Outreach

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

Citing

If you use zfit in research, please consider citing:

@article{ESCHLE2020100508,
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.}
}

Material

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zfit workflow

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