zfit: Scalable pythonic fitting

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

doi:10.1016/j.softx.2020.100508

zfit: Scalable pythonic fitting

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

Research output: Contribution to journal › Article › peer-review

6 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 language	English (US)
Article number	100508
Journal	SoftwareX
Volume	11
DOIs	https://doi.org/10.1016/j.softx.2020.100508
State	Published - Jan 1 2020
Externally published	Yes

Keywords

Data analysis
Model fitting
Python
Statistical inference

ASJC Scopus subject areas

Software
Computer Science Applications

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10.1016/j.softx.2020.100508

Cite this

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title = "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.",

keywords = "Data analysis, Model fitting, Python, Statistical inference",

author = "Jonas Eschle and {Puig Navarro}, Albert and {Silva Coutinho}, Rafael and Nicola Serra",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors",

year = "2020",

month = jan,

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doi = "10.1016/j.softx.2020.100508",

language = "English (US)",

volume = "11",

journal = "SoftwareX",

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T1 - zfit

T2 - Scalable pythonic fitting

AU - Eschle, Jonas

AU - Puig Navarro, Albert

AU - Silva Coutinho, Rafael

AU - Serra, Nicola

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Data analysis

KW - Model fitting

KW - Python

KW - Statistical inference

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zfit: Scalable pythonic fitting

Abstract

Keywords

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