Constrained MSSM (CMSSM) Kane, Kolda, LR, Wells (1993) ...“benchmark framework” for the LHC (...e.g., mSUGRA) At M GUT ≃ 2 × 10 16 GeV : M 1 = M 2 = m e g = m 1 / 2 gauginos scalars m 2 q i = m 2 l i = m 2 H b = m 2 H t = m 2 0 e e 3–linear soft terms A b = A t = A 0 radiative EWSB Ht tan 2 β m 2 Hb − m 2 − m 2 µ 2 = Z tan 2 β − 1 2 4+1 independent parameters: m 1 / 2 , m 0 , A 0 , tan β, sgn( µ ) well developed machinery to compute some useful mass relations: masses and couplings bino: m χ ≃ 0 . 4 m 1 / 2 neutralino χ mostly bino gluino e g : m e g ≃ 2 . 7 m 1 / 2 q 0 . 15 m 2 1 / 2 + m 2 τ 1 ≃ supersymmetric tau (stau) e τ 1 : m e 0 L. Roszkowski, GGI, May 2010 – p.9
Earlier analyses of CMSSM very many papers L. Roszkowski, GGI, May 2010 – p.10
Earlier analyses of CMSSM very many papers Until recently usual approach has been to: do fixed-grid scans of m 1 / 2 and m 0 for fixed tan β and A 0 apply constraints from LEP , BR( ¯ B → X s γ ) , Ω χ h 2 , EWSB, charged LSP , etc impose rigid (in/out) 1 σ or 2 σ ranges L. Roszkowski, GGI, May 2010 – p.10
Earlier analyses of CMSSM very many papers Until recently usual approach has been to: do fixed-grid scans of m 1 / 2 and m 0 for fixed tan β and A 0 hep-ph/0404052 apply constraints from LEP , BR( ¯ B → X s γ ) , Ω χ h 2 , EWSB, charged LSP , etc impose rigid (in/out) 1 σ or 2 σ ranges obtain narrow “allowed” regions L. Roszkowski, GGI, May 2010 – p.10
Earlier analyses of CMSSM very many papers Until recently usual approach has been to: do fixed-grid scans of m 1 / 2 and m 0 for fixed tan β and A 0 hep-ph/0404052 apply constraints from LEP , BR( ¯ B → X s γ ) , Ω χ h 2 , EWSB, charged LSP , etc impose rigid (in/out) 1 σ or 2 σ ranges obtain narrow “allowed” regions Shortcomings: hard to compare relative impact of various constraints hard to include TH + residual SM errors, etc. full scan of PS not feasible impossible to assess relative impact of var- ious constraints L. Roszkowski, GGI, May 2010 – p.10
Earlier analyses of CMSSM very many papers Until recently usual approach has been to: do fixed-grid scans of m 1 / 2 and m 0 for fixed tan β and A 0 hep-ph/0404052 apply constraints from LEP , BR( ¯ B → X s γ ) , Ω χ h 2 , EWSB, charged LSP , etc impose rigid (in/out) 1 σ or 2 σ ranges obtain narrow “allowed” regions Shortcomings: hard to compare relative impact of various constraints hard to include TH + residual SM errors, etc. full scan of PS not feasible impossible to assess relative impact of var- ious constraints results in over-simplified predictions L. Roszkowski, GGI, May 2010 – p.10
Bayesian Analysis of the CMSSM Apply to the CMSSM: recent development, led by 2 groups L. Roszkowski, GGI, May 2010 – p.11
Bayesian Analysis of the CMSSM Apply to the CMSSM: recent development, led by 2 groups m = ( θ, ψ ) – model’s all relevant parameters L. Roszkowski, GGI, May 2010 – p.11
