pyhf: pure-python HistFactory -- the tutorial

<!--HTML-->The HistFactory p.d.f. template [CERN-OPEN-2012-016][1] is per-se independent of its implementation in ROOT and sometimes, it’s useful to be able to run statistical analysis outside of ROOT, RooFit, RooStats framework. This tutorial will introduce users (who are generally familiar w...

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Detalles Bibliográficos
Autores principales: Stark, Giordon Holtsberg, Heinrich, Lukas Alexander, Feickert, Matthew
Lenguaje:eng
Publicado: 2021
Materias:
LPCC Workshops
Acceso en línea:http://cds.cern.ch/record/2752552
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author Stark, Giordon Holtsberg
Heinrich, Lukas Alexander
Feickert, Matthew
author_facet Stark, Giordon Holtsberg
Heinrich, Lukas Alexander
Feickert, Matthew
author_sort Stark, Giordon Holtsberg
collection CERN
description <!--HTML-->The HistFactory p.d.f. template [CERN-OPEN-2012-016][1] is per-se independent of its implementation in ROOT and sometimes, it’s useful to be able to run statistical analysis outside of ROOT, RooFit, RooStats framework. This tutorial will introduce users (who are generally familiar with hypothesis testing) to the [`pyhf`][2] python package. Examples using public [HEPData][3] material will be used for asymptotic fits, brazil bands, and pull plots. By the end of the tutorial, users will understand how to find likelihoods on HEPData, how to download and perform hypothesis testing, how to patch in a signal into a background-only pdf, and how to simplify likelihoods. [1]: https://cds.cern.ch/record/1456844 [2]: https://scikit-hep.org/pyhf/ [3]: https://www.hepdata.net/
id cern-2752552
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2021
record_format invenio
spelling cern-27525522022-11-02T22:36:00Zhttp://cds.cern.ch/record/2752552engStark, Giordon HoltsbergHeinrich, Lukas AlexanderFeickert, Matthewpyhf: pure-python HistFactory -- the tutorial(Re)interpreting the results of new physics searches at the LHCLPCC Workshops<!--HTML-->The HistFactory p.d.f. template [CERN-OPEN-2012-016][1] is per-se independent of its implementation in ROOT and sometimes, it’s useful to be able to run statistical analysis outside of ROOT, RooFit, RooStats framework. This tutorial will introduce users (who are generally familiar with hypothesis testing) to the [`pyhf`][2] python package. Examples using public [HEPData][3] material will be used for asymptotic fits, brazil bands, and pull plots. By the end of the tutorial, users will understand how to find likelihoods on HEPData, how to download and perform hypothesis testing, how to patch in a signal into a background-only pdf, and how to simplify likelihoods. [1]: https://cds.cern.ch/record/1456844 [2]: https://scikit-hep.org/pyhf/ [3]: https://www.hepdata.net/oai:cds.cern.ch:27525522021
spellingShingle LPCC Workshops
Stark, Giordon Holtsberg
Heinrich, Lukas Alexander
Feickert, Matthew
pyhf: pure-python HistFactory -- the tutorial
title pyhf: pure-python HistFactory -- the tutorial
title_full pyhf: pure-python HistFactory -- the tutorial
title_fullStr pyhf: pure-python HistFactory -- the tutorial
title_full_unstemmed pyhf: pure-python HistFactory -- the tutorial
title_short pyhf: pure-python HistFactory -- the tutorial
title_sort pyhf: pure-python histfactory -- the tutorial
topic LPCC Workshops
url http://cds.cern.ch/record/2752552
work_keys_str_mv AT starkgiordonholtsberg pyhfpurepythonhistfactorythetutorial
AT heinrichlukasalexander pyhfpurepythonhistfactorythetutorial
AT feickertmatthew pyhfpurepythonhistfactorythetutorial
AT starkgiordonholtsberg reinterpretingtheresultsofnewphysicssearchesatthelhc
AT heinrichlukasalexander reinterpretingtheresultsofnewphysicssearchesatthelhc
AT feickertmatthew reinterpretingtheresultsofnewphysicssearchesatthelhc

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