August 30, 2021 to September 3, 2021
University of Innsbruck
Europe/Zurich timezone

【396】zfit: scalable pythonic fitting

Sep 2, 2021, 6:15 PM
Room D

Room D

Talk Nuclear, Particle- and Astrophysics (FAKT - TASK) Nuclear, Particle- & Astrophysics


Jonas Eschle (Universitaet Zuerich (CH))


Statistical modelling is a key element in many parts of physics, especially in High-Energy Physics (HEP). zfit is a Python library for unbinned, likelihood model fitting. Its main computational backend is TensorFlow, an easy-to-use, highly scalable computing library similar to Numpy. zfit provides a high level interface for advanced model building and fitting while also designed with a unified interface to be easily extendable, allowing the usage of custom and cutting-edge developments from the scientific Python ecosystem in a transparent way.
This talk presents zfit and its usability for data analyses in physics, especially in HEP, as well as recent developments and improvements to the library.

Primary authors

Albert Puig Navarro (Universität Zürich (CH)) Jonas Eschle (Universitaet Zuerich (CH)) Matthieu Marinangeli (EPFL - Ecole Polytechnique Federale Lausanne (CH)) Nicola Serra (Universitaet Zuerich (CH)) Rafael Silva Coutinho (Universitaet Zuerich (CH))

Presentation materials