Bayesian Analysis of the CMSSM Apply to the CMSSM: recent development, led by 2 groups m = ( θ, ψ ) – model’s all relevant parameters CMSSM parameters θ = m 1 / 2 , m 0 , A 0 , tan β relevant SM param’s ψ = M t , m b ( m b ) MS , α MS , α em ( M Z ) MS s L. Roszkowski, GGI, May 2010 – p.11
Bayesian Analysis of the CMSSM Apply to the CMSSM: recent development, led by 2 groups m = ( θ, ψ ) – model’s all relevant parameters CMSSM parameters θ = m 1 / 2 , m 0 , A 0 , tan β relevant SM param’s ψ = M t , m b ( m b ) MS , α MS , α em ( M Z ) MS s ξ = ( ξ 1 , ξ 2 , . . . , ξ m ) : set of derived variables (observables): ξ ( m ) L. Roszkowski, GGI, May 2010 – p.11
Bayesian Analysis of the CMSSM Apply to the CMSSM: recent development, led by 2 groups m = ( θ, ψ ) – model’s all relevant parameters CMSSM parameters θ = m 1 / 2 , m 0 , A 0 , tan β relevant SM param’s ψ = M t , m b ( m b ) MS , α MS , α em ( M Z ) MS s ξ = ( ξ 1 , ξ 2 , . . . , ξ m ) : set of derived variables (observables): ξ ( m ) d : data ( Ω CDM h 2 , b → sγ , m h , etc) L. Roszkowski, GGI, May 2010 – p.11
Bayesian Analysis of the CMSSM Apply to the CMSSM: recent development, led by 2 groups m = ( θ, ψ ) – model’s all relevant parameters CMSSM parameters θ = m 1 / 2 , m 0 , A 0 , tan β relevant SM param’s ψ = M t , m b ( m b ) MS , α MS , α em ( M Z ) MS s ξ = ( ξ 1 , ξ 2 , . . . , ξ m ) : set of derived variables (observables): ξ ( m ) d : data ( Ω CDM h 2 , b → sγ , m h , etc) Bayes’ theorem: posterior pdf p ( θ, ψ | d ) = p ( d | ξ ) π ( θ,ψ ) p ( d ) p ( d | ξ ) = L : likelihood π ( θ, ψ ) : prior pdf likelihood × prior posterior = normalization factor p ( d ) : evidence (normalization factor) L. Roszkowski, GGI, May 2010 – p.11
Bayesian Analysis of the CMSSM Apply to the CMSSM: recent development, led by 2 groups m = ( θ, ψ ) – model’s all relevant parameters CMSSM parameters θ = m 1 / 2 , m 0 , A 0 , tan β relevant SM param’s ψ = M t , m b ( m b ) MS , α MS , α em ( M Z ) MS s ξ = ( ξ 1 , ξ 2 , . . . , ξ m ) : set of derived variables (observables): ξ ( m ) d : data ( Ω CDM h 2 , b → sγ , m h , etc) Bayes’ theorem: posterior pdf p ( θ, ψ | d ) = p ( d | ξ ) π ( θ,ψ ) p ( d ) p ( d | ξ ) = L : likelihood π ( θ, ψ ) : prior pdf likelihood × prior posterior = normalization factor p ( d ) : evidence (normalization factor) usually marginalize over SM (nuisance) parameters ψ ⇒ p ( θ | d ) L. Roszkowski, GGI, May 2010 – p.11
Impact of varying SM parameters L. Roszkowski, GGI, May 2010 – p.12
Impact of varying SM parameters fix tan β , A 0 + all SM param’s 4000 3500 3000 m 0 (GeV) 2500 2000 1500 tan β =50 1000 A 0 =0 500 500 1000 1500 2000 m 1/2 (GeV) Relative probability density 0 0.2 0.4 0.6 0.8 1 L. Roszkowski, GGI, May 2010 – p.12
Impact of varying SM parameters vary M t fix tan β , A 0 + all SM param’s 4000 4000 3500 3500 3000 3000 m 0 (GeV) m 0 (GeV) 2500 2500 2000 2000 1500 1500 M t varied tan β =50 1000 1000 tan β =50 A 0 =0 A 0 =0 500 500 500 1000 1500 2000 500 1000 1500 2000 m 1/2 (GeV) m 1/2 (GeV) Relative probability density Relative probability density 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 L. Roszkowski, GGI, May 2010 – p.12
