zfit: Scalable pythonic fitting

Jonas Eschle, Albert Puig Navarro, Rafael Silva Coutinho, Nicola Serra

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Article number100508
StatePublished - Jan 1 2020
Externally publishedYes


  • Data analysis
  • Model fitting
  • Python
  • Statistical inference

ASJC Scopus subject areas

  • Software
  • Computer Science Applications


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