These samples were obtained by the KISMET method (Killer Inference in Susy METeorology) described in our paper
arXiv:0705.0487.
If you use them, please cite that paper. You might also like to look at the preceeding papers on the same topic: [1 | 2 | 3].
The SLHA files come from independent samplings of the MCMC samples that they are listed with.
[ Tarred gzipped files | root file | SLHA format files ]:
When you untar the file (via tar -xvzf), you should end up with 10 independent files. Each one is a statistically independent sampling of the posterior.
The density of points in parameter space is proportional to the posterior probability of the point. The fits are described in the paper, and have had the dark matter, (g-2)_mu, B_s->mu mu, MW, sin^2 theta_l, mt, mb, alphas, BR(b->s gamma) and sparticle/Higgs direct search limits applied to them.
Each point in the sample is a line of the file, organised into a list of numbers, in the following order:
- The weight of the point - you *must* take this into account if you're doing Bayesian stats.
- An integer specifying the number of parameters to come
- m0/GeV
- m12/GeV
- A0/GeV
- tan beta
- mb(mb) MSbar/GeV
- mt/GeV
- alpha_s(MZ) MSbar/GeV
- alpha(MZ) MSbar
- sign mu
- m_gluino/GeV
- m_chargino1/GeV
- mstau_1/GeV
- mselectron_R/GeV
- msquark_L/GeV
- mstop_1/GeV
- msbottom_1/GeV
- msneutrino_1/GeV
- mh^0/GeV
- mA/GeV
- omega_{DM} h^2
- (g-2)_mu
- BR(b->s gamma)
- m_neutralino_1/GeV
- BR(B_s->mu mu)
- m_neutralino_2/GeV
- sin^2 theta_l^(eff)
- MW/GeV
- irrelevant
- irrelevant
- irrelevant
- irrelevant
- sigma(LHC QCD sparticle production)/pb
- sigma(LHC gaugino production)/pb
- sigma(LHC slepton production)/pb
- mu parameter/GeV
- m3squared parameter/GeV^2
- ln(posterior) (overall normalisation)