Poking Holes: Looking for Gaps in LIGO/Virgo's Black Hole Population

被引:23
|
作者
Edelman, Bruce [1 ]
Doctor, Zoheyr [1 ]
Farr, Ben [1 ]
机构
[1] Univ Oregon, Inst Fundamental Sci, Dept Phys, Eugene, OR 97403 USA
基金
美国国家科学基金会;
关键词
INSTABILITY MASS-LOSS; HIERARCHICAL MERGERS; GW190521; STARS; EVOLUTION;
D O I
10.3847/2041-8213/abfdb3
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
Stellar evolution models predict the existence of a gap in the black hole mass spectrum from similar to 55 M-circle dot-120M(circle dot) due to pair-instability supernovae (PISNe). We investigate the possible existence of such an "upper" mass gap in the second gravitational-wave transient catalog (GWTC-2) by hierarchically modeling the astrophysical distribution of black hole masses. We extend the TRUNCATED and POWERLAW+PEAK mass distribution families to allow for an explicit gap in the mass distribution, and apply the extended models to GWTC-2. We find that with the TRUNCATED model there is mild evidence favoring an upper mass gap with log Bayes Factor ln. = 2.79, inferring the lower and upper bounds at 56.12 (+7.54)(-4.38) M-circle dot and 103.74 (+17.01)(-6.32) M-circle dot respectively. When using the POWERLAW+PEAK model, we find no preference for the gap. When imposing tighter priors on the gap bounds centered on the expected PISNe gap bounds, the log Bayes factors in favor of a gap mildly increase. These results are however contingent on the parameter inference for the most massive binary, GW190521, for which follow-up analyses showed the source may be an intermediate mass ratio merger that has component masses straddling the gap. Using the GW190521 posterior samples from the analysis in Nitz & Capano (2021), we find an increase in Bayes factors in favor of the gap. However, the overall conclusions are unchanged: there is no preference for a gap when using the POWERLAW+PEAK model. This work paves the way for constraining the physics of pair-instability and pulsational pair-instability supernovae and high-mass black hole formation.
引用
收藏
页数:7
相关论文
共 50 条
  • [31] Optimized search for a binary black hole merger population in LIGO-Virgo O3 data
    Kumar, Praveen
    Dent, Thomas
    PHYSICAL REVIEW D, 2024, 110 (04)
  • [32] Implications of recoil kicks for black hole mergers from LIGO/Virgo catalogs
    Fragione, Giacomo
    Loeb, Abraham
    MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2021, 502 (03) : 3879 - 3884
  • [33] LIGO/Virgo Black Holes from a First Order Quark Confinement Phase Transition
    Davoudiasl, Hooman
    PHYSICAL REVIEW LETTERS, 2019, 123 (10)
  • [34] Mass Function of Stellar Black Holes as Revealed by the LIGO-Virgo-KAGRA Observations
    Dong, Xiao-Fei
    Huang, Yong-Feng
    Zhang, Zhi-Bin
    Li, Xiu-Juan
    Zou, Ze-Cheng
    Hu, Chen-Ran
    Deng, Chen
    Liu, Yang
    ASTROPHYSICAL JOURNAL, 2024, 977 (01):
  • [35] NANOGrav signal and LIGO-Virgo primordial black holes from the Higgs field
    Yi, Zhu
    Zhu, Zong-Hong
    JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS, 2022, (05):
  • [36] The importance of priors on LIGO-Virgo parameter estimation: the case of primordial black holes
    Bhagwat, S.
    De Luca, V
    Franciolini, G.
    Pani, P.
    Riotto, A.
    JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS, 2021, (01):
  • [37] Where Are LIGO's Big Black Holes?
    Fishbach, Maya
    Holz, Daniel E.
    ASTROPHYSICAL JOURNAL LETTERS, 2017, 851 (02)
  • [38] Unraveling the origin of black holes from effective spin measurements with LIGO-Virgo
    Fernandez, Nicolas
    Profumo, Stefano
    JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS, 2019, (08):
  • [39] Constraining accretion efficiency in massive binary stars with LIGO-Virgo black holes
    Bouffanais, Yann
    Mapelli, Michela
    Santoliquido, Filippo
    Giacobbo, Nicola
    Iorio, Giuliano
    Costa, Guglielmo
    MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2021, 505 (03) : 3873 - 3882
  • [40] Posterior samples of the parameters of binary black holes from Advanced LIGO, Virgo's second observing run
    De, Soumi
    Biwer, Christopher M.
    Capano, Collin D.
    Nitz, Alexander H.
    Brown, Duncan A.
    SCIENTIFIC DATA, 2019, 6 (1)