Litcius/Paper detail

Publishing statistical models: Getting the most out of particle physics experiments

K. Cranmer, Sabine Kraml, H. Prosper, Philip Bechtle, F. U. Bernlochner, Itay M. Bloch, Enzo Canonero, M. Chrząszcz, A. Coccaro, J. M. Conrad, G. Cowan, M. Feickert, N. Ferreiro, Andrew Fowlie, L. Heinrich, A. Held, T. Kuhr, Anders Kvellestad, Maeve Madigan, F. Mahmoudi, K. Morå, M. S. Neubauer, M. Pierini, Juan Rojo, S. Sekmen, L. Silvestrini, Verónica Sanz, G. H. Stark, Riccardo Torre, R. S. Thorne, W. Waltenberger, N. Wardle, Jonas Wittbrodt

2022SciPost Physics50 citationsDOIOpen Access PDF

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.

Topics & Concepts

Particle physicsHiggs bosonPartonPhysics beyond the Standard ModelPhysicsStatistical physicsField (mathematics)Data scienceComputer scienceMathematicsQuantum chromodynamicsPure mathematicsParticle physics theoretical and experimental studiesComputational Physics and Python ApplicationsHigh-Energy Particle Collisions Research