Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/130465
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Type: Journal article
Title: CosmoBit: a GAMBIT module for computing cosmological observables and likelihoods
Author: Renk, J.J.
Stöcker, P.
Bloor, S.
Hotinli, S.
Balázs, C.
Bringmann, T.
Gonzalo, T.E.
Handley, W.
Hoof, S.
Howlett, C.
Kahlhoefer, F.
Scott, P.
Vincent, A.C.
White, M.
Citation: Journal of Cosmology and Astroparticle Physics, 2021; 2021(2):022-1-022-100
Publisher: IOP Publishing
Issue Date: 2021
ISSN: 1475-7516
1475-7516
Statement of
Responsibility: 
Janina J. Renk, Patrick Stöcker, Sanjay Bloor, Selim Hotinli, Csaba Balázs, Torsten Bringmann ... et al.
Abstract: We introduce CosmoBit, a module within the open-source GAMBIT software framework for exploring connections between cosmology and particle physics with joint global fits. CosmoBit provides a flexible framework for studying various scenarios beyond ΛCDM, such as models of inflation, modifications of the effective number of relativistic degrees of freedom, exotic energy injection from annihilating or decaying dark matter, and variations of the properties of elementary particles such as neutrino masses and the lifetime of the neutron. Many observables and likelihoods in CosmoBit are computed via interfaces to AlterBBN, CLASS, DarkAges, MontePython, MultiModeCode, and plc. This makes it possible to apply a wide range of constraints from large-scale structure, Type Ia supernovae, Big Bang Nucleosynthesis and the cosmic microwave background. Parameter scans can be performed using the many different statistical sampling algorithms available within the GAMBIT framework, and results can be combined with calculations from other GAMBIT modules focused on particle physics and dark matter. We include extensive validation plots and a first application to scenarios with non-standard relativistic degrees of freedom and neutrino temperature, showing that the corresponding constraint on the sum of neutrino masses is much weaker than in the standard scenario.
Rights: © 2021 IOP Publishing Ltd and Sissa Medialab
DOI: 10.1088/1475-7516/2021/02/022
Grant ID: http://purl.org/au-research/grants/arc/FT190100814
http://purl.org/au-research/grants/arc/DP180102209
http://purl.org/au-research/grants/arc/FL180100168
Published version: http://dx.doi.org/10.1088/1475-7516/2021/02/022
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