Multiband gravitational wave cosmography with dark sirens

Seymour, Brian C.; Yu, Hang; Chen, Yanbei

doi:10.1103/physrevd.108.044038

Cited by 2 publications

(2 citation statements)

References 114 publications

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“…If, on the other hand, dark sirens could also be localized with sufficient precision using galaxy cross correlation or any other viable technique(s) (Oguri 2016;Gray et al 2023), their addition would significantly increase the number of detectable events at higher redshifts, thus bolstering the fidelity of the results obtained through our adopted methodology. Such prospects have attracted considerable attention in recent years in light of a number of proposed ground-based and space-based GW missions (Seymour et al 2022;Wang et al 2022;Jin et al 2023;Muttoni et al 2023;Zhu & Chen 2023). As far as the reconstruction methodology demonstrated in this work is concerned, these are a couple of other challenging directions that would be interesting to pursue in the near future.…”

Section: Discussionmentioning

confidence: 94%

“…Such lines of inquiry, alongside the inherent limitations arising from the sensitivity bounds of the present observatories, naturally lead to the question of how efficient next-generation GW missions might be when it comes to the question of constraining the expansion history of our Universe. Constraints based on future GW data are expected to be much more precise due to significantly higher numbers of detectable events up to higher redshifts, whose prospects have been explored by multiple authors (Messenger & Read 2012;Kyutoku & Seto 2017;Mortlock et al 2019;Feeney et al 2021;Cao et al 2022;Chen et al 2022;Ghosh et al 2022;Liu et al 2022;Seymour et al 2022;Califano et al 2023;Jana et al 2023;Jin et al 2023;Zhang et al 2023). However, the predicted results show dependence on the choice of cosmological parameters when real data is absent and forecasts are carried out using mission-specific mock standard siren catalogs (Shah et al 2023).…”

Section: Introductionmentioning

confidence: 99%

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Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning

Mukherjee,

Shah,

Bhaumik

et al. 2023

ApJ

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We study the prospects of Gaussian processes (GPs), a machine-learning (ML) algorithm, as a tool to reconstruct the Hubble parameter H(z) with two upcoming gravitational-wave (GW) missions, namely, the evolved Laser Interferometer Space Antenna (eLISA) and the Einstein Telescope (ET). Assuming various background cosmological models, the Hubble parameter has been reconstructed in a nonparametric manner with the help of a GP using realistically generated catalogs for each mission. The effects of early-time and late-time priors on the reconstruction of H(z), and hence on the Hubble constant (H 0), have also been focused on separately. Our analysis reveals that a GP is quite robust in reconstructing the expansion history of the Universe within the observational window of the specific missions under consideration. We further confirm that both eLISA and ET would be able to provide constraints on H(z) and H 0, which would be competitive to those inferred from current data sets. In particular, we observe that an eLISA run of a ∼10 yr duration with ∼80 detected bright siren events would be able to constrain H 0 as precisely as a ∼3 yr ET run assuming ∼1000 bright siren event detections. Further improvement in precision is expected for longer eLISA mission durations such as a ∼15 yr time frame having ∼120 events. Lastly, we discuss the possible role of these future GW missions in addressing the Hubble tension, for each model, on a case-by-case basis.

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Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning

Mukherjee,

Shah,

Bhaumik

et al. 2023

ApJ

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Prospects for detecting neutron star–white dwarf mergers with decihertz gravitational-wave observatories

Kang,

Liu,

Zhu

et al. 2024

Monthly Notices of the Royal Astronomical Society

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Based on different neutron star–white dwarf (NS-WD) population models, we investigate the prospects of gravitational-wave (GW) detections for NS-WD mergers, with the help of early warnings from two space-borne decihertz GW observatories, DO-Optimal and DECIGO. We not only give quick assessments of the GW detection rates for NS-WD mergers with the two decihertz GW detectors, but also report systematic analyses on the characteristics of GW-detectable merger events using the method of Fisher matrix. With a sufficient one-day early-warning time, the yearly GW detection number for DO-Optimal is in the range of (1.5–1.9) × 103, while it is (3.3–4.6) × 104 for DECIGO. More importantly, our results show that most NS-WD mergers can be localized with an uncertainty of $\mathcal {O}(10^{-2})\, \mathrm{deg}^2$. Given the NS-WD merger as a possible origin for a peculiar long-duration gamma-ray burst, GRB 211211A, followed with kilonova-like emissions, we further suggest that the GW early-warning detection would allow future electromagnetic telescopes to get prepared to follow-up transients after some special NS-WD mergers. Based on our analyses, we emphasize that such a feasible ‘wait-for’ pattern can help to firmly identify the origin of GRB 211211A-like events in the future and bring excellent opportunities for the multimessenger astronomy.

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Multiband gravitational wave cosmography with dark sirens

Cited by 2 publications

References 114 publications

Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning

Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning

Prospects for detecting neutron star–white dwarf mergers with decihertz gravitational-wave observatories

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