CaltechAUTHORS
Published February 15, 2017 | Version Published + Submitted
Journal Article Open

Improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors

Creators

  • 1. ROR icon Max Planck Institute for Gravitational Physics
  • 2. ROR icon University of Maryland, College Park
  • 3. ROR icon Princeton University
  • 4. ROR icon Canadian Institute for Theoretical Astrophysics
  • 5. ROR icon University of Toronto
  • 6. ROR icon Canadian Institute for Advanced Research
  • 7. ROR icon Cornell University
  • 8. ROR icon California Institute of Technology
  • 9. ROR icon California State University, Fullerton
  • 10. ROR icon Jet Propulsion Lab

Abstract

We improve the accuracy of the effective-one-body (EOB) waveforms that were employed during the first observing run of Advanced LIGO for binaries of spinning, nonprecessing black holes by calibrating them to a set of 141 numerical-relativity (NR) waveforms. The NR simulations expand the domain of calibration toward larger mass ratios and spins, as compared to the previous EOBNR model. Merger-ringdown waveforms computed in black-hole perturbation theory for Kerr spins close to extremal provide additional inputs to the calibration. For the inspiral-plunge phase, we use a Markov-chain Monte Carlo algorithm to efficiently explore the calibration space. For the merger-ringdown phase, we fit the NR signals with phenomenological formulae. After extrapolation of the calibrated model to arbitrary mass ratios and spins, the (dominant-mode) EOBNR waveforms have faithfulness—at design Advanced-LIGO sensitivity—above 99% against all the NR waveforms, including 16 additional waveforms used for validation, when maximizing only on initial phase and time. This implies a negligible loss in event rate due to modeling for these binary configurations. We find that future NR simulations at mass ratios ≳4 and double spin ≳0.8 will be crucial to resolving discrepancies between different ways of extrapolating waveform models. We also find that some of the NR simulations that already exist in such region of parameter space are too short to constrain the low-frequency portion of the models. Finally, we build a reduced-order version of the EOBNR model to speed up waveform generation by orders of magnitude, thus enabling intensive data-analysis applications during the upcoming observation runs of Advanced LIGO.

Additional Information

© 2017 American Physical Society. Received 14 November 2016; published 17 February 2017. We would like to thank Mark Hannam and Sascha Husa for kindly providing us with the nonpublic BAM (q,χ_1,χ_2)=(8,0.85,0.85) waveform, which was used in Sec. IV to calibrate the EOB model. This work was supported in part at Caltech by the Sherman Fairchild Foundation and NSF Grants No. PHY-1404569, at Cornell by NSF Grants No. PHY-1606654 and No. AST-1333129 and the Sherman Fairchild Foundation and at Cal State Fullerton by NSF grants PHY-1307489 and PHY-1606522. We gratefully acknowledge support for this research at CITA from NSERC of Canada, the Ontario Early Researcher Awards Program, the Canada Research Chairs Program, and the Canadian Institute for Advanced Research. Calculations were performed at the GPC supercomputer at the SciNet HPC Consortium; SciNet is funded by: the Canada Foundation for Innovation (CFI) under the auspices of Compute Canada; the Government of Ontario; Ontario Research Fund (ORF)—Research Excellence; and the University of Toronto. Further calculations were performed on the Briarée cluster at Sherbrooke University, managed by Calcul Québec and Compute Canada and with operation funded by the Canada Foundation for Innovation (CFI), Ministère de l'Économie, de l'Innovation et des Exportations du Quebec (MEIE), RMGA and the Fonds de recherche du Québec–Nature et Technologies (FRQ-NT). Some of the calculations were performed on the ORCA cluster at Cal State Fullerton, which is supported by the Research Corporation for Science Advancement, PHY-1429873, and Cal State Fullerton. New NR simulations were performed on the AEI Datura and Minerva clusters. The Markov-chain Monte Carlo runs were performed on the AEI Vulcan cluster.

Attached Files

Published - PhysRevD.95.044028.pdf

Submitted - 1611.03703.pdf

Files

1611.03703.pdf

Files (12.2 MB)

Name Size
md5:0f42c8776d32567c7d0806e41c7e06bd
7.8 MB Preview Download
md5:cfe8a8c5797b564ac86a03c7b70fe7fd
4.4 MB Preview Download

Additional details

Identifiers

Eprint ID
74463
Resolver ID
CaltechAUTHORS:20170222-095610973

Related works

Describes
https://arxiv.org/abs/1611.03703 (URL)

Funding

Sherman Fairchild Foundation
NSF
PHY-1404569
NSF
PHY-1606654
NSF
AST-1333129
NSF
PHY-1307489
NSF
PHY-1606522
Natural Sciences and Engineering Research Council of Canada (NSERC)
Ontario Early Researcher Awards Program
Canada Research Chairs Program
Canadian Institute for Advanced Research (CIFAR)
Canada Foundation for Innovation
Compute Canada
Government of Ontario
Ontario Research Fund-Research Excellence
University of Toronto
Ministère de l'Économie, de l'Innovation et des Exportations du Quebec (MEIE)
RMGA
Fonds de recherche du Québec–Nature et Technologies (FRQ-NT)
NSF
PHY-1429873
California State University, Fullerton

Dates

Created
2017-02-22
Created from EPrint's datestamp field
Updated
2021-11-11
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
LIGO , Physics Department