Impact of varying SM parameters vary α s fix tan β , A 0 + all SM param’s 4000 4000 3500 3500 3000 3000 m 0 (GeV) m 0 (GeV) 2500 2500 2000 2000 α s varied 1500 1500 tan β =50 tan β =50 1000 1000 A 0 =0 A 0 =0 500 500 500 1000 1500 2000 500 1000 1500 2000 m 1/2 (GeV) m 1/2 (GeV) Relative probability density Relative probability density 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 L. Roszkowski, GGI, May 2010 – p.12
Impact of varying SM parameters vary α s fix tan β , A 0 + all SM param’s 4000 4000 3500 3500 3000 3000 m 0 (GeV) m 0 (GeV) 2500 2500 2000 2000 α s varied 1500 1500 tan β =50 tan β =50 1000 1000 A 0 =0 A 0 =0 500 500 500 1000 1500 2000 500 1000 1500 2000 m 1/2 (GeV) m 1/2 (GeV) Relative probability density Relative probability density 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 residual errors in SM parameters ⇒ strong impact on favoured SUSY ranges effect of varying A 0 , tan β also substantial L. Roszkowski, GGI, May 2010 – p.12
Bayesian Analysis of the CMSSM L. Roszkowski, GGI, May 2010 – p.13
Bayesian Analysis of the CMSSM θ = ( m 0 , m 1 / 2 , A 0 , tan β ) : CMSSM parameters ψ = ( M t , m b ( m b ) M S , α em ( M Z ) M S , α M S ) : SM (nuisance) parameters s L. Roszkowski, GGI, May 2010 – p.13
Bayesian Analysis of the CMSSM θ = ( m 0 , m 1 / 2 , A 0 , tan β ) : CMSSM parameters ψ = ( M t , m b ( m b ) M S , α em ( M Z ) M S , α M S ) : SM (nuisance) parameters s priors – assume flat distributions and ranges as: CMSSM parameters θ 50 GeV < m 0 < 4 TeV 50 GeV < m 1 / 2 < 4 TeV | A 0 | < 7 TeV 2 < tan β < 62 flat priors: SM (nuisance) parameters ψ 160 GeV < M t < 190 GeV 4 GeV < m b ( m b ) M S < 5 GeV 0 . 10 < α M S < 0 . 13 s 127 . 5 < 1 /α em ( M Z ) M S < 128 . 5 L. Roszkowski, GGI, May 2010 – p.13
Bayesian Analysis of the CMSSM θ = ( m 0 , m 1 / 2 , A 0 , tan β ) : CMSSM parameters ψ = ( M t , m b ( m b ) M S , α em ( M Z ) M S , α M S ) : SM (nuisance) parameters s priors – assume flat distributions and ranges as: CMSSM parameters θ 50 GeV < m 0 < 4 TeV 50 GeV < m 1 / 2 < 4 TeV | A 0 | < 7 TeV 2 < tan β < 62 flat priors: SM (nuisance) parameters ψ 160 GeV < M t < 190 GeV 4 GeV < m b ( m b ) M S < 5 GeV 0 . 10 < α M S < 0 . 13 s 127 . 5 < 1 /α em ( M Z ) M S < 128 . 5 vary all 8 (CMSSM+SM) parameters simultaneously, apply MCMC include all relevant theoretical and experimental errors L. Roszkowski, GGI, May 2010 – p.13
Experimental Measurements (assume Gaussian distributions) L. Roszkowski, GGI, May 2010 – p.14
Experimental Measurements (assume Gaussian distributions) SM (nuisance) parameter Mean Error µ σ (expt) M t 172.6 GeV 1.4 GeV m b ( m b ) M S 4.20 GeV 0.07 GeV α s 0.1176 0.0020 1 /α em ( M Z ) 127.955 0.030 L. Roszkowski, GGI, May 2010 – p.14
