The project zfit aims to establish a basis in terms of API and basic functionality for a (likelihood) fitting ecosystem that is capable of dealing with the demands from High Energy Physics (HEP).

Papers and proceedings#

Used by#

The following analysis have used zfit


If you use zfit in research, please consider citing:

title = {zfit: Scalable pythonic fitting},
journal = {SoftwareX},
volume = {11},
pages = {100508},
year = {2020},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2020.100508},
url = {https://www.sciencedirect.com/science/article/pii/S2352711019303851},
author = {Jonas Eschle and Albert {Puig Navarro} and Rafael {Silva Coutinho} and Nicola Serra},
keywords = {Model fitting, Data analysis, Statistical inference, Python},
abstract = {Statistical modeling is a key element in many scientific fields and especially in
High-Energy Physics (HEP) analysis. The standard framework to perform this task in HEP is the
C++ ROOT/RooFit toolkit; with Python bindings that are only loosely integrated into the
scientific Python ecosystem. In this paper, zfit, a new alternative to RooFit written in pure Python,
is presented. Most of all, zfit provides a well defined high-level API and workflow
for advanced model building and fitting, together with an implementation on top of TensorFlow,
allowing a transparent usage of CPUs and GPUs. It is designed to be extendable in a
very simple fashion, allowing the usage of cutting-edge developments from
the scientific Python ecosystem in a transparent way. The main features of zfit are introduced,
and its extension to data analysis, especially in the context of HEP experiments, is discussed.}


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