Strategies for WIMP Detection direct detection (DD): measure WIMPs scattering off a target go underground to beat cosmic ray bgnd indirect detection (ID): HE neutrinos from the Sun (or Earth) WIMPs get trapped in Sun’s core, start pair annihilating, only ν ’s escape p , ¯ antimatter ( e + , ¯ D ) from WIMP pair-annihilation in the MW halo from within a few kpc gamma rays from WIMP pair-annihilation in the Galactic center depending on DM distribution in the GC other ideas: traces of WIMP annihilation in dwarf galaxies, in rich clusters, etc more speculative L. Roszkowski, GGI, 9 Feb ’09 – p.10
MSSM: Expectations for σ SI p general MSSM µ > 0 Kim, Nihei, LR & Ruiz de Austri (02) σ SI p – WIMP–proton SI elastic scatt. c.s. (elastic c.s. for χp → χp at zero momentum transfer) L. Roszkowski, GGI, 9 Feb ’09 – p.11
MSSM: Expectations for σ SI p general MSSM µ > 0 Kim, Nihei, LR & Ruiz de Austri (02) σ SI p – WIMP–proton SI elastic scatt. c.s. (elastic c.s. for χp → χp at zero momentum transfer) ⇒ MSSM: vast ranges! Lacks real predictive power! L. Roszkowski, GGI, 9 Feb ’09 – p.11
Add grand unification... L. Roszkowski, GGI, 9 Feb ’09 – p.12
Constrained MSSM (CMSSM) Kane, Kolda, LR, Wells (1993) ...“benchmark framework” for the LHC (...e.g., mSUGRA) L. Roszkowski, GGI, 9 Feb ’09 – p.13
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 L. Roszkowski, GGI, 9 Feb ’09 – p.13
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 L. Roszkowski, GGI, 9 Feb ’09 – p.13
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 ) L. Roszkowski, GGI, 9 Feb ’09 – p.13
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 masses and couplings L. Roszkowski, GGI, 9 Feb ’09 – p.13
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 masses and couplings neutralino χ mostly bino L. Roszkowski, GGI, 9 Feb ’09 – p.13
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, 9 Feb ’09 – p.13
Earlier analyses of CMSSM very many papers L. Roszkowski, GGI, 9 Feb ’09 – p.14
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, 9 Feb ’09 – p.14
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, 9 Feb ’09 – p.14
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, 9 Feb ’09 – p.14
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, 9 Feb ’09 – p.14
Bayesian Analysis of the CMSSM Apply to the CMSSM: new development, led by 2 groups L. Roszkowski, GGI, 9 Feb ’09 – p.15
Bayesian Analysis of the CMSSM Apply to the CMSSM: new development, led by 2 groups m = ( θ, ψ ) – model’s all relevant parameters L. Roszkowski, GGI, 9 Feb ’09 – p.15
Bayesian Analysis of the CMSSM Apply to the CMSSM: new 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 , fix sgn( µ ) relevant SM param’s ψ = M t , m b ( m b ) MS , α MS , α em ( M Z ) MS s L. Roszkowski, GGI, 9 Feb ’09 – p.15
Bayesian Analysis of the CMSSM Apply to the CMSSM: new 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 , fix sgn( µ ) 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, 9 Feb ’09 – p.15
Bayesian Analysis of the CMSSM Apply to the CMSSM: new 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 , fix sgn( µ ) 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, 9 Feb ’09 – p.15
Bayesian Analysis of the CMSSM Apply to the CMSSM: new 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 , fix sgn( µ ) 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, 9 Feb ’09 – p.15
