Reconstruction of Reconstruction of Fundamental SUSY Fundamental SUSY

Parameters at Parameters at LHC and LC 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

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OutlineOutline

Reconstruction of Fundamental SUSY Parameters at LHC and LC

1. The SPA project2. SPS1a uncertainties at LHC and LC3. Top-down approach: SPS1a mSUGRA scenario4. Bottom-up approach: SPS1a pMSSM fit5. Back to GUT scale

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1. The SPA Project1. 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.]

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1. SPA Conventions1. 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:

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1. SPA Framework1. 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

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2. Higgs Masses2. Higgs Masses

hep-ph/0212020 [Degrassi et al.] for higher order corrections

hep-ph/0406166 [Allanach et al.] for SoftSusy, SPheno and SuSpect comparison

Higgs mass theoretical uncertainties dominate, even at LHC

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2. Gaugino and Sfermions 2. Gaugino and Sfermions MassesMasses

[Gjelsten et al, Martyn et al.]Theoretical uncertainties from hep-ph/0302102 [Allanach et al.]

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2. LC Cross Sections and BR2. LC Cross Sections and BR

Only statistical uncertainties included [Martyn et al.]

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3. mSUGRA Fit Results3. mSUGRA Fit Results

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

Mass measurements only, using experimental and theoretical uncertainties

[SFitter]

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3. mSUGRA Contour Plots3. mSUGRA Contour Plots

1 contour

Slight correlations, no secondary minima Easy fit

1 contour

[SFitter]

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3. Suspect and SoftSusy 3. Suspect and SoftSusy ComparisonComparison

Fitting particle mass spectrum (SuSpect) with

SuSpect [Djouadi, Kneur]

and

SoftSusy [Allanach]

Errors compatible

Central values within 1

except for A0 - Systematic ?

[SFitter]

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3. mSUGRA Fit Summary3. 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 msms) Bottom up approach needed to cross check mSUGRA

assumptions and test a larger class of models

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4. Phenomenological MSSM 4. Phenomenological MSSM FitFit

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

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4. pMSSM Fit Results4. 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]

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4. pMSSM Fit Results4. pMSSM Fit Results

500 GeV-LC gives high precision on crucial parameters LHC necessary for any determination of M3 and squark sector

[SFitter]

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4. pMSSM Scan Plots4. pMSSM Scan Plots

We use a 24 parameter fit, some are easy to fit:

LC: no heavy squarks

no 2 dependencyLHC: low 2 dependency

[SFitter]

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4. pMSSM Scan Plots4. pMSSM Scan Plots

Others need additional observables:

Mirror solution and low 2 dependency Smooth but very low 2 dependency

[SFitter]

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4. pMSSM Pull Distributions4. 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

MEAN VALUE ~ 0.0 and RMS ~ 1.0 Uncertainties correctly estimated

[Fittino]

Pull = (Pfit-Ptrue)/PP: a fit parameter

tan A0

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4. pMSSM Parameters 4. pMSSM Parameters SensitivitySensitivity

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

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5. Evolution to GUT scale5. Evolution to GUT scale

[Porod et al.] using Fittino results

Two possibilities:

1. Top down fit of a high-scale scenario to the pMSSM parameters obtained

Ex: mSUGRA

tan m0

A0 m1/2

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5. Extrapolation to GUT scale5. Extrapolation to GUT scale

[Porod et al.] using Fittino results

2. Bottom-up approach: extrapolate pMSSM parameters to GUT using RGE

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Next stepsNext 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 m0 measured at LC into LHC analysis [Polesello et al.] improve squark right mass uncertainties