Experimental Measurements (assume Gaussian distributions) SM (nuisance) parameter Mean Error µ σ (expt) new BR(¯ B → X s γ ) × 10 4 : M t 172.6 GeV 1.4 GeV SM: 3 . 15 ± 0 . 23 (Misiak & m b ( m b ) M S 4.20 GeV 0.07 GeV Steinhauser, Sept 06) used here α s 0.1176 0.0020 1 /α em ( M Z ) 127.955 0.030 L. Roszkowski, GGI, May 2010 – p.14
Experimental Measurements (assume Gaussian distributions) SM (nuisance) parameter Mean Error µ σ (expt) new BR(¯ B → X s γ ) × 10 4 : M t 172.6 GeV 1.4 GeV SM: 3 . 15 ± 0 . 23 (Misiak & m b ( m b ) M S 4.20 GeV 0.07 GeV Steinhauser, Sept 06) used here α s 0.1176 0.0020 1 /α em ( M Z ) 127.955 0.030 Derived observable Mean Errors µ σ (expt) τ (th) M W 80 . 398 GeV 25 MeV 15 MeV sin 2 θ eff 16 × 10 − 5 15 × 10 − 5 0 . 23153 δa SUSY × 10 10 29 . 5 8 . 8 1 µ BR(¯ B → X s γ ) × 10 4 3 . 55 0 . 26 0 . 21 ∆ M B s 17.33 0.12 4.8 Ω χ h 2 0 . 1 Ω χ h 2 0.1099 0.0062 take w/o error: M Z = 91 . 1876(21) GeV , G F = 1 . 16637(1) × 10 − 5 GeV − 2 L. Roszkowski, GGI, May 2010 – p.14
Experimental Limits Derived observable upper/lower Constraints limit ξ lim τ (theor.) 1 . 5 × 10 − 7 → 3 × 10 − 8 BR(B s → µ + µ − ) UL 14% m h LL 114 . 4 GeV ( 91 . 0 GeV ) 3 GeV ζ 2 h ≡ g 2 ZZh /g 2 f ( m h ) UL 3% ZZH SM m χ LL 50 GeV 5% m χ ± LL 103 . 5 GeV ( 92 . 4 GeV ) 5% 1 m ˜ LL 100 GeV ( 73 GeV ) 5% e R m ˜ 95 GeV ( 73 GeV ) LL 5% µ R m ˜ LL 87 GeV ( 73 GeV ) 5% τ 1 m ˜ LL 94 GeV ( 43 GeV ) 5% ν m ˜ LL 95 GeV ( 65 GeV ) 5% t 1 m ˜ LL 95 GeV ( 59 GeV ) 5% b 1 m ˜ 318 GeV LL 5% q m ˜ LL 233 GeV 5% g ( σ SI ∼ 100% ) UL WIMP mass dependent p Note: DM direct detection σ SI not applied due to astroph’l uncertainties (eg, local DM density) p L. Roszkowski, GGI, May 2010 – p.15
The Likelihood: 1-dim case Take a single observable ξ ( m ) that has been measured (e.g., M W ) L. Roszkowski, GGI, May 2010 – p.16
The Likelihood: 1-dim case Take a single observable ξ ( m ) that has been measured (e.g., M W ) c – central value, σ – standard exptal error L. Roszkowski, GGI, May 2010 – p.16
The Likelihood: 1-dim case Take a single observable ξ ( m ) that has been measured (e.g., M W ) c – central value, σ – standard exptal error define χ 2 = [ ξ ( m ) − c ] 2 σ 2 L. Roszkowski, GGI, May 2010 – p.16
The Likelihood: 1-dim case Take a single observable ξ ( m ) that has been measured (e.g., M W ) c – central value, σ – standard exptal error define χ 2 = [ ξ ( m ) − c ] 2 σ 2 assuming Gaussian distribution ( d → ( c, σ ) ): � � − χ 2 1 L = p ( σ, c | ξ ( m )) = 2 πσ exp √ 2 L. Roszkowski, GGI, May 2010 – p.16
The Likelihood: 1-dim case Take a single observable ξ ( m ) that has been measured (e.g., M W ) c – central value, σ – standard exptal error define χ 2 = [ ξ ( m ) − c ] 2 σ 2 assuming Gaussian distribution ( d → ( c, σ ) ): � � − χ 2 1 L = p ( σ, c | ξ ( m )) = 2 πσ exp √ 2 when include theoretical error estimate τ (assumed Gaussian): σ → s = √ σ 2 + τ 2 TH error “smears out” the EXPTAL range L. Roszkowski, GGI, May 2010 – p.16
