Multi-Generational Black Hole Population Analysis with an Astrophysically Informed Mass Function
Yannick Ulrich, Djuna Croon, Jeremy Sakstein, Samuel McDermott

TL;DR
This paper develops an astrophysically-informed mass function model to analyze black hole populations in gravitational wave data, revealing insights into black hole formation, mass gaps, and implications for cosmology.
Contribution
It introduces a new mass function formalism that separates black hole sub-populations, improving data fit and providing detailed insights into black hole formation and merger history.
Findings
Better fit than previous models with Bayes factor 9.7
Lower edge of the black hole mass gap at ~84 M_sun
Stellar remnant spins near zero, higher generation spins larger
Abstract
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 . We measure the location of the lower edge of the upper black hole mass gap to be , providing evidence that the Gaussian peak detected in the data using other…
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Taxonomy
TopicsCosmology and Gravitation Theories · Gamma-ray bursts and supernovae · Astrophysical Phenomena and Observations
