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Subpercent Accurate Fitting of Modified Gravity Growth

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dc.contributor.author Denissenya, Mikhail
dc.contributor.author Linder, Eric V.
dc.date.accessioned 2019-07-20T10:41:23Z
dc.date.available 2019-07-20T10:41:23Z
dc.date.issued 2017-09-25
dc.identifier.citation Denissenya, M., & Linder, E. V. (2017). Subpercent accurate fitting of modified gravity growth. Journal of Cosmology and Astroparticle Physics, 2017(11), 1–8. https://doi.org/10.1088/1475-7516/2017/11/052 en_US
dc.identifier.issn 14757516
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/4035
dc.description Energetic Cosmos Laboratory. ECL Publications en_US
dc.description.abstract Adding to our previous method for dealing with gravitational modifications at redshiftz&3through a single parameter, we investigate treatment of lower redshift modifications to linear growth observables. We establish sub percent accurate fits to the redshift space distortion observablefσ8(a)using two parameters binned in redshift, testing the results for modifications with time dependence that rises, falls, is nonmonotonic, is multipeaked, and corresponds to f(R) and braneworld gravity. The residuals are then propagated to cosmological parameter biases for DESI observations, and found to cause a shift in the dark energy joint confidence contour by less than the equivalent of∼0.1σ. The proposed 2–3 parameter modified gravity description also can reveal physical characteristics of the underlying theory. en_US
dc.language.iso en en_US
dc.publisher NURIS; Energetic Cosmos Laboratory en_US
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject galaxy clustering en_US
dc.subject modified gravity en_US
dc.subject redshift surveys en_US
dc.subject The 2nd international conference of the Energetic Cosmos Laboratory (ECL) en_US
dc.subject ECL19 en_US
dc.subject Energetic Cosmos Laboratory (ECL) en_US
dc.title Subpercent Accurate Fitting of Modified Gravity Growth en_US
dc.type Article en_US
workflow.import.source science


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