zfit: Scalable pythonic fitting

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Article number100508
JournalSoftwareX
Volume11
DOIs
StatePublished - Jan 1 2020
Externally publishedYes

Keywords

  • Data analysis
  • Model fitting
  • Python
  • Statistical inference

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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