Work

zfit: Scalable pythonic fitting

Year: 2020

Type: software-paper

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.

Source: SoftwareX

DOI: 10.1016/j.softx.2020.100508

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

Institution: University of Zurich

Language: English

Topic: Computational Physics and Python Applications

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $500

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung