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.

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

References in corpus (15)

• Supersymmetric Dark Matter
• Cosmological parameters from CMB and other data: a Monte-Carlo approach
• A Supersymmetry Primer
• 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
• Status of the scalar singlet dark matter model
• Nested sampling for physical scientists
• Combined collider constraints on neutralinos and charginos
• DarkBit: A GAMBIT module for computing dark matter observables and likelihoods
• Search for direct production of electroweakinos in final states with one lepton, missing transverse momentum and a Higgs boson decaying into two $b$-jets in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Bayes factor functions for reporting outcomes of hypothesis tests
• Assessing Bayes factor surfaces using interactive visualization and computer surrogate modeling
• Evidential Calibration of Confidence Intervals
• 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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• Precise interpretations of traditional fine-tuning measures
• stanhf: HistFactory models in the probabilistic programming language Stan
• Statistical Indications of Toponium Formation in Top Quark Pair Production
• Inferring correlated distributions: boosted top jets
• Deciphering compressed electroweakino excesses with MadAnalysis 5