Simple and statistically sound recommendations for analysing physical theories

Shehu S. Abdussalam , Fruzsina J. Agocs , Benjamin C. Allanach , Peter Athron , Csaba Balázs , Emanuele Bagnaschi , Philip Bechtle , Oliver Buchmueller , Ankit Beniwal , Jihyun Bhom , Sanjay Bloor , Torsten Bringmann , Andy Buckley , Anja Butter , José Eliel Camargo-Molina , Marcin Chrzaszcz , Jan Conrad , Jonathan M. Cornell , Matthias Danninger , Jorge de Blas , Albert de Roeck , Klaus Desch , Matthew Dolan , Herbert Dreiner , Otto Eberhardt , John Ellis , Ben Farmer , Marco Fedele , Henning Flächer , Andrew Fowlie , Tomás E. Gonzalo , Philip Grace , Matthias Hamer , Will Handley , Julia Harz , Sven Heinemeyer , Sebastian Hoof , Selim Hotinli , Paul Jackson , Felix Kahlhoefer , Kamila Kowalska , Michael Krämer , Anders Kvellestad , Miriam Lucio Martinez , Farvah Mahmoudi (1) , Diego Martinez Santos , Gregory D. Martinez , Satoshi Mishima , Keith Olive , Ayan Paul , Markus Tobias Prim , Werner Porod , Are Raklev , Janina J. Renk , Christopher Rogan , Leszek Roszkowski , Roberto Ruiz de Austri , Kazuki Sakurai , Andre Scaffidi , Pat Scott , Enrico Maria Sessolo , Tim Stefaniak , Patrick Stöcker , Wei Su , Sebastian Trojanowski , Roberto Trotta , Yue-Lin Sming Tsai , Jeriek van den Abeele , Mauro Valli , Aaron C. Vincent , Georg Weiglein , Martin White , Peter Wienemann , Lei Wu , Yang Zhang

1 IP2I Lyon - Institut de Physique des 2 Infinis de Lyon

Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with statistically unsound ad hoc methods, involving intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these procedures, suggesting where possible simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide any physicists lacking comprehensive statistical training with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden. Our examples can be reproduced with the code publicly available at Zenodo.

Mots clés: particle physics; statistics; methodology; cosmological model: parameter space; higher-dimensional; programming; category

Domaines: Physique des Hautes Energies - Phénoménologie [hep-ph]; Astrophysique [astro-ph]; Analyse de données, Statistiques et Probabilités [physics.data-an]

Shehu S. Abdussalam, Fruzsina J. Agocs, Benjamin C. Allanach, Peter Athron, Csaba Balázs, et al.. Simple and statistically sound recommendations for analysing physical theories. Rept.Prog.Phys., 2022, 85 (5), pp.052201. ⟨10.1088/1361-6633/ac60ac⟩. ⟨hal-03136775⟩