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DC Field | Value | Language |
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dc.contributor.author | Dialektopoulos, Konstantinos F. | - |
dc.contributor.author | Mukherjee, Purba | - |
dc.contributor.author | Said, Jackson | - |
dc.contributor.author | Mifsud, Jurgen | - |
dc.date.accessioned | 2024-04-29T14:28:55Z | - |
dc.date.available | 2024-04-29T14:28:55Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Dialektopoulos, K. F., Mukherjee, P., Said, J. L., & Mifsud, J. (2023). Neural network reconstruction of scalar-tensor cosmology. Physics of the Dark Universe, 43, 101383. | en_GB |
dc.identifier.issn | 22126864 | - |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/121579 | - |
dc.description.abstract | Neural networks have shown great promise in providing a data-first approach to exploring new physics. In this work, we use the full implementation of late time cosmological data to reconstruct a number of scalar-tensor cosmological models within the context of neural network systems. In this pipeline, we incorporate covariances in the data in the neural network training algorithm, rather than a likelihood which is the approach taken in Markov chain Monte Carlo analyses. For general subclasses of classic scalar-tensor models, we find stricter bounds on functional models which may help in the understanding of which models are observationally viable. Specifically, we show that the quintessence potential cannot deviate much from a linear behavior at the redshifts of interest, while in higher derivative theory. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier BV | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Cosmology -- Research | en_GB |
dc.subject | Gravitational waves | en_GB |
dc.subject | Scalar field theory | en_GB |
dc.subject | Dark energy (Astronomy) | en_GB |
dc.subject | Neural Networks (Computer Science) | en_GB |
dc.title | Neural network reconstruction of scalar-tensor cosmology | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of tis work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1016/j.dark.2023.101383 | - |
dc.publication.title | Physics of the Dark Universe | en_GB |
Appears in Collections: | Scholarly Works - InsSSA |
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Neural_network_reconstruction_of_scalar-tensor_cosmology(2023).pdf Restricted Access | 5.27 MB | Adobe PDF | View/Open Request a copy |
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