Springer : $\texttt{HEPfit}$: a Code for the Combination of Indirect and Direct Constraints on High Energy Physics Models

De Blas, J.; Watanabe, R.; Silvestrini, L.; Grilli Di Cortona, G.; Ciuchini, M.; Miralles, V.; Chowdhury, D.; Eberhardt, O.; Peñuelas, A.; Mishima, S.; Paul, A.; Pierini, M.; Coutinho, A.M.; Reina, L.; Fedele, M.; Valli, M.; Franco, E.; Yokozaki, N.

doi:10.1140/epjc/s10052-020-7904-z

Article
Report number	arXiv:1910.14012 ; CERN-TH-2019-178 ; CPHT-RR060.102019 ; DESY-19-184 ; DESY 19-184 ; FTUV/19-1031 ; KEK-TH-2163 ; LPT-Orsay-19-36 ; PSI-PR-19-22 ; UCI-TR-2019-26 ; IFIC/19-44
Title	$\texttt{HEPfit}$: a Code for the Combination of Indirect and Direct Constraints on High Energy Physics Models
Author(s)	De Blas, J. (Padua U. ; INFN, Padua) ; Chowdhury, D. (Ecole Polytechnique, CPHT ; Orsay, LPT) ; Ciuchini, M. (INFN, Rome) ; Coutinho, A.M. (PSI, Villigen) ; Eberhardt, O. (Valencia U., IFIC) ; Fedele, M. (Barcelona U.) ; Franco, E. (INFN, Rome) ; Grilli Di Cortona, G. (Warsaw U.) ; Miralles, V. (Valencia U.) ; Mishima, S. (KEK, Tsukuba) ; Paul, A. (DESY ; Humboldt U., Berlin) ; Peñuelas, A. (Valencia U.) ; Pierini, M. (CERN) ; Reina, L. (Florida State U.) ; Silvestrini, L. (INFN, Rome ; CERN) ; Valli, M. (UC, Irvine) ; Watanabe, R. (INFN, Rome3) ; Yokozaki, N. (Tohoku U.)
Publication	2020-05-21
Imprint	2019-10-30
Number of pages	31
Note	44 pages 5 figures
In:	Eur. Phys. J. C 80 (2020) 456
DOI	10.1140/epjc/s10052-020-7904-z (publication)
Subject category	hep-ex ; Particle Physics - Experiment ; hep-ph ; Particle Physics - Phenomenology
Abstract	$\texttt{HEPfit}$ is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to $\textit{i)}$ fit the model parameters to a given set of experimental observables; $\textit{ii)}$ obtain predictions for observables. $\texttt{HEPfit}$ 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 $\texttt{HEPfit}$ 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.
Copyright/License	publication: © 2020-2025 The Author(s) (License: CC-BY-4.0) preprint: (License: arXiv nonexclusive-distrib 1.0)

$\it Some representative runs with} \HEPfit ~{\it to show the advantages of the MPI implementation. Times are given in DD:HH:MM. The number of iterations refer to the sum total of pre-run and main-run iterations. The number of chains are equal to the number of CPUs by choice. $^\dagger$The $b\to s$ analysis is done with factorized priors, hence the number of iterations should be multiplied by the number of parameters ($\sim$50) to get a comparative estimate with the other cases. All runs performed in the BIRD or Maxwell clusters at DESY, Hamburg. : Yukawa couplings in the four possible $Z_2$ symmetric THDM types. : Model parameters in the {\tt NPEpsilons\_pureNP} class. : Model parameters in the {\tt NPSTU} class. : Model parameters in the {\tt HiggsKigen} class. ``Name'' refers to the name of the parameter in \HEPfit that can be used in the configuration files. : Some processes that have been implemented (\checkmark) or are under development ($\circ$) in \HEPfit for flavour physics.$ $\it Selected results from the papers presented in Section \ref{sec:EWResults}. Top left (from ref.~\cite{Ciuchini:2013pca}): two-dimensional probability distribution for $\varepsilon_1$ and $\varepsilon_3$ in the fit, assuming $\varepsilon_2=\varepsilon_2^{\rm SM}$ and $\varepsilon_b=\varepsilon_b^{\rm SM}$, showing the impact of different constraints. The SM prediction at 95\% is denoted by a point with an error bar. Top right (from ref.~\cite{deBlas:2016ojx}): two-dimensional $68\%$ (dark) and $95\%$ (light) probability contours for $\kappa_V$ and $\kappa_f$ (from darker to lighter), obtained from the fit to the Higgs-boson signal strengths and the EWPO. Bottom (from ref.~\cite{deBlas:2019wgy}): a scheme-ball illustration of the correlations between Higgs and EW sector couplings. The $Z$-pole runs are included for FCC-ee and CEPC. Projections from HL-LHC and measurements from LEP and SLD are included in all scenarios. The outer bars give the 1$\sigma$ precision on the individual coupling.$ $\it Selected results from the papers presented in Section \ref{sec:EWResults}. Top left (from ref.~\cite{Ciuchini:2013pca}): two-dimensional probability distribution for $\varepsilon_1$ and $\varepsilon_3$ in the fit, assuming $\varepsilon_2=\varepsilon_2^{\rm SM}$ and $\varepsilon_b=\varepsilon_b^{\rm SM}$, showing the impact of different constraints. The SM prediction at 95\% is denoted by a point with an error bar. Top right (from ref.~\cite{deBlas:2016ojx}): two-dimensional $68\%$ (dark) and $95\%$ (light) probability contours for $\kappa_V$ and $\kappa_f$ (from darker to lighter), obtained from the fit to the Higgs-boson signal strengths and the EWPO. Bottom (from ref.~\cite{deBlas:2019wgy}): a scheme-ball illustration of the correlations between Higgs and EW sector couplings. The $Z$-pole runs are included for FCC-ee and CEPC. Projections from HL-LHC and measurements from LEP and SLD are included in all scenarios. The outer bars give the 1$\sigma$ precision on the individual coupling.$ Show more plots

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