TY - JOUR
T1 - PROSPECT
T2 - A profile likelihood code for frequentist cosmological parameter inference
AU - Holm, Emil Brinch
AU - Nygaard, Andreas
AU - Dakin, Jeppe
AU - Hannestad, Steen
AU - Tram, Thomas
N1 - Publisher Copyright:
© 2024 The Author(s).
PY - 2024/12
Y1 - 2024/12
N2 - 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 parametrization and the choice of prior on the model parameter space. In some cases, this can lead to conclusions that are driven by choice of parametrization and priors rather than by data. The profile likelihood method provides a complementary frequentist tool that 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.
AB - 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 parametrization and the choice of prior on the model parameter space. In some cases, this can lead to conclusions that are driven by choice of parametrization and priors rather than by data. The profile likelihood method provides a complementary frequentist tool that 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.
KW - cosmic background radiation
KW - cosmological parameters
KW - methods: numerical
KW - methods: statistical
UR - http://www.scopus.com/inward/record.url?scp=85212348316&partnerID=8YFLogxK
U2 - 10.1093/mnras/stae2555
DO - 10.1093/mnras/stae2555
M3 - Journal article
AN - SCOPUS:85212348316
SN - 0035-8711
VL - 535
SP - 3686
EP - 3699
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
IS - 4
ER -