Publishing statistical models: Getting the most out of particle physics experiments
Jan 1, 2022·
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1 min read
Kyle Cranmer
Sabine Kraml
Harrison B. Prosper
Philip Bechtle
Florian U. Bernlochner
Itay M. Bloch
Enzo Canonero
Marcin Chrzaszcz
Andrea Coccaro
Jan Conrad
Glen Cowan
Matthew Feickert
Nahuel Ferreiro Iachellini
Andrew Fowlie
Lukas Heinrich
Alexander Held
Thomas Kuhr
Anders Kvellestad
Maeve Madigan
Farvah Mahmoudi
Knut Dundas Mora
Mark Neubauer
Maurizio Pierini
Juan Rojo
Sezen Sekmen
Luca Silvestrini
Veronica Sanz
Giordon Stark
Riccardo Torre
Robert Thorne
Wolfgang Waltenberger
Nicholas Wardle
Jonas Wittbrodt
Abstract
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.
Type
Publication
SciPost Phys.
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University of Illinois at Urbana-Champaign
I am a professor at the University of Illinois. My research is highly interdisciplinary at the intersection of particle physics, AI/ML, and quantum, aiming to understand the universe at its fundamental level and to accelerate scientific discovery through innovation.
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