Physics – High Energy Physics – High Energy Physics - Phenomenology
Scientific paper
2010-11-18
JCAP 1107:002,2011
Physics
High Energy Physics
High Energy Physics - Phenomenology
30 pages, 5 figures; v2 includes major updates in response to referee's comments; extra scans and tables added, discussion exp
Scientific paper
10.1088/1475-7516/2011/07/002
Models of weak-scale supersymmetry offer viable dark matter (DM) candidates. Their parameter spaces are however rather large and complex, such that pinning down the actual parameter values from experimental data can depend strongly on the employed statistical framework and scanning algorithm. In frequentist parameter estimation, a central requirement for properly constructed confidence intervals is that they cover true parameter values, preferably at exactly the stated confidence level when experiments are repeated infinitely many times. Since most widely-used scanning techniques are optimised for Bayesian statistics, one needs to assess their abilities in providing correct confidence intervals in terms of the statistical coverage. Here we investigate this for the Constrained Minimal Supersymmetric Standard Model (CMSSM) when only constrained by data from direct searches for dark matter. We construct confidence intervals from one-dimensional profile likelihoods and study the coverage by generating several pseudo-experiments for a few benchmark sets of pseudo-true parameters. We use nested sampling to scan the parameter space and evaluate the coverage for the benchmarks when either flat or logarithmic priors are imposed on gaugino and scalar mass parameters. The sampling algorithm has been used in the configuration usually adopted for exploration of the Bayesian posterior. We observe both under- and over-coverage, which in some cases vary quite dramatically when benchmarks or priors are modified. We show how most of the variation can be explained as the impact of explicit priors as well as sampling effects, where the latter are indirectly imposed by physicality conditions. For comparison, we also evaluate the coverage for Bayesian credible intervals, and observe significant under-coverage in those cases.
Akrami Yashar
Conrad Jan
Edsjo Joakim
Savage Christopher
Scott Pat
No associations
LandOfFree
Statistical coverage for supersymmetric parameter estimation: a case study with direct detection of dark matter does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Statistical coverage for supersymmetric parameter estimation: a case study with direct detection of dark matter, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Statistical coverage for supersymmetric parameter estimation: a case study with direct detection of dark matter will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-116451