zfit: scalable pythonic fitting
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.
Cite
@article{arxiv.1910.13429,
title = {zfit: scalable pythonic fitting},
author = {Jonas Eschle and Albert Puig Navarro and Rafael Silva Coutinho and Nicola Serra},
journal= {arXiv preprint arXiv:1910.13429},
year = {2020}
}
Comments
12 pages, 2 figures