Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning
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 va...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4357/ad055f |
| _version_ | 1797382525317808128 |
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| author | Purba Mukherjee Rahul Shah Arko Bhaumik Supratik Pal |
| author_facet | Purba Mukherjee Rahul Shah Arko Bhaumik Supratik Pal |
| author_sort | Purba Mukherjee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T21:07:39Z |
| format | Article |
| id | doaj.art-a2b78883bc144ad7a2ed25f6d9f7f7ff |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-08T21:07:39Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-a2b78883bc144ad7a2ed25f6d9f7f7ff2023-12-22T11:08:54ZengIOP PublishingThe Astrophysical Journal1538-43572023-01-0196016110.3847/1538-4357/ad055fReconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine LearningPurba Mukherjee0https://orcid.org/0000-0002-2701-5654Rahul Shah1https://orcid.org/0000-0001-7682-9219Arko Bhaumik2https://orcid.org/0000-0002-8421-9397Supratik Pal3https://orcid.org/0000-0003-4136-329XPhysics and Applied Mathematics Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata 700 108, India ; purba16@gmail.comPhysics and Applied Mathematics Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata 700 108, India ; purba16@gmail.comPhysics and Applied Mathematics Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata 700 108, India ; purba16@gmail.comPhysics and Applied Mathematics Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata 700 108, India ; purba16@gmail.com; Technology Innovation Hub on Data Science, Big Data Analytics and Data Curation, Indian Statistical Institute , 203, B.T. Road, Kolkata 700 108, IndiaWe 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.https://doi.org/10.3847/1538-4357/ad055fHubble diagramHubble constantGravitational wavesGravitational wave detectorsInterferometersGaussian Processes regression |
| spellingShingle | Purba Mukherjee Rahul Shah Arko Bhaumik Supratik Pal Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning The Astrophysical Journal Hubble diagram Hubble constant Gravitational waves Gravitational wave detectors Interferometers Gaussian Processes regression |
| title | Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning |
| title_full | Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning |
| title_fullStr | Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning |
| title_full_unstemmed | Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning |
| title_short | Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning |
| title_sort | reconstructing the hubble parameter with future gravitational wave missions using machine learning |
| topic | Hubble diagram Hubble constant Gravitational waves Gravitational wave detectors Interferometers Gaussian Processes regression |
| url | https://doi.org/10.3847/1538-4357/ad055f |
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