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Fast extreme-mass-ratio-inspiral waveforms: New tools for millihertz gravitational-wave data analysis

Katz, Michael L. and Chua, Alvin J. K. and Speri, Lorenzo and Warburton, Niels and Hughes, Scott A. (2021) Fast extreme-mass-ratio-inspiral waveforms: New tools for millihertz gravitational-wave data analysis. Physical Review D, 104 (6). Art. No. 064047. ISSN 2470-0010. doi:10.1103/physrevd.104.064047.

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We present the fastemriwaveforms (FEW) package, a collection of tools to build and analyze extreme mass ratio inspiral (EMRI) waveforms. Here, we expand on [A. J. K. Chua et al., Phys. Rev. Lett. 126, 051102 (2021).] that introduced the first fast and accurate fully-relativistic EMRI waveform template model. We discuss the construction of the overall framework; constituent modules; and the general methods used to accelerate EMRI waveforms. Because the fully relativistic FEW model waveforms are for now limited to eccentric orbits in the Schwarzschild spacetime, we also introduce an improved augmented analytic kludge (AAK) model that describes generic Kerr inspirals. Both waveform models can be accelerated using graphics processing unit (GPU) hardware. With the GPU-accelerated waveforms in hand, a variety of studies are performed including an analysis of EMRI mode content, template mismatch, and fully Bayesian Markov Chain Monte Carlo-based EMRI parameter estimation. We find relativistic EMRI waveform templates can be generated with fewer harmonic modes (∼10–100) without biasing signal extraction. However, we show for the first time that extraction of a relativistic injection with semirelativistic amplitudes can lead to strong bias and anomalous structure in the posterior distribution for certain regions of parameter space.

Item Type:Article
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URLURL TypeDescription Paper
Katz, Michael L.0000-0002-7605-5767
Chua, Alvin J. K.0000-0001-5242-8269
Speri, Lorenzo0000-0002-5442-7267
Warburton, Niels0000-0003-0914-8645
Hughes, Scott A.0000-0001-6211-1388
Alternate Title:FastEMRIWaveforms: New tools for millihertz gravitational-wave data analysis
Additional Information:© 2021 The Author(s). Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Open access publication funded by the Max Planck Society. (Received 9 April 2021; accepted 9 August 2021; published 17 September 2021) M. L. K. thanks Jonathan Gair and Ollie Burke for helpful discussions and Stas Babak for providing scripts for the conventions diagrams. A. J. K. C. acknowledges support from the NASA Grant No. 18-LPS18-0027. 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. S. A. H.’s work on this problem was supported by NASA ATP Grant No. 80NSSC18K1091 and NSF Grant No. PHY-1707549. This research was supported in part through the computational resources and staff contributions provided for the Quest/Grail high performance computing facility at Northwestern University. This paper also employed use of scipy [88] and matplotlib [89]. This work makes use of the Black Hole Perturbation Toolkit [74].
Funding AgencyGrant Number
Science Foundation, Ireland16/RS-URF/3428
Max Planck SocietyUNSPECIFIED
Issue or Number:6
Record Number:CaltechAUTHORS:20210927-213255697
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111057
Deposited By: George Porter
Deposited On:27 Sep 2021 23:00
Last Modified:27 Sep 2021 23:00

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