hi_class: Background Evolution, Initial Conditions and Approximation Schemes

TL;DR

hi_class is an advanced, flexible code for linear cosmology that now includes new functionalities for covariant models, initial conditions, and efficient approximations, enhancing dark energy and modified gravity research.

Contribution

The paper introduces three new features in hi_class: support for covariant Lagrangian models, consistent initial conditions for scalar perturbations, and an automated quasistatic approximation scheme.

Findings

Supports a wide range of dark energy models.

Provides accurate initial conditions for scalar fields.

Improves computational performance significantly.

Abstract

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 quasistatic 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.