Teixeira, E.M., Barros, B.J., Ferreira, V.M.C. et al. (1 more author) (2022) Dissecting kinetically coupled quintessence: phenomenology and observational tests. Journal of Cosmology and Astroparticle Physics, 2022 (11). 059. ISSN 1475-7516
Abstract
We investigate an interacting dark energy model which allows for the kinetic term of the scalar field to couple to dark matter via a power-law interaction. The model is characterised by scaling solutions at early times, which are of high interest to alleviate the coincidence problem, followed by a period of accelerated expansion. We discuss the phenomenology of the background evolution and of the linear scalar perturbations and we identify measurable signatures of the coupling in the dark sector on the cosmic microwave background, the lensing potential auto-correlation and the matter power spectra. We also perform a parameter estimation analysis using data of cosmic microwave background temperature, polarisation and lensing, baryonic acoustic oscillations and supernovae. We find that the strength of the coupling between the dark sectors, regulated by the parameter α, is constrained to be of order 10-4. A model selection analysis does not reveal a statistical preference between ΛCDM and the Kinetic model.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | cosmological parameters from CMBR; Cosmological perturbation theory in GR and beyond; cosmological simulations; dark energy theory |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Dec 2022 14:46 |
Last Modified: | 05 Dec 2022 14:46 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/1475-7516/2022/11/059 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194050 |
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Licence: CC-BY 4.0