@Article{deBlas_etal2020,
author="de Blas, J.
and Chowdhury, D.
and Ciuchini, M.
and Coutinho, A. M.
and Eberhardt, O.
and Fedele, M.
and Franco, E.
and di Cortona, G. G.
and Miralles, V.
and Mishima, S.
and Paul, A.
and Pe{\~{n}}uelas, A.
and Pierini, M.
and Reina, L.
and Silvestrini, L.
and Valli, M.
and Watanabe, R.
and Yokozaki, N.",
title="HEPfit: a code for the combination of indirect and direct constraints on high energy physics models",
journal="European Physical Journal C",
year="2020",
publisher="Springer",
volume="80",
number="5",
pages="456 - 31pp",
abstract="HEPfit is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to (i) fit the model parameters to a given set of experimental observables; (ii) obtain predictions for observables. HEPfit can be used either in Monte Carlo mode, to perform a Bayesian Markov Chain Monte Carlo analysis of a given model, or as a library, to obtain predictions of observables for a given point in the parameter space of the model, allowing HEPfit to be used in any statistical framework. In the present version, around a thousand observables have been implemented in the Standard Model and in several new physics scenarios. In this paper, we describe the general structure of the code as well as models and observables implemented in the current release.",
optnote="WOS:000537056900002",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=4414), last updated on Sat, 13 Jun 2020 11:16:19 +0000",
issn="1434-6044",
doi="10.1140/epjc/s10052-020-7904-z",
opturl="https://arxiv.org/abs/1910.14012",
opturl="https://doi.org/10.1140/epjc/s10052-020-7904-z",
language="English"
}