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Published January 15, 2021 | Submitted + Supplemental Material + Published
Journal Article Open

Adding gravitational memory to waveform catalogs using BMS balance laws

Abstract

Accurate models of gravitational waves from merging binary black holes are crucial for detectors to measure events and extract new science. One important feature that is currently missing from the Simulating eXtreme Spacetimes (SXS) Collaboration's catalog of waveforms for merging black holes, and other waveform catalogs, is the gravitational memory effect: a persistent, physical change to spacetime that is induced by the passage of transient radiation. We find, however, that by exploiting the Bondi-van der Burg-Metzner-Sachs (BMS) balance laws, which come from the extended BMS transformations, we can correct the strain waveforms in the SXS catalog to include the missing displacement memory. Our results show that these corrected waveforms satisfy the BMS balance laws to a much higher degree of accuracy. Furthermore, we find that these corrected strain waveforms coincide especially well with the waveforms obtained from Cauchy-characteristic extraction (CCE) that already exhibit memory effects. These corrected strain waveforms also evade the transient junk effects that are currently present in CCE waveforms. Last, we make our code for computing these contributions to the BMS balance laws and memory publicly available as a part of the python package sxs, thus enabling anyone to evaluate the expected memory effects and violation of the BMS balance laws.

Additional Information

© 2021 American Physical Society. Received 4 November 2020; accepted 21 December 2020; published 15 January 2021. We would like to thank Dennis Pollney for sharing data that was used in the early stages of this project. Computations were performed with the High Performance Computing Center and on the Wheeler cluster at Caltech, which is supported by the Sherman Fairchild Foundation and by Caltech. This work was supported in part by the Sherman Fairchild Foundation and by NSF Grants No. PHY-2011961, No. PHY-2011968, and No. OAC-1931266 at Caltech, NSF Grants No. PHY-1912081 and No. OAC-1931280 at Cornell, and NSF Grant No. PHY-1806356, Grant No. UN2017-92945 from the Urania Stott Fund of the Pittsburgh Foundation, and the Eberly research funds of Penn State at Penn State.

Attached Files

Published - PhysRevD.103.024031.pdf

Submitted - 2011.01309.pdf

Supplemental Material - Memory.ipynb

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Additional details

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