Should we still believe in constrained supersymmetry?
Csaba Bal\'azs, Andy Buckley, Daniel Carter, Benjamin Farmer, Martin, White

TL;DR
This paper uses Bayesian analysis to evaluate how recent experimental results from LEP and LHC have significantly decreased confidence in the constrained minimal supersymmetric standard model (CMSSM).
Contribution
It provides a quantitative Bayesian assessment of the impact of multiple collider and dark matter experiments on the credibility of the CMSSM.
Findings
LEP and LHC results reduce belief in CMSSM by about two orders of magnitude.
LEP and LHC Higgs searches induce strong Occam factors.
Experimental data significantly challenge the viability of CMSSM.
Abstract
We calculate Bayes factors to quantify how the feasibility of the constrained minimal supersymmetric standard model (CMSSM) has changed in the light of a series of observations. This is done in the Bayesian spirit where probability reflects a degree of belief in a proposition and Bayes' theorem tells us how to update it after acquiring new information. Our experimental baseline is the approximate knowledge that was available before LEP, and our comparison model is the Standard Model with a simple dark matter candidate. To quantify the amount by which experiments have altered our relative belief in the CMSSM since the baseline data we compute the Bayes factors that arise from learning in sequence the LEP Higgs constraints, the XENON100 dark matter constraints, the 2011 LHC supersymmetry search results, and the early 2012 LHC Higgs search results. We find that LEP and the LHC strongly…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParticle physics theoretical and experimental studies · Computational Physics and Python Applications · Particle Detector Development and Performance
