期刊
JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
卷 -, 期 2, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1475-7516/2020/02/008
关键词
dark energy theory; modified gravity; cosmological parameters from CMBR; power spectrum
资金
- ERC H2020 [693024]
- Beecroft Trust
- Science and Technology Facilities Council (STFC)
- European Structural and Investment Fund
- Czech Ministry of Education, Youth and Sports (MSMT) [CZ.02.1.01/0.0/0.0/15 003/0000437]
- Marie Sklodowska-Curie Global Fellowship Project NLO-CO
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [693024]
- European Research Council (ERC) [693024] Funding Source: European Research Council (ERC)
Cosmological datasets have great potential to elucidate the nature of dark energy and test gravity on the largest scales available to observation. Theoretical predictions can be computed with hi class (www.hiclass-code.net), an accurate, fast and flexible code for linear cosmology, incorporating a wide range of dark energy theories and modifications to general relativity. We introduce three new functionalities into hi class: (1) Support for models based on covariant Lagrangians, including a constraint-preserving integration scheme for the background evolution and a series of worked-out examples: Galileon, nKGB, quintessence (monomial, tracker) and Brans-Dicke. (2) Consistent initial conditions for the scalar-field perturbations in the deep radiation era, identifying the conditions under which modified-gravity isocurvature perturbations may grow faster than adiabatic modes leading to a loss of predictivity. (3) An automated quasi-static approximation scheme allowing order-of-magnitude improvement in computing performance without sacrificing accuracy for wide classes of models. These enhancements bring the treatment of dark energy and modified gravity models to the level of detail comparable to software tools restricted to standard Lambda CDM cosmologies. The hi class code is publicly available (https://github.com/miguelzuma/hi class public), ready to explore current data and prepare for next-generation experiments.
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