Published October 9, 2023
| Version PyHEP2023
Software
Open
General likelihood fitting with zfit & hepstats
Creators
- 1. University of Zurich
- 2. Syracuse University
Description
Likelihood fits and inference are an essential part of most analyses in HEP. While some fitting libraries specialize on specific types of fits, zfit offers a general model fitting library.
This tutorial will introduce fitting with zfit and hepstats. The introduction will cover unbinned and binned model building, custom models, simultaneous fits and toy studies to cover most of the use-cases in HEP. Furthermore, hepstats will be used for statistical inference of limit setting, significance and sWeights. Other libraries from Scikit-HEP will be touched upon for data loading, plotting, binning, minimization - all in the context of likelihood fits.
Files
zfit/zfit-PyHEP-PyHEP2023.zip
Files
(191.4 kB)
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md5:477d7458127340b79a3699337feb33eb
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Additional details
Related works
- Is supplement to
- https://github.com/zfit/zfit-PyHEP/tree/PyHEP2023 (URL)
Funding
- Swiss National Science Foundation
- Probing the Standard Model of particle physics with rare beauty decays 168169
- Swiss National Science Foundation
- Flavour anomalies and matter-antimatter asymmetry in b-baryon decays 174182