The Likelihood: 1-dim case Take a single observable ξ ( m ) that has been measured (e.g., M W ) c – central value, σ – standard exptal error define χ 2 = [ ξ ( m ) − c ] 2 σ 2 assuming Gaussian distribution ( d → ( c, σ ) ): � � − χ 2 1 L = p ( σ, c | ξ ( m )) = 2 πσ exp √ 2 when include theoretical error estimate τ (assumed Gaussian): σ → s = √ σ 2 + τ 2 TH error “smears out” the EXPTAL range for several uncorrelated observables (assumed Gaussian): � � − � χ 2 L = exp i i 2 L. Roszkowski, GGI, May 2010 – p.16
Probability maps of the CMSSM L. Roszkowski, GGI, May 2010 – p.17
Probability maps of the CMSSM arXiv:0705.2012 Roszkowski, Ruiz & Trotta (2007) 4 MCMC scan 3.5 Bayesian analysis 3 relative probability density fn 2.5 m 0 (TeV) flat priors 2 68% total prob. – inner contours 1.5 95% total prob. – outer contours 1 0.5 CMSSM 2-dim pdf p ( m 0 , m 1 / 2 | d ) µ >0 0.5 1 1.5 2 favored: m 0 ≫ m 1 / 2 (FP region) m 1/2 (TeV) Relative probability density 0 0.2 0.4 0.6 0.8 1 L. Roszkowski, GGI, May 2010 – p.17
Probability maps of the CMSSM arXiv:0705.2012 Roszkowski, Ruiz & Trotta (2007) 4 MCMC scan 3.5 Bayesian analysis 3 relative probability density fn 2.5 m 0 (TeV) flat priors 2 68% total prob. – inner contours 1.5 95% total prob. – outer contours 1 0.5 CMSSM 2-dim pdf p ( m 0 , m 1 / 2 | d ) µ >0 0.5 1 1.5 2 favored: m 0 ≫ m 1 / 2 (FP region) m 1/2 (TeV) Relative probability density 0 0.2 0.4 0.6 0.8 1 similar study by Allanach+Lester(+Weber) see also, Ellis et al (EHOW, χ 2 approach, no MCMC, they fix SM parameters!) L. Roszkowski, GGI, May 2010 – p.17
Probability maps of the CMSSM arXiv:0705.2012 Roszkowski, Ruiz & Trotta (2007) 4 MCMC scan 3.5 Bayesian analysis 3 relative probability density fn 2.5 m 0 (TeV) flat priors 2 68% total prob. – inner contours 1.5 95% total prob. – outer contours 1 0.5 CMSSM 2-dim pdf p ( m 0 , m 1 / 2 | d ) µ >0 0.5 1 1.5 2 favored: m 0 ≫ m 1 / 2 (FP region) m 1/2 (TeV) Relative probability density 0 0.2 0.4 0.6 0.8 1 unlike others (except for A+L), we vary also SM parameters L. Roszkowski, GGI, May 2010 – p.17
SUSY: Prospects for direct detection Bayesian analysis, MCMC scan of 8 params (4 SUSY+4 SM) L. Roszkowski, GGI, May 2010 – p.18
SUSY: Prospects for direct detection Bayesian analysis, MCMC scan of 8 params (4 SUSY+4 SM) CMSSM: global scan, MCMC Roszkowski, Ruiz & Trotta (2007) −4 CMSSM, µ > 0 −5 EDELWEISS−I −6 XENON−10 SI (pb)] ZEPLIN−III CDMS−II −7 Log[ σ p −8 −9 −10 −11 0.2 0.4 0.6 0.8 1 m χ (TeV) internal (external): 68% ( 95% ) region L. Roszkowski, GGI, May 2010 – p.18
SUSY: Prospects for direct detection Bayesian analysis, MCMC scan of 8 params (4 SUSY+4 SM) CMSSM: global scan, Nested Sampling XENON -100 and CDMS-II: < 10 − 7 pb: σ SI ∼ p also Zeplin–III ⇒ already explore 68% region (large m 0 ≫ m 1 / 2 ⇒ heavy squarks) largely beyond LHC reach internal (external): 68% ( 95% ) region L. Roszkowski, GGI, May 2010 – p.18
