accepted Version
Journal Article Open

Adiabatic waveforms for extreme mass-ratio inspirals via multivoice decomposition in time and frequency

Abstract

We compute adiabatic waveforms for extreme mass-ratio inspirals (EMRIs) by "stitching" together a long inspiral waveform from a sequence of waveform snapshots, each of which corresponds to a particular geodesic orbit. We show that the complicated total waveform can be regarded as a sum of "voices." Each voice evolves in a simple way on long timescales, a property which can be exploited to efficiently produce waveform models that faithfully encode the properties of EMRI systems. We look at examples for a range of different orbital geometries: spherical orbits, equatorial eccentric orbits, and one example of generic (inclined and eccentric) orbits. To our knowledge, this is the first calculation of a generic EMRI waveform that uses strong-field radiation reaction. We examine waveforms in both the time and frequency domains. Although EMRIs evolve slowly enough that the stationary phase approximation (SPA) to the Fourier transform is valid, the SPA calculation must be done to higher order for some voices, since their instantaneous frequency can change from chirping forward (ḟ > 0) to chirping backward (ḟ < 0). The approach we develop can eventually be extended to more complete EMRI waveform models—for example, to include effects neglected by the adiabatic approximation, such as the conservative self-force and spin-curvature coupling.

Additional Information

© 2021 American Physical Society. (Received 4 February 2021; accepted 7 April 2021; published 11 May 2021) An early presentation of some of the results and methods described here was given at the 22nd Capra Meeting on Radiation Reaction in General Relativity, hosted by the Centro Brasiliero de Pequisas Físicas in Rio de Janeiro, Brazil in June 2019; we thank the participants of that meeting for useful discussions, and particularly Marc Casals for organizing the meeting. The time-domain waveform computations were performed on the MIT/IBM Satori GPU supercomputer supported by the Massachusetts Green High Performance Computing Center (MGHPCC) and the CARNiE cluster at UMass Dartmouth. S. A. H. thanks the MIT Kavli Institute for providing computing resources and support, L. S. Finn for (long ago) suggesting the multivoice framework for examining EMRI waveforms, Stanislav Babak for emphasizing the importance of understanding EMRI waveform structure in the frequency domain, Steve Drasco and William T. Throwe for past contributions to GREMLIN development, Gustavo Velez for insight into the Jacobian between (dE/dt,dL_z/dt,dQ/dt) and (dp/dt,de/dt,dx_I/dt), Talya Klinger for work porting GREMLIN to the XSEDE computing environment, and both Talya Klinger and Sayak Datta for discussions of accuracy and systematic error in EMRI datasets. We also thank Leo Stein for raising the issue of the pathology discussed in Ref. [48], which was important to consider as a contrast to a similar issue we examine. S. A. H.'s work on this problem was supported by NASA ATP Grant No. 80NSSC18K1091 and NSF Grant No. PHY-1707549. N. W. acknowledges support from a Royal Society–Science Foundation Ireland Research Fellowship. This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant No. 16/RS-URF/3428. G. K. acknowledges support from NSF Grants No. PHY-2106755 and No. DMS-1912716. A. J. K. C. acknowledges support from NASA Grant No. 18-LPS18-0027. M. L. K. acknowledges support from NSF Grant No. DGE-0948017.

Attached Files

Published - PhysRevD.103.104014.pdf

Accepted Version - 2102.02713.pdf

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Additional details

Created:
August 20, 2023
Modified:
October 23, 2023