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Normalizing flows as an avenue to study overlapping gravitational wave signals

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arxiv 2211.15097 v1 pith:RRGBGRCT submitted 2022-11-28 gr-qc

Jurriaan Langendorff , Alex Kolmus , Justin Janquart , Chris Van Den Broeck This is my paper · ORCID
classification gr-qc
keywords parametersignalsfacedflowsgravitational-waveinferencenormalizingoverlapping
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Due to its speed after training, machine learning is often envisaged as a solution to a manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been given for various applications in gravitational-wave data analysis. In this work, we focus on a challenging problem faced by third-generation detectors: parameter inference for overlapping signals. Due to the high detection rate and increased duration of the signals, they will start to overlap, possibly making traditional parameter inference techniques difficult to use. Here, we show a proof-of-concept application of normalizing flows to perform parameter estimation on overlapped binary black hole systems.

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Cited by 5 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. Model-agnostic search of gravitational wave echoes in LVK data

    gr-qc 2025-12 conditional novelty 6.0 of 10

    A multi-detector phase-marginalized echo search finds no statistically significant long-lived quasinormal-mode echoes in GW150914, GW231226, and GW250114, and sets 90% upper limits on their network SNR and amplitude.

  3. Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State

    gr-qc 2024-12 conditional novelty 6.0 of 10

    Normalizing flows with multibanding, heterodyning, and neural compression can produce BNS parameter posteriors and EOS constraints for 3G-detector signals in about a second, with accuracy restored by importance sampling.

  4. Probing dark matter halo profiles with multi-band observations of gravitational waves

    gr-qc 2024-11 conditional novelty 6.0 of 10

    A Fisher-matrix forecast shows that the proposed deci-Hz space detector GWSat would constrain dark matter spike density and slope around intermediate-mass ratio inspirals to sub-percent accuracy, while third-generatio...

  5. Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms

    gr-qc 2025-09 conditional novelty 5.0 of 10

    AresGW model 1's injection detection count at a false-alarm rate of 1/month varies with noise dataset by up to 39% coefficient of variation, while sensitive distance varies by only a few percent.

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