SUSY: Prospects for direct detection Bayesian analysis, MCMC scan of 8 params (4 SUSY+4 SM) CMSSM: global scan, Nested Sampling XENON -100 and CDMS-II: < 10 − 7 pb: σ SI ∼ p also Zeplin–III ⇒ already explore 68% region (large m 0 ≫ m 1 / 2 ⇒ heavy squarks) largely beyond LHC reach Roszkowski, Ruiz & Trotta (2007) 4 3.5 3 m 0 (TeV) 2.5 2 1.5 LHC 1 0.5 CMSSM internal (external): 68% ( 95% ) region µ >0 0.5 1 1.5 2 m 1/2 (TeV) L. Roszkowski, GGI, May 2010 – p.18
SUSY: Prospects for direct detection Bayesian analysis, MCMC scan of 8 params (4 SUSY+4 SM) CMSSM: global scan, Nested Sampling XENON -100 and CDMS-II: < 10 − 7 pb: σ SI ∼ p also Zeplin–III ⇒ already explore 68% region (large m 0 ≫ m 1 / 2 ⇒ heavy squarks) largely beyond LHC reach internal (external): 68% ( 95% ) region next: ZENON-100 - sensitivity reach ∼ 10 − 9 pb ⇒ later this year future: 1 tonne detectors - sensitivity reach ∼ 10 − 10 pb ⇒ in a few years L. Roszkowski, GGI, May 2010 – p.18
SUSY: Prospects for direct detection Bayesian analysis, MCMC scan of 8 params (4 SUSY+4 SM) CMSSM: global scan, Nested Sampling XENON -100 and CDMS-II: < 10 − 7 pb: σ SI ∼ p also Zeplin–III ⇒ already explore 68% region (large m 0 ≫ m 1 / 2 ⇒ heavy squarks) largely beyond LHC reach internal (external): 68% ( 95% ) region ⇒ DD: prospects look very good L. Roszkowski, GGI, May 2010 – p.18
CMSSM: Impact of priors L. Roszkowski, GGI, May 2010 – p.19
CMSSM: Impact of priors flat in m 0 , m 1 / 2 L. Roszkowski, GGI, May 2010 – p.19
CMSSM: Impact of priors flat in m 0 , m 1 / 2 flat in log( m 0 ) , log( m 1 / 2 ) L. Roszkowski, GGI, May 2010 – p.19
CMSSM: Impact of priors flat in m 0 , m 1 / 2 flat in log( m 0 ) , log( m 1 / 2 ) still strong prior dependence (data not yet constraining enough) both priors: most regions above some 10 − 10 pb ⇒ good news for DM expt < 400 − 500 GeV ⇒ additional vital info LHC reach: m χ ∼ L. Roszkowski, GGI, May 2010 – p.19
Bayesian vs frequentist CMSSM: L. Roszkowski, GGI, May 2010 – p.20
Bayesian vs frequentist CMSSM: L. Roszkowski, GGI, May 2010 – p.20
Bayesian vs frequentist CMSSM: Buchmueller, et al (09) ( 10 − 44 cm 2 = 10 − 8 pb) L. Roszkowski, GGI, May 2010 – p.20
Bayesian vs frequentist CMSSM: Buchmueller, et al (09) ( 10 − 44 cm 2 = 10 − 8 pb) reasonable agreement L. Roszkowski, GGI, May 2010 – p.20
SUSY models and DM direct detection Bayesian analysis, log priors L. Roszkowski, GGI, May 2010 – p.21
SUSY models and DM direct detection Bayesian analysis, log priors Constrained MSSM DM: mostly gaugino L. Roszkowski, GGI, May 2010 – p.21
SUSY models and DM direct detection Bayesian analysis, log priors Constrained Next -to-MSSM (CNMSSM) add singlet Higgs S ; λS 3 Constrained MSSM Lopez−Fogliani, Roszkowski, Ruiz de Austri, Varley (2009) −4 EDELWEISS−I −5 ZEPLIN−II −6 ZEPLIN−III XENON−10 SI (pb)] CDMS−II −7 Log[ σ p −8 −9 CNMSSM µ >0 −10 log prior −11 0.2 0.4 0.6 0.8 1 m χ (TeV) DM: mostly gaugino singlino DM? very rare ⇒ fairly similar pattern L. Roszkowski, GGI, May 2010 – p.21
