- Editors' Suggestion
- Access by Xinjiang University
Overlapping signals in next-generation gravitational wave observatories: A recipe for selecting the best parameter estimation technique
Phys. Rev. D 112, 082001 – Published 3 October, 2025
DOI: https://doi.org/10.1103/8cwp-mxcd
Abstract
Third-generation gravitational wave detectors such as Einstein Telescope and Cosmic Explorer will have significantly better sensitivities than current detectors, as well as a wider frequency bandwidth. This will increase the number and duration of the observed signals, leading to many signals overlapping in time. If not adequately accounted for, this can lead to biases in parameter estimation. In this work, we combine the joint parameter estimation method with relative binning to handle full parameter inference on overlapping signals from binary black holes, including precession effects and higher-order mode content. As this method is computationally more expensive than traditional single-signal parameter estimation, we test a prior-informed Fisher matrix and a time-frequency overlap method for estimating expected bias to help us decide when joint parameter estimation is necessary over the simpler methods. We improve upon previous Fisher matrix implementations by including the prior information and performing an optimization routine to better locate the maximum likelihood point, but we still find the method unreliable. The time-frequency method is accurate in 86% of close binary black hole mergers. We end by developing our own method of estimating bias due overlaps, where we reweight the single signal parameter estimation posterior to quantify how much the overlapping signals affect it. We show it has 99% accuracy for zero noise injections (98% in Gaussian noise), at the cost of one additional standard sampling run when joint parameter estimation proves to be necessary.
Physics Subject Headings (PhySH)
Article Text
References (59)
- J. Aasi et al. (LIGO Scientific Collaboration), Advanced LIGO, Classical Quantum Gravity 32, 074001 (2015).
- F. Acernese et al. (Virgo Collaboration), Advanced Virgo: A second-generation interferometric gravitational wave detector, Classical Quantum Gravity 32, 024001 (2015).
- The LIGO Scientific, the Virgo, and KAGRA Collaborations, LIGO/Virgo/KAGRA public alerts, GraceDB (2025).
- R. Abbott et al. (KAGRA, Virgo, and LIGO Scientific Collaborations), GWTC-3: Compact binary coalescences observed by LIGO and Virgo during the second part of the third observing run, Phys. Rev. X 13, 041039 (2023).
- R. Abbott et al. (LIGO Scientific, Virgo, and KAGRA Collaborations), Tests of general relativity with GWTC-3, arXiv:2112.06861.
- R. Abbott et al. (LIGO Scientific, Virgo, and KAGRA Collaborations), The population of merging compact binaries inferred using gravitational waves through GWTC-3, Phys. Rev. X 13, 011048 (2023).
- R. Abbott et al. (LIGO Scientific, Virgo, and KAGRA Collaborations), Constraints on the cosmic expansion history from GWTC-3, Astrophys. J. 949, 76 (2023).
- Report of the LSC Post-O5 study group, https://dcc.ligo.org/LIGO-T2200287/public (accessed: 2025-03-20).
- Kentaro Somiya (KAGRA Collaboration), Detector configuration of KAGRA: The Japanese cryogenic gravitational-wave detector, Classical Quantum Gravity 29, 124007 (2012).
- Yoichi Aso, Yuta Michimura, Kentaro Somiya, Masaki Ando, Osamu Miyakawa, Takanori Sekiguchi, Daisuke Tatsumi, and Hiroaki Yamamoto (KAGRA Collaboration), Interferometer design of the KAGRA gravitational wave detector, Phys. Rev. D 88, 043007 (2013).
- Bala Iyer et al., LIGO-India, Proposal of the Consortium for Indian Initiative in Gravitational-Wave Observations (IndIGO), https://dcc.ligo.org/LIGO-M1100296/public (2021).
- M. Punturo et al., The Einstein Telescope: A third-generation gravitational wave observatory, Classical Quantum Gravity 27, 194002 (2010).
- Adrian Abac et al., The science of the Einstein Telescope, arXiv:2503.12263.
