The Bayes factor surface for searches for new physics

Abstract

The Bayes factor surface is a new way to present results from experimental searches for new physics. Searches are regularly expressed in terms of phenomenological parameters - such as the mass and cross-section of a weakly interacting massive particle. Bayes factor surfaces indicate the strength of evidence for or against models relative to the background only model in terms of the phenomenological parameters that they predict. They provide a clear and direct measure of evidence, may be easily reinterpreted, but do not depend on choices of prior or parameterization. We demonstrate the Bayes factor surface with examples from dark matter, cosmology, and collider physics.

matches published version. extended discussion on computational. 17 pages, 5 figures

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• The NANOGrav 15-year Data Set: Search for Signals from New Physics
• First Dark Matter Search Results from the LUX-ZEPLIN (LZ) Experiment
• A portrait of the Higgs boson by the CMS experiment ten years after the discovery
• Nested sampling for physical scientists
• Bayes factor functions for reporting outcomes of hypothesis tests
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• Search for physics beyond the standard model in top quark production with additional leptons in the context of effective field theory

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• Deciphering compressed electroweakino excesses with MadAnalysis 5