SUSY models and DM direct detection Bayesian analysis, log priors Constrained Next -to-MSSM (CNMSSM) add singlet Higgs S ; λS 3 Constrained MSSM Lopez−Fogliani, Roszkowski, Ruiz de Austri, Varley (2009) −4 EDELWEISS−I −5 ZEPLIN−II −6 ZEPLIN−III XENON−10 SI (pb)] CDMS−II −7 Log[ σ p −8 −9 CNMSSM µ >0 −10 log prior −11 0.2 0.4 0.6 0.8 1 m χ (TeV) DM: mostly gaugino singlino DM? very rare ⇒ fairly similar pattern many collider signatures also (likely to be) similar ⇒ LHC, DM expt: it may be hard to discriminate among models L. Roszkowski, GGI, May 2010 – p.21
SUSY models and DM direct detection Bayesian analysis, flat priors L. Roszkowski, GGI, May 2010 – p.22
SUSY models and DM direct detection Bayesian analysis, flat priors Constrained MSSM L. Roszkowski, GGI, May 2010 – p.22
SUSY models and DM direct detection Bayesian analysis, flat priors Non -Universal Higgs Model (NUHM) m 2 H u , m 2 H d � = m 2 0 Constrained MSSM Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) −4 EDELWEISS−I −5 ZEPLIN−II XENON−10 −6 ZEPLIN−III SI (pb)] CDMS−II −7 Log[ σ p −8 −9 NUHM, µ > 0 −10 log prior −11 0.5 1 1.5 m χ (TeV) higgsino DM region at m χ ≃ 1 TeV L. Roszkowski, GGI, May 2010 – p.22
SUSY models and DM direct detection Bayesian analysis, flat priors Non -Universal Higgs Model (NUHM) m 2 H u , m 2 H d � = m 2 0 Constrained MSSM Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) −4 EDELWEISS−I −5 ZEPLIN−II XENON−10 −6 ZEPLIN−III SI (pb)] CDMS−II −7 Log[ σ p −8 −9 NUHM, µ > 0 −10 log prior −11 0.5 1 1.5 m χ (TeV) higgsino DM region at m χ ≃ 1 TeV ⇒ fairly similar patterns, except for 1 TeV higgsino in NUHM L. Roszkowski, GGI, May 2010 – p.22
SUSY models and DM direct detection Bayesian analysis, flat priors Non -Universal Higgs Model (NUHM) m 2 H u , m 2 H d � = m 2 0 Constrained MSSM Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) −4 EDELWEISS−I −5 ZEPLIN−II XENON−10 −6 ZEPLIN−III SI (pb)] CDMS−II −7 Log[ σ p −8 −9 NUHM, µ > 0 −10 log prior −11 0.5 1 1.5 m χ (TeV) higgsino DM region at m χ ≃ 1 TeV ⇒ fairly similar patterns, except for 1 TeV higgsino in NUHM collider signatures also similar ⇒ LHC, DM: it may be hard to distinguish models L. Roszkowski, GGI, May 2010 – p.22
Indirect detection L. Roszkowski, GGI, May 2010 – p.23
Indirect detection look for traces of WIMP annihilation in the MW halo ( γ ’s, e + ’s, ¯ p , ...) detection prospects often strongly depend on astrophysical uncertainties (halo models, astro bgnd, ...) Much activity in connection with: L. Roszkowski, GGI, May 2010 – p.23
Indirect detection look for traces of WIMP annihilation in the MW halo ( γ ’s, e + ’s, ¯ p , ...) detection prospects often strongly depend on astrophysical uncertainties (halo models, astro bgnd, ...) Much activity in connection with: PAMELA L. Roszkowski, GGI, May 2010 – p.23
