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Parameter estimation methods for analyzing overlapping gravitational wave signals in the third-generation detector era

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arxiv 2211.01304 v2 pith:OTUJI4EI submitted 2022-11-02 gr-qc

classification gr-qc
keywords signalsoverlappingparameterdetectorsestimationmethodsanalyzebinary
verification ladder T0 review T1 audit T2 compute T3 formal
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In the coming years, third-generation detectors such as Einstein Telescope and Cosmic Explorer will enter the network of ground-based gravitational-wave detectors. Their current design predicts a significantly improved sensitivity band with a lower minimum frequency than existing detectors. This, combined with the increased arm length, leads to two major effects: the detection of more signals and the detection of longer signals. Both will result in a large number of overlapping signals. It has been shown that such overlapping signals can lead to biases in the recovered parameters, which would adversely affect the science extracted from the observed binary merger signals. In this work, we analyze overlapping binary black hole coalescences with two methods to analyze multi-signal observations: \textit{hierarchical subtraction} and \textit{joint parameter estimation}. We find that these methods enable a reliable parameter extraction in most cases and that joint parameter estimation is usually more precise but comes with higher computational costs.

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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 Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning

    astro-ph.IM 2025-09 conditional novelty 6.0 of 10

    A wavelet-convolution neural network distinguishes simulated lensed from unlensed gravitational waves with 92.2% accuracy (AUC 0.965) using wave-optics diffraction patterns.

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