- David Reitze et al., Cosmic Explorer: The U.S. Contribution to gravitational-wave astronomy beyond LIGO, Bull. Am. Astron. Soc. 51, 035 (2019), https://baas.aas.org/pub/2020n7i035/release/1.
- B. Sathyaprakash et al., Scientific objectives of Einstein Telescope, Classical Quantum Gravity 29, 124013 (2012).
- Tania Regimbau and Scott A. Hughes, Gravitational-wave confusion background from cosmological compact binaries: Implications for future terrestrial detectors, Phys. Rev. D 79, 062002 (2009).
- A. Samajdar, J. Janquart, C. Van Den Broeck, and T. Dietrich, Biases in parameter estimation from overlapping gravitational-wave signals in the third-generation detector era, Phys. Rev. D 104, 044003 (2021).
- E. Pizzati, S. Sachdev, A. Gupta, and B. S. Sathyaprakash et al., Toward inference of overlapping gravitational-wave signals, Phys. Rev. D 105, 104016 (2022).
- P. Relton and V. Raymond, Parameter estimation bias from overlapping binary black hole events in second generation interferometers, Phys. Rev. D 104, 084039 (2021).
- Yoshiaki Himemoto, Atsushi Nishizawa, and Atsushi Taruya, Impacts of overlapping gravitational-wave signals on the parameter estimation: Toward the search for cosmological backgrounds, Phys. Rev. D 104, 044010 (2021).
- J. Janquart, T. Baka, A. Samajdar, T. Dietrich, and C. Van Den Broeck, Analyses of overlapping gravitational wave signals using hierarchical subtraction and joint parameter estimation, Mon. Not. R. Astron. Soc. 523, 1699 (2023).
- Qian Hu and John Veitch, Accumulating errors in tests of general relativity with gravitational waves: Overlapping signals and inaccurate waveforms, Astrophys. J. 945, 103 (2023).
- A. Antonelli, O. Burke, and J. R. Gair, Noisy neighbours: Inference biases from overlapping gravitational-wave signals, Mon. Not. R. Astron. Soc. 507, 5069 (2021).
- Ulyana Dupletsa, Jan Harms, Ken K. Y. Ng, Jacopo Tissino, Filippo Santoliquido, and Andrea Cozzumbo, Validating prior-informed Fisher-matrix analyses against GWTC data, Phys. Rev. D 111, 024036 (2025).
- Aaron D. Johnson, Katerina Chatziioannou, and Will M. Farr, Source confusion from neutron star binaries in ground-based gravitational wave detectors is minimal, Phys. Rev. D 109, 084015 (2024).
- B. Zackay, L. Dai, and T. Venumadhav, Relative binning and fast likelihood evaluation for gravitational wave parameter estimation, arXiv:1806.08792.
- L. Dai, T. Venumadhav, and B. Zackay, Parameter estimation for GW170817 using relative binning, arXiv:1806.08793.
- N. Leslie, L. Dai, and G. Pratten, Mode-by-mode relative binning: Fast likelihood estimation for gravitational waveforms with spin-orbit precession and multiple harmonics, Phys. Rev. D 104, 123030 (2021).
- H. Narola, J. Janquart, Q. Meijer, K. Haris, and C. Van Den Broeck, Relative binning for complete gravitational-wave parameter estimation with higher-order modes and precession, and applications to lensing and third-generation detectors, Phys. Rev. D 110, 084085 (2024).
- J. Veitch and A. Vecchio, Bayesian coherent analysis of in-spiral gravitational wave signals with a detector network, Phys. Rev. D 81, 062003 (2010).
- Cajo J. F. Ter Braak, A Markov Chain Monte Carlo version of the genetic algorithm differential evolution: Easy Bayesian computing for real parameter spaces, Stat. Comput. 16, 239 (2006).
- Neil J. Cornish, Fast Fisher matrices and lazy likelihoods, arXiv:1007.4820.
