scholarly journals Adapted gauge to small mass ratio binary black hole evolutions

2021 ◽  
Vol 103 (10) ◽  
Author(s):  
Nicole Rosato ◽  
James Healy ◽  
Carlos O. Lousto
Keyword(s):  
Black Hole ◽  
Small Mass ◽  
Mass Ratio ◽  
Binary Black Hole

scholarly journals Binary black hole merger in the extreme-mass-ratio limit

2007 ◽  
Vol 24 (12) ◽  
pp. S109-S123 ◽  
Author(s):  
Alessandro Nagar ◽  
Thibault Damour ◽  
Angelo Tartaglia
Keyword(s):  
Black Hole ◽  
Mass Ratio ◽  
Binary Black Hole ◽  
Ratio Limit

scholarly journals Who Ordered That? Unequal-mass Binary Black Hole Mergers Have Larger Effective Spins

2021 ◽  
Vol 922 (1) ◽  
pp. L5
Author(s):  
Thomas A. Callister ◽  
Carl-Johan Haster ◽  
Ken K. Y. Ng ◽  
Salvatore Vitale ◽  
Will M. Farr
Keyword(s):  
Black Hole ◽  
Odds Ratio ◽  
Hierarchical Analysis ◽  
Mass Ratio ◽  
Unequal Mass ◽  
Degree Correlation ◽  
Binary Black Hole ◽  
Formation And Evolution ◽  
The Mean

Abstract Hierarchical analysis of binary black hole (BBH) detections by the Advanced LIGO and Virgo detectors has offered an increasingly clear picture of their mass, spin, and redshift distributions. Fully understanding the formation and evolution of BBH mergers will require not just the characterization of these marginal distributions, but the discovery of any correlations that exist between the properties of BBHs. Here, we hierarchically analyze the ensemble of BBHs discovered by LIGO and Virgo with a model that allows for intrinsic correlations between their mass ratios q and effective inspiral spins χ eff. At 98.7% credibility, we find that the mean of the χ eff distribution varies as a function of q, such that more unequa-mass BBHs exhibit systematically larger χ eff. We find a Bayesian odds ratio of 10.5 in favor of a model that allows for such a correlation over one that does not. Finally, we use simulated signals to verify that our results are robust against degeneracies in the measurements of q and χ eff for individual events. While many proposed astrophysical formation channels predict some degree correlation between spins and mass ratio, these predicted correlations typically act in an opposite sense to the trend we observationally identify in the data.

One parameter binary black hole inverse problem using a sparse training set

2018 ◽  
Vol 27 (04) ◽  
pp. 1850043 ◽  
Author(s):  
M. Carrillo ◽  
M. Gracia-Linares ◽  
J. A. González ◽  
F. S. Guzmán
Keyword(s):  
Black Hole ◽  
Time Domain ◽  
Signal To Noise Ratio ◽  
Small Sample ◽  
Mass Ratio ◽  
Training Set ◽  
The Core ◽  
Binary Black Hole ◽  
Orbital Phase ◽  
The Time Domain

In this paper, we use Artificial Neural Networks (ANNs) to estimate the mass ratio [Formula: see text] in a binary black hole collision out of the gravitational wave (GW) strain. We assume the strain is a time series (TS) that contains a part of the orbital phase and the ring-down of the final black hole. We apply the method to the strain itself in the time domain and also in the frequency domain. We present the accuracy in the prediction of the ANNs trained with various values of signal-to-noise ratio (SNR). The core of our results is that the estimate of the mass ratio is obtained with a small sample of training signals and resulting in predictions with errors of the order of 1% for our best ANN configurations.

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