https://doi.org/10.1140/epjc/s10052-020-7904-z
Special Article - Tools for Experiment and Theory
HEPfit: a code for the combination of indirect and direct constraints on high energy physics models
1
Dipartimento di Fisica e Astronomia “Galileo Galilei”, Università di Padova, Via Marzolo 8, 35131, Padua, Italy
2
INFN, Sezione di Padova, Via Marzolo 8, 35131, Padua, Italy
3
Centre de Physique Théorique, CNRS, École Polytechnique, IP Paris, 91128, Palaiseau, France
4
Laboratoire de Physique Théorique (UMR8627), CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91405, Orsay, France
5
INFN, Sezione di Roma Tre, Via della Vasca Navale 84, 00146, Rome, Italy
6
Paul Scherrer Institut (PSI), 5232, Villigen, Switzerland
7
IFIC, Universitat de València, CSIC, Apt. Correus 22085, 46071, Valencia, Spain
8
Dept. de Física Quàntica i Astrofísica, Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, Martí Franquès 1, 08028, Barcelona, Spain
9
INFN, Sezione di Roma, Piazzale A. Moro 2, 00185, Rome, Italy
10
Institute of Theoretical Physics, Faculty of Physics, University of Warsaw, ul. Pasteura 5, 02-093, Warsaw, Poland
11
Theory Center, IPNS, KEK, Tsukuba, 305-0801, Japan
12
DESY, Notkestraße 85, 22607, Hamburg, Germany
13
Institut für Physik, Humboldt-Universität zu Berlin, 12489, Berlin, Germany
14
CERN, Geneva, Switzerland
15
Physics Department, Florida State University, Tallahassee, FL, 32306-4350, USA
16
Theoretical Physics Department, CERN, Geneva, Switzerland
17
Department of Physics and Astronomy, University of California, Irvine, CA, 92697, USA
18
Department of Physics, Tohoku University, Sendai, Miyagi, 980-8578, Japan
* e-mail: hepfit-support@roma1.infn.it
Received:
6
November
2019
Accepted:
2
April
2020
Published online:
21
May
2020
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.
© The Author(s), 2020
