Published May 22, 2024 | Version v1
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Multi-Generational Black Hole Population Analysis with an Astrophysically Informed Mass Function

  • 1. ROR icon University of Bern
  • 2. Durham University
  • 3. ROR icon University of Hawaii System
  • 4. ROR icon University of Chicago


We analyze the population statistics of black holes in the LIGO/Virgo/KAGRA GWTC-3 catalog using a parametric mass function derived from simulations of massive stars experiencing pulsational pair-instability supernovae (PPISN). Our formalism enables us to separate the black hole mass function into sub-populations corresponding to mergers between objects formed via different astrophysical pathways, allowing us to infer the properties of black holes formed from stellar collapse and black holes formed via prior mergers separately. Applying this formalism, we find that this model fits the data better than the powerlaw+peak model with Bayes factor 9.7±0.1. We measure the location of the lower edge of the upper black hole mass gap to be 84.05-12.88+17.19 M, providing evidence that the 35M Gaussian peak detected in the data using other models is not associated with the PPISN pile-up predicted to precede this gap. Incorporating spin, we find that the normalized spins of stellar remnant black holes are close to zero while those of higher generation black holes tend to larger values. All of these results are in accordance with the predictions of stellar structure theory and black hole merger scenarios. Finally, we combine our mass function with the spectral siren method for measuring the Hubble constant to find H₀=36.19-10.91+17.50 km/s/Mpc and discuss potential explanations of this low value. Our results demonstrate how astrophysically-informed mass functions can facilitate the interpretation of gravitational wave catalog data to provide information about black hole formation and cosmology. Future data releases will improve the precision of our measurements.


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Additional details


UK National Phenomenology Institute ST/T001011/1
Science and Technology Facilities Council
Black Hole Archaeology 2207880
National Science Foundation
MRI Acquistion: High Performance Computing Cluster for Data Intensive Research 1920304
National Science Foundation


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Programming language
Python, Fortran
Development Status