Indirect detection look for traces of WIMP annihilation in the MW halo ( γ ’s, e + ’s, ¯ p , ...) detection prospects often strongly depend on astrophysical uncertainties (halo models, astro bgnd, ...) Much activity in connection with: PAMELA Fermi L. Roszkowski, GGI, May 2010 – p.23
Indirect detection look for traces of WIMP annihilation in the MW halo ( γ ’s, e + ’s, ¯ p , ...) detection prospects often strongly depend on astrophysical uncertainties (halo models, astro bgnd, ...) Much activity in connection with: PAMELA Fermi H.E.S.S, ATCs, ... L. Roszkowski, GGI, May 2010 – p.23
SUSY and positron flux Bayesian posterior probability maps BF=1 L. Roszkowski, GGI, May 2010 – p.24
SUSY and positron flux Bayesian posterior probability maps BF=1 CMSSM, flat priors, NFW Roszkowski, Ruiz, Silk & Trotta (2008) −1 10 PAMELA 08 −2 Moore+ac HEAT 00 10 HEAT 94+95 Φ e+ /( Φ e+ + Φ e− ) CAPRICE 94 NFW+ac −3 10 −4 10 NFW −5 10 68% NFW profile, BF = 1 CMSSM, µ > 0 −6 10 95% −7 10 −8 10 0.1 1 10 100 400 E e+ (GeV) L. Roszkowski, GGI, May 2010 – p.24
SUSY and positron flux Bayesian posterior probability maps BF=1 NUHM, flat priors, NFW CMSSM, flat priors, NFW 0 Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) 10 Roszkowski, Ruiz, Silk & Trotta (2008) 0.62< m χ /TeV <1.1 (68% range) −1 10 −1 10 PAMELA 08 NUHM, µ >0 −2 Moore+ac Φ e+ /( Φ e+ + Φ e− ) b g HEAT 00 n 10 d . HEAT 94+95 −2 NFW, BF=1 10 Φ e+ /( Φ e+ + Φ e− ) CAPRICE 94 NFW+ac −3 flat prior 10 CAPRICE 94 HEAT 94+95 HEAT 00 −3 −4 PAMELA 10 10 NFW −5 10 −4 68% NFW profile, BF = 1 10 CMSSM, µ > 0 −6 10 −5 10 95% −7 10 −1 0 1 2 10 10 10 10 −8 E e+ (GeV) 10 0.1 1 10 100 400 E e+ (GeV) m χ (GeV) 200 400 600 800 1000 1200 L. Roszkowski, GGI, May 2010 – p.24
SUSY and positron flux Bayesian posterior probability maps BF=1 NUHM, flat priors, NFW CMSSM, flat priors, NFW 0 Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) 10 Roszkowski, Ruiz, Silk & Trotta (2008) 0.62< m χ /TeV <1.1 (68% range) −1 10 −1 10 PAMELA 08 NUHM, µ >0 −2 Moore+ac Φ e+ /( Φ e+ + Φ e− ) b g HEAT 00 n 10 d . HEAT 94+95 −2 NFW, BF=1 10 Φ e+ /( Φ e+ + Φ e− ) CAPRICE 94 NFW+ac −3 flat prior 10 CAPRICE 94 HEAT 94+95 HEAT 00 −3 −4 PAMELA 10 10 NFW −5 10 −4 68% NFW profile, BF = 1 10 CMSSM, µ > 0 −6 10 −5 10 95% −7 10 −1 0 1 2 10 10 10 10 −8 E e+ (GeV) 10 0.1 1 10 100 400 E e+ (GeV) m χ (GeV) 200 400 600 800 1000 1200 simple unified SUSY models (CMSSM, NUHM): inconsistent with PAMELA’s e + claim ...even for unrealistically large boost factors (flux scales linearly with boost factor) L. Roszkowski, GGI, May 2010 – p.24
Gamma Rays From DM Annihilation L. Roszkowski, GGI, May 2010 – p.25
Gamma Rays From DM Annihilation WIMP pair -annihilation → WW, ZZ, ¯ qq , . . . → diffuse γ radiation (+ γγ , γZ lines) L. Roszkowski, GGI, May 2010 – p.25
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