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Reconstruction of Fundamental SUSY Parameters at LHC and LC

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Presentation on theme: "Reconstruction of Fundamental SUSY Parameters at LHC and LC"— Presentation transcript:

1 Reconstruction of Fundamental SUSY Parameters at LHC and LC
Rémi Lafaye - CERN/ATLAS on leave from LAPP-IN2P3 On behalf of the SFitter and Fittino authors: P. Bechtle, K. Desch, R. L, T. Plehn, P. Wienemann and D. Zerwas and the members of the SPA project

2 Reconstruction of Fundamental SUSY Parameters at LHC and LC
Outline Reconstruction of Fundamental SUSY Parameters at LHC and LC The SPA project SPS1a uncertainties at LHC and LC Top-down approach: SPS1a mSUGRA scenario Bottom-up approach: SPS1a pMSSM fit Back to GUT scale 8/17/2004 R. Lafaye

3 SPA: Supersymmetry Parameter Analysis
1. The SPA Project SPA: Supersymmetry Parameter Analysis The SPA project is a joint study of theorists and experimentalists working on LHC and LC phenomenology SPA Tasks: High-precision determination of the SUSY Lagrange parameters at the electroweak scale Extrapolation to high scale to reconstruct the fundamental parameters and the mechanism for SUSY breaking Need to match expected experimental accuracy with theoretical predictions Extend the set of observables Coherent LHC/LC analysis Starting point: LHC/LC study group report [G. Weiglein et al.] 8/17/2004 R. Lafaye

4 1. SPA Conventions SUSY particle masses = pole masses
Use DR scheme and scale MSUSY = 1 TeV Except for Higgs mixing matrix: On-shell and scale = light Higgs mass Standard Model input: 8/17/2004 R. Lafaye

5 1. SPA Framework Common standard: SUSY Les Houches Accord (SLHA)
Gathering of Tools: Spectrum calculators: FeynHiggs, IsaSusy, SoftSusy, SPheno, SuSpect Observables calculation: SDecay, NMHDecay, Prospino2 Event generators: Isajet, Pythia, Whizard Cold dark matter: Micromegas, DarkSusy RGE programs Extraction of SUSY parameters: SFitter [R. L, T. Plehn, D. Zerwas] Grid scan start + MINUIT Fittino [P. Bechtle, K. Desch, P. Wienemann] Tree-level start + MINUIT Both include higher order corrections  Similar results 8/17/2004 R. Lafaye

6 Higgs mass theoretical uncertainties dominate, even at LHC
2. Higgs Masses hep-ph/ [Degrassi et al.] for higher order corrections hep-ph/ [Allanach et al.] for SoftSusy, SPheno and SuSpect comparison Higgs mass theoretical uncertainties dominate, even at LHC 8/17/2004 R. Lafaye

7 2. Gaugino and Sfermions Masses
[Gjelsten et al, Martyn et al.] Theoretical uncertainties from hep-ph/ [Allanach et al.] 8/17/2004 R. Lafaye

8 2. LC Cross Sections and BR
Only statistical uncertainties included [Martyn et al.] 8/17/2004 R. Lafaye

9 3. mSUGRA Fit Results Mass measurements only, using experimental and theoretical uncertainties [SFitter] With statistical uncertainties only: LHC  10% better, LC and LHC+LC  1 order of magnitude better LHC first to provide measurements of mSUGRA parameters LC increases precision by an order of magnitude 8/17/2004 R. Lafaye

10 3. mSUGRA Contour Plots [SFitter] 1 contour 1 contour Slight correlations, no secondary minima  Easy fit 8/17/2004 R. Lafaye

11 3. Suspect and SoftSusy Comparison
Fitting particle mass spectrum (SuSpect) with SuSpect [Djouadi, Kneur] and SoftSusy [Allanach] Errors compatible Central values within 1 except for A0 - Systematic ? [SFitter] 8/17/2004 R. Lafaye

12 3. mSUGRA Fit Summary SPS1a gluino and most of the squarks not seen at LC, only at LHC  does not worsen the parameter determination at LC mSUGRA fit to LC : m1/2 = 0.72 GeV mSUGRA fit to LHC+LC : m1/2 = 0.67 GeV Because : gaugino mass unification is an mSUGRA feature m0 dominated by slepton mass measurements over squarks (squarks contribution about 10 times lower because m0  msms) Bottom up approach needed to cross check mSUGRA assumptions and test a larger class of models 8/17/2004 R. Lafaye

13 4. Phenomenological MSSM Fit
Summary of accelerators SUSY capabilities: LHC: gluino, squarks, neutralinos and sleptons masses and couplings LC: charginos, heavy Higgs and slepton mass high precision measurements  enough information to check mSUGRA assumptions without assuming a given SUSY breaking scenario Generic MSSM has 105 (too many) free parameters. Make some assumptions: All phases = 0 No mixing between generations No mixing within first 2 generations pMSSM  24 parameters 8/17/2004 R. Lafaye

14 4. pMSSM Fit Results Mass measurements only
Include theoretical uncertainties 500 GeV-LC gives high precision in the slepton sector Only LHC can scan the squark sector apart for stop right [SFitter] 8/17/2004 R. Lafaye

15 4. pMSSM Fit Results [SFitter] 500 GeV-LC gives high precision on crucial parameters LHC necessary for any determination of M3 and squark sector 8/17/2004 R. Lafaye

16 4. pMSSM Scan Plots We use a 24 parameter fit, some are easy to fit:
[SFitter] LC: no heavy squarks  no 2 dependency LHC: low 2 dependency 8/17/2004 R. Lafaye

17 4. pMSSM Scan Plots Others need additional observables: [SFitter]
Mirror solution and low 2 dependency Smooth but very low 2 dependency 8/17/2004 R. Lafaye

18 4. pMSSM Pull Distributions
120 independent fits - smeared observables Experimental errors only except for mh Start values: from tree-level formulae Use mass and cross section measurements mt included in the fit Pull = (Pfit-Ptrue)/P P: a fit parameter [Fittino] tan A0 MEAN VALUE ~ 0.0 and RMS ~ 1.0  Uncertainties correctly estimated 8/17/2004 R. Lafaye

19 4. pMSSM Parameters Sensitivity
Vary parameter by 1 and determine individual 2 contribution of the various observables using Fittino Only a combined LHC and LC study allows a complete fit without fixing any parameter 8/17/2004 R. Lafaye

20 5. Evolution to GUT scale Two possibilities:
1. Top down fit of a high-scale scenario to the pMSSM parameters obtained Ex: mSUGRA tan m0 A0 m1/2 [Porod et al.] using Fittino results 8/17/2004 R. Lafaye

21 5. Extrapolation to GUT scale
2. Bottom-up approach: extrapolate pMSSM parameters to GUT using RGE [Porod et al.] using Fittino results 8/17/2004 R. Lafaye

22 Next steps Study SPS1a’ = New mSUGRA point defined by SPA
 SPS1a like + compatibility with CDM measurements To be included in the fitting tools: Dark-matter observables New fitting techniques (genetic algorithms) Propagate m0 measured at LC into LHC analysis [Polesello et al.]  improve squark right mass uncertainties 8/17/2004 R. Lafaye

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