부산대학교 도서관

Cosmological parameter inference has been dominated by the Bayesian approach for the past two decades, primarily due to its computational efficiency. However, the Bayesian approach involves integration of the posterior probability and therefore depends on both the choice of model parametrisation and the choice of prior on the model parameter space. In some cases, this can lead to conclusions which are driven by choice of parametrisation and priors rather than by data. The profile likelihood method provides a complementary frequentist tool which can be used to investigate this effect. In this paper, we present the code PROSPECT for computing profile likelihoods in cosmology. We showcase the code using a phenomenological model for converting dark matter into dark radiation that suffers from large volume effects and prior dependence. PROSPECT is compatible with both cobaya and MontePython, and is publicly available at https://github.com/AarhusCosmology/prospect_public.
Comment: 27 pages, 9 figures; v2: submitted to JCAP