TY - JOUR

T1 - Distinguishing binary black hole precessional morphologies with gravitational wave observations

AU - Johnson-Mcdaniel, Nathan K.

AU - Phukon, Khun Sang

AU - Krishnendu, N. V.

AU - Gupta, Anuradha

N1 - Funding Information: We thank Daria Gangardt, Davide Gerosa, Leo Stein, and Aditya Vijaykumar for useful discussions. We also thank Sylvia Biscoveanu and the anonymous referees for a careful reading of the paper and useful comments and suggestions. N. K. J.-M. is supported by NSF Grant No. AST-2205920. K. S. P. acknowledges support from the Dutch Research Council (NWO). N. V. K. is thankful to the Max Planck Society’s Independent Research Group Grant. A. G. is supported in part by NSF Grants No. PHY-2308887 and No. AST-2205920. The authors are grateful for computational resources provided by the LIGO Lab and supported by NSF Grants No. PHY-0757058 and No. PHY-0823459. We also acknowledge the use of the Maple cluster at the University of Mississippi (funded by NSF Grant No. CHE-1338056), the IUCAA LDG cluster Sarathi, the University of Birmingham’s BlueBEAR HPC service, Nikhef’s Visar cluster, and Max Planck Computing and Data Facility’s clusters Raven and Cobra for the computational/numerical work. This research has made use of data or software obtained from the Gravitational Wave Open Science Center , a service of LIGO Laboratory, the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan. This study used the software packages dynesty , lalsuite , matplotlib , n um p y , p arallel b ilby , pesummary , and seaborn . This is LIGO document P2300012.

PY - 2023/11/3

Y1 - 2023/11/3

N2 - The precessional motion of binary black holes can be classified into one of three morphologies, based on the evolution of the angle between the components of the spins in the orbital plane: Circulating, librating around 0, and librating around π. These different morphologies can be related to the binary's formation channel and are imprinted in the binary's gravitational wave signal. In this paper, we develop a Bayesian model selection method to determine the preferred spin morphology of a detected binary black hole. The method involves a fast calculation of the morphology which allows us to restrict to a specific morphology in the Bayesian stochastic sampling. We investigate the prospects for distinguishing between the different morphologies using gravitational waves in the Advanced LIGO/Advanced Virgo network with their plus-era sensitivities. For this, we consider fiducial high- and low-mass binaries having different spin magnitudes and signal-to-noise ratios (SNRs). We find that in the cases with high spin and high SNR, the true morphology is strongly favored with log10 Bayes factors ≳4 compared to both alternative morphologies when the binary's parameters are not close to the boundary between morphologies. However, when the binary parameters are close to the boundary between morphologies, only one alternative morphology is strongly disfavored. In the low-spin, high-SNR cases, the true morphology is still favored with a log10 Bayes factor ∼2 compared to one alternative morphology, while in the low-SNR cases the log10 Bayes factors are at most ∼1 for many binaries. We also consider the gravitational wave signal from GW200129_065458 that has some evidence for precession (modulo data quality issues) and find that there is no preference for a specific morphology. Our method for restricting the prior to a given morphology is publicly available through an easy-to-use python package called bbh_spin_morphology_prior.

AB - The precessional motion of binary black holes can be classified into one of three morphologies, based on the evolution of the angle between the components of the spins in the orbital plane: Circulating, librating around 0, and librating around π. These different morphologies can be related to the binary's formation channel and are imprinted in the binary's gravitational wave signal. In this paper, we develop a Bayesian model selection method to determine the preferred spin morphology of a detected binary black hole. The method involves a fast calculation of the morphology which allows us to restrict to a specific morphology in the Bayesian stochastic sampling. We investigate the prospects for distinguishing between the different morphologies using gravitational waves in the Advanced LIGO/Advanced Virgo network with their plus-era sensitivities. For this, we consider fiducial high- and low-mass binaries having different spin magnitudes and signal-to-noise ratios (SNRs). We find that in the cases with high spin and high SNR, the true morphology is strongly favored with log10 Bayes factors ≳4 compared to both alternative morphologies when the binary's parameters are not close to the boundary between morphologies. However, when the binary parameters are close to the boundary between morphologies, only one alternative morphology is strongly disfavored. In the low-spin, high-SNR cases, the true morphology is still favored with a log10 Bayes factor ∼2 compared to one alternative morphology, while in the low-SNR cases the log10 Bayes factors are at most ∼1 for many binaries. We also consider the gravitational wave signal from GW200129_065458 that has some evidence for precession (modulo data quality issues) and find that there is no preference for a specific morphology. Our method for restricting the prior to a given morphology is publicly available through an easy-to-use python package called bbh_spin_morphology_prior.

UR - http://www.scopus.com/inward/record.url?scp=85177219023&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2301.10125

DO - 10.48550/arXiv.2301.10125

M3 - Article

AN - SCOPUS:85177219023

VL - 108

JO - Physical Review D

JF - Physical Review D

SN - 2470-0010

IS - 10

M1 - 103003

ER -