University of Cambridge > Talks.cam > HEP phenomenology joint Cavendish-DAMTP seminar > Bayesian approach and Naturalness in MSSM forecast for the LHC

Bayesian approach and Naturalness in MSSM forecast for the LHC

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  • UserMaria Cabrera (Universidad Autónoma de Madrid)
  • ClockFriday 03 July 2009, 16:00-17:00
  • HouseMR14, CMS.

If you have a question about this talk, please contact Steve Chun Hay Kom.

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The start of LHC has motivated an effort to determine the relative probability of the different regions of the MSSM parameter space, taking into account the present, theoretical and experimental, wisdom about the model. Since the present experimental data are not powerful enough to select a small region of the MSSM parameter space, the choice of a judicious prior probability for the parameters becomes most relevant. Previous studies have proposed theoretical priors that incorporate some (conventional) measure of the fine-tuning, to penalize unnatural possibilities. However, we show that such penalization arises from the Bayesian analysis itself (with no ad hoc assumptions) when the experimental value of Mz is considered. This allows to scan the whole parameter space, still the low-energy region is statistically favoured (even before including dark matter or 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 the statistical weight of the low-energy region what makes this effect. Then we incorporate all the important experimental constrains to the analysis. [Work in progress]

This talk is part of the HEP phenomenology joint Cavendish-DAMTP seminar series.

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