- Kruthi Krishna et al., Accelerated parameter estimation in bilby with relative binning, arXiv:2312.06009.
- J. Janquart and H. Narola, Relativebilbying: A package for relative binning with bilby, https://github.com/lemnis12/relativebilbying (2022).
- G. Ashton et al., bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy, Astrophys. J. Suppl. Ser. 241, 27 (2019).
- I. M. Romero-Shaw et al., Bayesian inference for compact binary coalescences with bilby: Validation and application to the first LIGO—Virgo gravitational-wave transient catalogue, Mon. Not. R. Astron. Soc. 499, 3295 (2020).
- Geraint Pratten et al., Computationally efficient models for the dominant and subdominant harmonic modes of precessing binary black holes, Phys. Rev. D 103, 104056 (2021).
- Marica Branchesi et al., Science with the Einstein Telescope: A comparison of different designs, J. Cosmol. Astropart. Phys. 07 (2023) 068.
- S. Hild et al., Sensitivity studies for third-generation gravitational wave observatories, Classical Quantum Gravity 28, 094013 (2011).
- Benjamin P. Abbott et al. (LIGO Scientific Collaborations), Exploring the sensitivity of next generation gravitational wave detectors, Classical Quantum Gravity 34, 044001 (2017).
- Joshua S. Speagle, dynesty: A dynamic nested sampling package for estimating Bayesian posteriors and evidences, Mon. Not. R. Astron. Soc. 493, 3132 (2019).
- Victor Elvira, Luca Martino, and Christian P. Robert, Rethinking the effective sample size, Int. Stat. Rev. 90, 525 (2022).
- Qian Hu, Hierarchical subtraction with neural density estimators as a general solution to overlapping gravitational wave signals, arXiv:2507.05209.
- J. Veitch et al., Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library, Phys. Rev. D 91, 042003 (2015).
- Frank J. Massey, The Kolmogorov-Smirnov test for goodness of fit, J. Am. Stat. Assoc. 46, 68 (1951).
- R. A. Fisher, Statistical Methods for Research Workers (Oliver & Boyd, Edinburgh, 1925).
- Masamune Oguri, Effect of gravitational lensing on the distribution of gravitational waves from distant binary black hole mergers, Mon. Not. R. Astron. Soc. 480, 3842 (2018).
- Lee S. Finn, Detection, measurement and gravitational radiation, Phys. Rev. D 46, 5236 (1992).
- Curt Cutler and Michele Vallisneri, LISA detections of massive black hole inspirals: Parameter extraction errors due to inaccurate template waveforms, Phys. Rev. D 76, 104018 (2007).
- R. Storn and K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim. 11, 341 (1997).
- P. Virtanen et al., scipy 1.0: Fundamental algorithms for scientific computing in python, Nat. Methods 17, 261 (2020).
- Z. I. Botev, The normal law under linear restrictions: Simulation and estimation via minimax tilting, J. R. Stat. Soc. Ser. B 79, 125 (2016).
- J. Lin, Divergence measures based on the Shannon entropy, IEEE Trans. Inf. Theory 37, 145 (1991).
- S. Kullback and R. A. Leibler, On information and sufficiency, Ann. Math. Stat. 22, 79 (1951).
- B. P. Abbott et al., The basic physics of the binary black hole merger GW150914, Ann. Phys. (Berlin) 529, 1600209 (2017).
- P. Relton, A. Virtuoso, S. Bini, V. Raymond, I. Harry, M. Drago, C. Lazzaro, A. Miani, and S. Tiwari, Addressing the challenges of detecting time-overlapping compact binary coalescences, Phys. Rev. D 106, 104045 (2022).
- J. D. Hunter, matplotlib: A 2D graphics environment, Comput. Sci. Eng. 9, 90 (2007).
- Charles R. Harris et al., Array programming with numpy, Nature (London) 585, 357 (2020).
- LIGO Scientific, Virgo, and KAGRA Collaborations, LVK Algorithm Library—LALSuite, Free software (GPL) (2018).