The Galactic Black Hole Mass Distribution

Abstract

The population of extragalactic black holes has been significantly expanded and better understood since the first detection of gravitational waves. Over the past decade, approximately 290 events have been confirmed to involve at least one black hole. In contrast, the Galactic black hole population has grown at a much slower pace, with only 42 systems possessing mass estimates, while the vast majority remain as candidates. In this work, we present the first update of a Bayesian analysis of the mass distribution of Galactic black holes since 2011, previously based on a sample of 15 low-mass X-ray binaries (LMXBs) and five high-mass X-ray binaries (HMXBs). Our update includes 28 LMXBs, six HMXBs, seven detached binaries, and one isolated black hole, also present in the living catalog CatNoir , which we introduce. We apply a Bayesian framework using five parametric models—Gaussian, two-Gaussian, lognormal, power-law, and decaying exponential—and compare their performance in fitting the observed data. Furthermore, we inspect the effect of removing model-dependent systems from the sample. Our results suggest that the power law better describes the Combined sample, while the Gaussian is preferred for the LMXB-only sample, closely followed by the two-Gaussian and lognormal. We derive the 1% quantile of the preferred model’s posterior predictive distribution to access information about the lower-mass gap. For the Combined and LMXB samples, we find M 1% > 4.06 M ⊙ and M 1% > 2.56 M ⊙ (at 90% credible intervals), respectively. The LMXB result allows for the existence of a sparsely populated gap, instead of a completely empty one.

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BibTeX RIS

BibTeX

@article{rocha2026galactic,
  title = {{The Galactic Black Hole Mass Distribution}},
  author = {Lívia S. Rocha and Nathalia Pires and Lucas M. de Sá and Bianca B. Martins and Lucas G. Barão and Guilherme R. C. Sampaio and Davi V. Rodrigues and Rachel M. G. Takahashi and M. G. B. de Avellar and J. E. Horvath and Antônio Bernardo},
  journal = {The Astrophysical Journal Supplement Series},
  year = {2026},
  month = oct,
  doi = {10.3847/1538-4365/aea6ab},
  url = {https://iopscience.iop.org/article/10.3847/1538-4365/aea6ab},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - The Galactic Black Hole Mass Distribution
AU  - Lívia S. Rocha
AU  - Nathalia Pires
AU  - Lucas M. de Sá
AU  - Bianca B. Martins
AU  - Lucas G. Barão
AU  - Guilherme R. C. Sampaio
AU  - Davi V. Rodrigues
AU  - Rachel M. G. Takahashi
AU  - M. G. B. de Avellar
AU  - J. E. Horvath
AU  - Antônio Bernardo
PY  - 2026
DA  - 2026/10/27
JO  - The Astrophysical Journal Supplement Series
DO  - 10.3847/1538-4365/aea6ab
UR  - https://iopscience.iop.org/article/10.3847/1538-4365/aea6ab
AB  - The population of extragalactic black holes has been significantly expanded and better understood since the first detection of gravitational waves. Over the past decade, approximately 290 events have been confirmed to involve at least one black hole. In contrast, the Galactic black hole population has grown at a much slower pace, with only 42 systems possessing mass estimates, while the vast majority remain as candidates. In this work, we present the first update of a Bayesian analysis of the mass distribution of Galactic black holes since 2011, previously based on a sample of 15 low-mass X-ray binaries (LMXBs) and five high-mass X-ray binaries (HMXBs). Our update includes 28 LMXBs, six HMXBs, seven detached binaries, and one isolated black hole, also present in the living catalog CatNoir , which we introduce. We apply a Bayesian framework using five parametric models—Gaussian, two-Gaussian, lognormal, power-law, and decaying exponential—and compare their performance in fitting the observed data. Furthermore, we inspect the effect of removing model-dependent systems from the sample. Our results suggest that the power law better describes the Combined sample, while the Gaussian is preferred for the LMXB-only sample, closely followed by the two-Gaussian and lognormal. We derive the 1% quantile of the preferred model’s posterior predictive distribution to access information about the lower-mass gap. For the Combined and LMXB samples, we find M 1% > 4.06 M ⊙ and M 1% > 2.56 M ⊙ (at 90% credible intervals), respectively. The LMXB result allows for the existence of a sparsely populated gap, instead of a completely empty one.
SN  - 0067-0049
SN  - 1538-4365
ER  -

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