Neutralino dark matter detection Neutralino dark matter detection around the corner?around the corner?

Status and prospects Status and prospects in the Constrained MSSMin the Constrained MSSM

Roberto TrottaOxford Astrophysics & Royal Astronomical Society

With Roberto Ruiz de Austri (Madrid) & Leszek Roszkowsky (Sheffield)

Fritz ZwickyAndromeda (M31)

Sir Isaac Newton

The dark side of the Universe

Newton

Observations

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What is the Universe made of?What is the Universe made of?

Dark energy70%

Dark matter25%

Stars,planets,human beings,...

Gas

Neutral, stable, cold ΩCDMh2 = 0.103 ± 0.009 (WMAP3)

A well motivated candidate:Lightest Supersymmetric Particle

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SUSY frameworkSUSY framework

Lightest SUSY particle: the neutralinoχ0 = lightest mass eigenstate of

quarks squarks

R = +1 R = -1

leptons sleptons

gauge bosons gauginosHiggs higgsinos

χ0 Forbiddenby R-parity

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WIMP relic densityWIMP relic density

Ωχ h2 ∼ 1/<σv>

(1) Thermal equilibrium(2) Pair annhililation(3) Γ << H : freee out

Relic abundance

Mass mχ ≈ 0.4 m1/2∼ 0.1 – 1 TeV

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The Constrained MSSMThe Constrained MSSM

General MSSM scenario: soft SUSY breaking> 120 free parameters in the Lagrangian

Assuming Universal boundary conditions at MGUT

Gaugino masses: M1 = M2 = M3 = m1/2

Scalar masses: mHd

2 = mHu2 = ML

2 = MR2= MQ

2 = MD2= MU

2 =m02

Trilinear couplings Au = Ad = Al = A0

Higgs vev ratio tanβ = vu/vd

A 4 parameters benchmark scenario(m1/2, m0, A0, tanβ)

2D slices of CMSSM parameter space2D slices of CMSSM parameter space

(Nihei, Roszkowski, Ruiz de Austri 2002)

But this is only for fixed A0, tanβ, mt, mb,....

mmbb = 4.0 GeV m= 4.0 GeV mbb = 4.5 GeV= 4.5 GeV

Uncertainty in SM parameters cannot be neglected (Roszkowski, Ruiz de Austri, Nihei 2001)

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Bayesian parameter estimationBayesian parameter estimation

Bayes’ Theorem

prior

posterior

likelihood

θPr

obab

ility

den

sity

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Bayes + Monte Carlo Markov ChainBayes + Monte Carlo Markov Chain

MCMC: a procedure to draw samples from the posterior pdf

MCMC Bayesian Frequentist

Efficiency ∝ N ∝ kN

Nuisance params YES undefined

Marginalization trivial close to impossible

Derived params YES need estimator

Prior information YES undefined

Model comparison YES significance tests only

Theoretical uncert’ies YES only simplistic

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A full 8D Bayesian analysisA full 8D Bayesian analysis

CMSSM parameters

Nuisance (SM) parameters

Collider observablesSUSY mass limits (LEPII), Higgs limits, BR’s, g-2, EW observables

Cosmological CDM abundance(WMAP1 + others)

Roberto Ruiz de Austri (Madrid), RTLeszek Roszkowski (Sheffield) JHEP 05 (2006) 002, hep-ph/0602028

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Direct DM detection in the CMSSMDirect DM detection in the CMSSM

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Direct searches: present & futureDirect searches: present & future

Courtesy Hans Kraus

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Direct DM detection in the CMSSMDirect DM detection in the CMSSM

1 event/kg/yre.g. EDELWEISS IICRESST II

1 event/ton/yrEURECA

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Sensitivity to assumptionsSensitivity to assumptions

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DD

Complementarity with LHCComplementarity with LHC

Ruiz, Trotta, Roszkowski (2006)

LHC

LHC

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SUSY @ LHC?SUSY @ LHC?Pr

obab

ility

den

sity

4 TeV prior2 TeV prior 4 TeV prior, no g-2

Inte

gra

ted p

robab

ility Eg,

gluino mass< 2.7 TeVwith78% prob.

ROBUST

Sleptons, squarks: prior and g-2 dependent

Probab

ility

den

sity

Inte

gra

ted p

robab

ility

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Final remarksFinal remarksBAYESIAN FRAMEWORK + MCMC

Powerful, flexible tool for statistical inferenceHandles effortlessly marginalization, nuisance & derived params, theoretical errors,...

CONSTRAINED MSSM LSP well motivated DM candidateHighly predictive frameworkCareful treatment of uncertainties necessary

OBSERVATIONAL PROSPECTS CMSSM neutralino dark matter: direct detection possible by the end of the decadeDM search complementary to collider SUSY searches Null result: easy to accomodate in the general MSSM, but...

“Squarks, sleptons, dark matter, dark energy.All those damn particles you can’t see.

That’s what drove me to drink. But now I can see them!”