Publishing statistical models: Getting the most out of particle physics experiments
The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the techni...
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| creator | Cranmer, Kyle Kraml, Sabine Prosper, Harrison B Bechtle, Philip Bernlochner, Florian U Bloch, Itay M Canonero, Enzo Chrzaszcz, Marcin Coccaro, Andrea Conrad, Jan Cowan, Glen Feickert, Matthew Nahuel Ferreiro Iachellini Fowlie, Andrew Heinrich, Lukas Held, Alexander Kuhr, Thomas Kvellestad, Anders Madigan, Maeve Mahmoudi, Farvah Knut Dundas Morå Neubauer, Mark S Pierini, Maurizio Rojo, Juan Sekmen, Sezen Silvestrini, Luca Sanz, Veronica Stark, Giordon Torre, Riccardo Thorne, Robert Waltenberger, Wolfgang Wardle, Nicholas Wittbrodt, Jonas |
| description | The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases -- including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits -- we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results. |
| doi_str_mv | 10.48550/arxiv.2109.04981 |
| format | Article |
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In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases -- including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits -- we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2109.04981</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Dark matter ; Distribution functions ; Field theory ; Flavor (particle physics) ; Higgs bosons ; Particle physics ; Partons ; Physics ; Physics - High Energy Physics - Experiment ; Physics - High Energy Physics - Phenomenology ; Standard model (particle physics) ; Statistical models</subject><ispartof>arXiv.org, 2021-09</ispartof><rights>2021. 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| source | arXiv.org; Free E- Journals |
| subjects | Dark matter Distribution functions Field theory Flavor (particle physics) Higgs bosons Particle physics Partons Physics Physics - High Energy Physics - Experiment Physics - High Energy Physics - Phenomenology Standard model (particle physics) Statistical models |
| title | Publishing statistical models: Getting the most out of particle physics experiments |
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