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Parameter Estimation Bias From Overlapping Binary Black Hole Events In Second Generation Interferometers

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arxiv 2103.16225 v2 pith:NXUQCRKJ submitted 2021-03-30 gr-qc astro-ph.IM

classification gr-qcastro-ph.IM
keywords biasdetectorsoverlappingeventsgenerationparameterwhencurrent
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Since the initial detection of Gravitational Waves in 2015, 50 candidate events have been reported by the LIGO-Virgo-KAGRA collaboration. As the current generation of detectors move towards their design sensitivity the rate of these detections will increase. The next generation of detectors are likely to have high enough sensitivities that multiple merging binaries will be visible at the same time. In this paper we show that this is likely to happen before the end of the decade, with the move to the LIGO-Voyager detector. We investigate the situation of overlapping Binary-Black-Hole mergers in these detectors. We find that current parameter estimation techniques are capable of distinguishing the louder of two merging BBH events, without significant bias, when their merger times are not less than $\sim0.1$ seconds apart and when the ratio of the signal-to-noise ratios of the systems is uneven. This region of overlapping parameter space is dependent upon the sky locations of the signals and the relation of those locations to the light travel time between detectors. We also find that, if two signals are highly overlapping, then the recovered set of parameters often show strong evidence of precession. Finally we show that bias can occur even when the signal causing the bias is below the detection threshold.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hierarchical Subtraction with Neural Density Estimators as a General Solution to Overlapping Gravitational Wave Signals

    gr-qc 2025-07 conditional novelty 7.0 of 10

    The paper introduces an iterative, ensemble-based hierarchical subtraction scheme powered by neural density estimators that recovers overlapping gravitational wave signals accurately and fast.

  2. Identifying lensed gravitational waves with physics-informed posterior learning

    gr-qc 2026-07 conditional novelty 6.0 of 10

    Fusing a simulation-trained common-source mass posterior with waveform features raises lensed-event detection efficiency from 20.8% to 35.2% at 1% false-positive rate and lowers the SNR for 50% efficiency from 45.3 to 33.5.

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