TY - JOUR
T1 - zfit
T2 - Scalable pythonic fitting
AU - Eschle, Jonas
AU - Puig Navarro, Albert
AU - Silva Coutinho, Rafael
AU - Serra, Nicola
N1 - Funding Information:
We are grateful to Anton Poluektov, Chris Burr and IgorBabuschkin for demonstrating the potential of unbinned model fitting within the context of TensorFlow, which inspired this work. We also thank the Zurich LHCb Group, Matthieu Marinangeli, Josh Bendavid, Lukas Heinrich and the HSF community, especially Scikit-HEP project members, for useful discussions. A. Puig, R. Silva Coutinho, J.Eschle and N. Serra gratefully acknowledge the support by the Swiss National Science Foundation (SNF) under contracts 168169 , 174182 and 182622 .
Publisher Copyright:
© 2020 The Authors
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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U2 - 10.1016/j.softx.2020.100508
DO - 10.1016/j.softx.2020.100508
M3 - Article
AN - SCOPUS:85084740308
SN - 2352-7110
VL - 11
JO - SoftwareX
JF - SoftwareX
M1 - 100508
ER -