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arXiv · 1910.13429

zfit: scalable pythonic fitting

Jonas Eschle·Albert Puig Navarro·Rafael Silva Coutinho·Nicola Serra
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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.

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Jonas Eschle, Albert Puig Navarro, Rafael Silva Coutinho, Nicola Serra. 2019-10-29. zfit: scalable pythonic fitting. https://doi.org/10.1016/j.softx.2020.100508

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