Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published September 15, 2023 | Published
Journal Article Open

Accurate and efficient waveform model for precessing binary black holes

Abstract

We present IMRPhenomXODE, a new phenomenological frequency-domain waveform approximant for gravitational wave (GW) signals from precessing binary black holes (BBHs) with generic spin configurations. We build upon the success of IMRPhenomXPHM [G. Pratten et al., Phys. Rev. D 103, 104056 (2021)], which is one of the most widely adopted waveform approximants in GW data analyses that include spin precession, and introduce two additional significant improvements. First, we employ an efficient technique to numerically solve the (next-to)⁴-leading-order post-Newtonian precession equations, which allows us to accurately determine the evolution of the orientation of the orbital angular momentum L̂_N even in cases with complicated precession dynamics, such as transitional precession. Second, we recalibrate the phase of GW modes in the frame coprecessing with L̂_N against SEOBNRv4PHM [S. Ossokine et al., Phys. Rev. D 102, 044055 (2020)] to capture effects due to precession such as variations in the spin components aligned with L̂_N. By incorporating these new features, IMRPhenomXODE achieves matches with SEOBNRv4PHM that are better than 99% for most BBHs with mass ratios q ≥ 1/6 and with arbitrary spin configurations. In contrast, the mismatch between IMRPhenomXPHM and SEOBNRv4PHM often exceeds 10% for a BBH with q ≲ 1/2 and large in-plane or antialigned spin components. Our implementation is also computationally efficient, with waveform evaluation times that can even be shorter than those of IMRPhenomXPHM for BBH signals with long durations and hence high-frequency resolutions. The accuracy and efficiency of IMRPhenomXODE position it as a valuable tool for GW event searches, parameter estimation analyses, and the inference of underlying population properties.

Copyright and License

© 2023 American Physical Society.

Acknowledgement

We thank Maria Haney, Marta Colleoni, Katerina Chatziioannou, and other LVK colleagues for their useful comments during the preparation of this manuscript. H. Y.'s work at K. I. T. P. is supported by the National Science Foundation (No. NSF PHY-1748958) and by the Simons Foundation (No. 216179, LB). H. Y. is also supported by NSF Grant No. PHY-2308415. T. V. acknowledges support from NSF Grants No. PHY-2012086 and No. PHY-2309360, the Alfred P. Sloan Foundation through Grant No. FG-2023-20470, and the Hellman Family Faculty Fellowship. M. Z. is supported by NSF Grants No. 2209991 and No. NSF-BSF 2207583. Use was made of computational facilities purchased with funds from the National Science Foundation (CNS-1725797) and administered by the Center for Scientific Computing (CSC). The C. S. C. is supported by the California NanoSystems Institute and the Materials Research Science and Engineering Center (MRSEC; NSF DMR 1720256) at UC Santa Barbara.

Files

PhysRevD.108.064059.pdf
Files (5.1 MB)
Name Size Download all
md5:ff1d040dedfa26a535c4287cfabc8302
5.1 MB Preview Download

Additional details

Created:
October 16, 2023
Modified:
October 16, 2023