Horndeski in the Cosmic Linear
Anisotropy Solving System

M. Zumalacarregui, E. Bellini,
I. Sawicki, J. Lesgourgues

The Science

hi_class implements Horndeski's theory in the modern Cosmic Linear Anisotropy Solving System. It can be used to compute any linear observable in seconds, including cosmological distances, CMB, matter power and number count spectra. hi_class can be readily interfaced with Monte Python to test Gravity and Dark Energy models.

Horndeski is the most general scalar-tensor theory described by second-order equations of motion, and contains many well known models, including (but by no means limited to) covariant Galileons, Brans-Dicke, f(R), chameleons, k-essence and quintesssence. hi_class relies on a reformulation of the Effective Field Theory for Dark Energy developed by E. Bellini and I. Sawicki (see JCAP 1407 (2014) 050).

The publicly available version (hi_class teaser) is presented and described in:

hi_class has been used to obtain results in the following publications:

The Code

hi_class is currently being developed by

We are very grateful to Thomas Tram for his invaluable advice and Janina Renk for puting hi_class to work during her Master's thesis and suggesting improvements.

hi_class has been tested against the Galileon code developed by Barreira et al. (based on CAMB). The results agree within 1% for the CMB-TT spectra, 0.1% for matter power spectra and, 0.01% for the background expansion (using default precision parameters). A detailed comparison with EFTCAMB is ongoing.


hi_class is freely available to the scientific community. If you use it in a publication/preprint please cite at least the original CLASS paper and

The code can be cloned from the GitHub repository or downloaded as a compressed file. To get started and find detailed information on the available models and code functionality please read the hi_class.ini file.



If you are interesing in using a beta version or for other inquiries about hi_class please contact emilio.bellini--icc.ub.edu or miguel.zumalacarregui--nordita.org