TL;DR
zfit is a scalable, Python-based fitting library built on TensorFlow, offering a high-level API for advanced statistical modeling and analysis, particularly suited for High-Energy Physics applications.
Contribution
It introduces a pure Python alternative to RooFit with a well-defined API and TensorFlow backend, enabling efficient CPU and GPU usage for scientific data analysis.
Findings
Provides a high-level API for model building and fitting.
Enables transparent CPU and GPU utilization via TensorFlow.
Designed for easy extension and integration with scientific Python ecosystem.
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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