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SUMMARY:Bayesian approach and Naturalness in MSSM forecast for the LHC - M
 aria Cabrera (Universidad Autónoma de Madrid)
DTSTART:20090703T150000Z
DTEND:20090703T160000Z
UID:TALK18817@talks.cam.ac.uk
CONTACT:Steve Chun Hay Kom
DESCRIPTION:The start of LHC has motivated an effort to determine the rela
 tive probability of the different regions of the MSSM parameter space\, ta
 king into account the present\, theoretical and experimental\, wisdom abou
 t the model. Since the present experimental data are not powerful enough t
 o select a small region of the MSSM parameter space\, the choice of a judi
 cious prior probability for the parameters becomes most relevant.  Previou
 s studies have proposed theoretical priors that incorporate some (conventi
 onal) measure of the fine-tuning\, to penalize unnatural possibilities. Ho
 wever\, we show that such penalization arises from the Bayesian analysis i
 tself (with no ad hoc assumptions) when the experimental value of Mz is co
 nsidered. This allows to scan the whole parameter space\, still the low-en
 ergy region is statistically favoured (even before including dark matter o
 r g-2 constraints). The result are also remarkable stable when using flat 
 or logaritmic priors\, this does not mean that the experimental result are
  able to select a definite region of the parameter space. Rather\, it is t
 he statistical weight of the low-energy region what makes this effect. The
 n we incorporate all the important experimental constrains to the analysis
 . [Work in progress]
LOCATION:MR14\, CMS
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