The formation of merging black holes with masses beyond 30 M ⊙ at solar metallicity

Gravitational-wave astronomy has revealed a population of stellar-mass black holes more massive than observed previously by other means. The maximum mass of black holes formed in isolated binaries is determined by stellar winds, mixing processes and interactions between the binary components. We consider the impact that fully self-consistent, detailed stellar-structure and binary-evolution calculations have on the population synthesis of black-hole binaries at solar metallicity. We find a qualitatively different picture from previous studies employing rapid population-synthesis techniques. Merging binary black holes form with a non-negligible rate ( \(\sim 4\times 1^\,_<\odot >^\) ) and their progenitor stars with initial masses ≳ 50 M ⊙ do not expand to supergiant radii, thereby largely avoiding substantial dust-driven or luminous blue variable winds. The progenitor stars lose less mass in winds, which results in black holes as massive as ~30 M ⊙ , and approximately half avoid a mass-transfer episode before forming the first-born black hole. Binaries with initial periods of a few days, some of which may undergo Roche-lobe overflow mass transfer, result in mildly spinning first-born black holes, χBH1 ≲ 0.2, assuming efficient angular-momentum transport.

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Data availability

The source and Supplementary Data used in the main manuscript and Supplementary Discussion are publicly available on Zenodo 65 . The dataset includes all single-stellar model variations discussed in the manuscript and the BBH population synthesis model that can be used to reproduce our results. Additionally, POSYDON v.1.0 single and binary grid datasets can be found on Zenodo 66 .

Code availability

The POSYDON v.1.0 software used to generate the BBH population synthesis and single-stellar models is open source and is available on GitHub 67 . Additionally, the software used to compute double compact object merger rates and to generate the intrinsic and detectable BBH observable distributions in Fig. 4 is available on GitHub 68 .

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Acknowledgements

The POSYDON project is supported primarily by two sources: the Swiss National Science Foundation Professorship grant (PI Fragos, project numbers PP00P2_176868 and PP00P2_211006) and the Gordon and Betty Moore Foundation (PI Kalogera, grant award GBMF8477). S.S.B., T.F., D.M. and E.Z. were supported by the project PP00P2_176868 and S.S.B., T.F., M.K. and Z.X. were supported by the project number PP00P2_211006. Z.X. was also supported by the Chinese Scholarship Council (CSC). E.Z. acknowledges funding support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 772086). V.K., A.D., K.A.R., P.M.S. and M.S. were supported by the project number GBMF8477. C.P.L.B. acknowledges support from the University of Glasgow. K.K. acknowledges support from the Federal Commission for Scholarships for Foreign Students for the Swiss Government Excellence Scholarship (ESKAS no. 2021.0277) and the Spanish State Research Agency, through the María de Maeztu Program for Centers and Units of Excellence in R&D, no. CEX2020-001058-M. K.A.R. also thanks the LSSTC Data Science Fellowship Program, funded by LSSTC, NSF Cybertraining grant no. 1829740, the Brinson Foundation and the Moore Foundation; their participation in the programme has benefited this work. The computations were performed at Northwestern University on the Trident computer cluster (funded by the GBMF8477 award) and at the University of Geneva on the Yggdrasil computer cluster. This research was partly supported by the computational resources and staff contributions provided for the Quest high-performance computing facility at Northwestern University, jointly supported by the Office of the Provost, the Office for Research and Northwestern University Information Technology. All figures were made with the open-source Python module Matplotlib 69 . This research used the Python modules Astropy 70 , iPython 71 , Numpy 72 , Pandas 73 and SciPy 74 .

Author information

Authors and Affiliations

  1. Département d’Astronomie, Université de Genève, Versoix, Switzerland Simone S. Bavera, Tassos Fragos, Matthias Kruckow, Konstantinos Kovlakas, Devina Misra & Zepei Xing
  2. Gravitational Wave Science Center (GWSC), Université de Genève, Geneva, Switzerland Simone S. Bavera, Tassos Fragos, Matthias Kruckow & Zepei Xing
  3. IAASARS, National Observatory of Athens, Vas. Pavlou and I. Metaxa, Penteli, Greece Emmanouil Zapartas
  4. Department of Physics, University of Florida, Gainesville, FL, USA Jeff J. Andrews
  5. Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), Northwestern University, Evanston, IL, USA Vicky Kalogera, Aaron Dotter, Kyle A. Rocha, Philipp M. Srivastava & Meng Sun
  6. Department of Physics and Astronomy, Northwestern University, Evanston, IL, US Vicky Kalogera & Kyle A. Rocha
  7. SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow, UK Christopher P. L. Berry
  8. Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Magrans, Barcelona, Spain Konstantinos Kovlakas
  9. Institut d’Estudis Espacials de Catalunya (IEEC), Barcelona, Spain Konstantinos Kovlakas
  10. Institutt for Fysikk, Norwegian University of Science and Technology, Trondheim, Norway Devina Misra
  11. Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA Philipp M. Srivastava
  1. Simone S. Bavera