Simple and statistically sound recommendations for analysing physical theories

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

doi:10.1088/1361-6633/ac60ac

Title: Simple and statistically sound recommendations for analysing physical theories

Journal Article · 28 April 2022 · Reports on Progress in Physics

DOI: https://doi.org/10.1088/1361-6633/ac60ac · OSTI ID:1905293

AbdusSalam, Shehu S. ^[1]; Agocs, Fruzsina J.; Allanach, Benjamin C.;

;

; Bagnaschi, Emanuele; Bechtle, Philip; Buchmueller, Oliver; Beniwal, Ankit; Bhom, Jihyun; Bloor, Sanjay; Bringmann, Torsten; Buckley, Andy; Butter, Anja; Camargo-Molina, José Eliel; Chrzaszcz, Marcin; Conrad, Jan; Cornell, Jonathan M.; Danninger, Matthias; de Blas, Jorge more »

University of Minnesota

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. Furthermore, our examples can be reproduced with the code publicly available at Zenodo.

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Research Organization:: Univ. of Minnesota, Minneapolis, MN (United States)

Sponsoring Organization:: USDOE

Contributing Organization:: The GAMBIT Community; The MasterCode Collaboration; The Fittino Collaboration; HEPfit; BayesFits Group

Grant/Contract Number:: SC0011842

OSTI ID:: 1905293

Journal Information:: Reports on Progress in Physics, Journal Name: Reports on Progress in Physics Journal Issue: 5 Vol. 85; ISSN 0034-4885

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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