zfit: scalable pythonic fitting
| Autor: | Eschle, Jonas, Navarro, Albert Puig, Coutinho, Rafael Silva, Serra, Nicola |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | SoftwareX 11 (2020) 100508 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/j.softx.2020.100508 |
| Popis: | 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. Comment: 12 pages, 2 figures |
| Databáze: | arXiv |
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