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Peter Athron

Fine Tuning In Supersymmetric Models. Peter Athron. In collaboration with. David Miller. Overview. Little Hierarchy Problem Traditional Measure Define New Measure Illustrate with SM Hierarchy Problem Application to MSSM. Little Hierarchy Problem. MSSM EWSB constraint (Tree Level) :.

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Peter Athron

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  1. Fine TuningIn Supersymmetric Models Peter Athron In collaboration with David Miller

  2. Overview • Little Hierarchy Problem • Traditional Measure • Define New Measure • Illustrate with SM Hierarchy Problem • Application to MSSM

  3. Little Hierarchy Problem MSSM EWSB constraint (Tree Level) : Sparticle mass limits ) Parameters But

  4. Traditional Measure Observable • R. Barbieri & G.F. Giudice, (1988) DefineTuning Parameter % change in from 1% change in is fine tuned

  5. Limitations of the Traditional Measure • Considers each parameter separately The fine tuning is about cancellations between parameters . A good fine tuning measure considers all parameters together. • Considers only one observable Theories may contain tunings in more than one observable • Takes infinitesimal perturbations about the point MSSM observables are complicated functions of many parameters. Many small isolated regions of parameter space may give the same value of the observable. • Implicitly assumes a uniform distribution of parameters Parameters in LGUT may be different to those in LSUSY Corresponds to choosing parameters from a different probability distribution

  6. New Measure Tuning occurs when variations in dimensionless parameters ) larger variations in dimensionless observables. Parameter space point, the volume of parameter space, the subspace of s.t. the observables Tuning is defined as:

  7. SM Revisited

  8. Fine Tuning in the MSSM • Choose a point P in the parameter space at GUT scale • Take random fluctuations about this point. • Using a modified version of Softsusy (B.C. Allanach) • Run to Electro-Weak Symmetry Breaking scale. • Predict Mz and sparticle masses • Count how often Mz (and sparticle masses) is ok • Apply fine tuning measure

  9. For example . . . MSUGRA benchmark point SPS1a:

  10. If the tuning in the MSSM is fine for flat probability distributions: • Nature is fine tuned. • EWSB by some other mechanism than the Higgs e.g. Technicolor • The Hierarchy problem is solved other new physics e.g. Little Higgs, Large Extra Dimensions • Extended Higgs sector SSM’s are favoured e.g. NMSSM, nMSSM, ESSM • The MSSM parameters are not all equally likely. What probability distribution ameliorates this tuning? Is there a GUT with this distribution?

  11. Conclusions • Fine Tuning in the SM • SUSY • Broken SUSY appears fine tuned • Little Hierarchy Problem • Hint for a GUT theory? • Current measures of tuning neglect: • Probability distribution of parameters. • Many parameter nature of fine tuning • Additional tunings in other observables • Cancellations a finite distance from point • New measure addresses these issues • Demonstrates an increase in tuning with the susy scale.

  12. Numerical Approximation Where is the number of points in space

  13. Hierarchy Problem • physical mass = “bare mass” + “loops” = + + divergent • Cut off integral at Planck Scale Fine tuning

  14. Supersymmetry • The only possible extension to space-time • Unifies gauge couplings • Provides Dark Matter candidates • Baryogenesis in the early universe • Essential ingredient for M-Theory • Elegant solution to the Hierarchy Problem!

  15. Bosonic degrees of freedom = Fermionic degrees of freedom. )Two scalar superpartners for each fermion In Susy Quadratic divergences cancelled! No Fine Tuning?

  16. Exact Susy Softly Broken Susy LEP search fruitless!! Lower bounds on sparticles Fine Tuning reintroduced? MSSM At Tree Level: Sparticle mass limits ) Parameters But Little Hierarchy Problem

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