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
Search for long-lived particles decaying to \($$e ^\pm $$$$\mu ^\mp $$$$\nu $$\), Eur. Phys. J. C 81, 261 (2021)
Angular analysis of \($$ {B}^0\to {D}^{\ast -}{D}_s^{\ast +} $$with $$ {D}_s^{\ast +}\to {D}_s^{+}\gamma $$\) decays, J. High Energ. Phys. 2021, 177 (2021)
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#
If there is material missing, do not hesitate to contact us.