Bayesian Analysis of the CMSSM Apply to the CMSSM: new 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 , fix sgn( µ ) 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, 9 Feb ’09 – p.15
Impact of varying SM parameters L. Roszkowski, GGI, 9 Feb ’09 – p.16
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, 9 Feb ’09 – p.16
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, 9 Feb ’09 – p.16
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, 9 Feb ’09 – p.16
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, 9 Feb ’09 – p.16
CMSSM: Prospects for direct detection CMSSM: Constrained MSSM Bayesian analysis, flat priors, MCMC L. Roszkowski, GGI, 9 Feb ’09 – p.17
CMSSM: Prospects for direct detection CMSSM: Constrained MSSM Bayesian analysis, flat priors, 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, 9 Feb ’09 – p.17
CMSSM: Prospects for direct detection CMSSM: Constrained MSSM Bayesian analysis, flat priors, MCMC Roszkowski, Ruiz & Trotta (2007) −4 XENON-10 (June 07) and CDMS-II CMSSM, µ > 0 (Feb 08): −5 EDELWEISS−I < 10 − 7 pb: σ SI −6 ∼ XENON−10 p SI (pb)] ZEPLIN−III also Zeplin–III CDMS−II −7 Log[ σ p ⇒ already explore 68% region −8 (large m 0 ≫ m 1 / 2 ⇒ heavy squarks) −9 largely beyond LHC reach −10 −11 0.2 0.4 0.6 0.8 1 m χ (TeV) internal (external): 68% ( 95% ) region L. Roszkowski, GGI, 9 Feb ’09 – p.17
CMSSM: Prospects for direct detection CMSSM: Constrained MSSM Bayesian analysis, flat priors, MCMC Roszkowski, Ruiz & Trotta (2007) −4 XENON-10 (June 07) and CDMS-II CMSSM, µ > 0 (Feb 08): −5 EDELWEISS−I < 10 − 7 pb: σ SI −6 ∼ XENON−10 p SI (pb)] ZEPLIN−III also Zeplin–III CDMS−II −7 Log[ σ p ⇒ already explore 68% region −8 (large m 0 ≫ m 1 / 2 ⇒ heavy squarks) −9 largely beyond LHC reach −10 Roszkowski, Ruiz & Trotta (2007) 4 3.5 −11 0.2 0.4 0.6 0.8 1 3 m 0 (TeV) 2.5 m χ (TeV) 2 1.5 LHC internal (external): 68% ( 95% ) region 1 0.5 CMSSM µ >0 0.5 1 1.5 2 m 1/2 (TeV) L. Roszkowski, GGI, 9 Feb ’09 – p.17
CMSSM: Prospects for direct detection CMSSM: Constrained MSSM Bayesian analysis, flat priors, MCMC Roszkowski, Ruiz & Trotta (2007) −4 XENON-10 (June 07) and CDMS-II CMSSM, µ > 0 (Feb 08): −5 EDELWEISS−I < 10 − 7 pb: σ SI −6 ∼ XENON−10 p SI (pb)] ZEPLIN−III also Zeplin–III CDMS−II −7 Log[ σ p ⇒ already explore 68% region −8 (large m 0 ≫ m 1 / 2 ⇒ heavy squarks) −9 largely beyond LHC reach −10 Roszkowski, Ruiz & Trotta (2007) 4 3.5 −11 0.2 0.4 0.6 0.8 1 3 m 0 (TeV) 2.5 m χ (TeV) 2 1.5 LHC internal (external): 68% ( 95% ) region 1 0.5 CMSSM ⇒ DD: prospects look very good µ >0 0.5 1 1.5 2 m 1/2 (TeV) L. Roszkowski, GGI, 9 Feb ’09 – p.17
Impact of priors L. Roszkowski, GGI, 9 Feb ’09 – p.18
Impact of priors flat in m 0 , m 1 / 2 CMSSM, µ >0, flat priors Trotta et al (2008) −5 ZEPLIN−II XENON−10 −6 SI ) (pb) CDMS−II −7 −8 log( σ p −9 −10 −11 200 400 600 800 m χ (GeV) L. Roszkowski, GGI, 9 Feb ’09 – p.18
Impact of priors flat in m 0 , m 1 / 2 flat in log( m 0 ) , log( m 1 / 2 ) CMSSM, µ >0, flat priors CMSSM, µ >0, log priors Trotta et al (2008) Trotta et al (2008) −5 −5 ZEPLIN−II ZEPLIN−II XENON−10 XENON−10 −6 −6 SI ) (pb) SI ) (pb) CDMS−II −7 −7 CDMS−II −8 −8 log( σ p log( σ p −9 −9 −10 −10 −11 −11 200 400 600 800 200 400 600 800 m χ (GeV) m χ (GeV) L. Roszkowski, GGI, 9 Feb ’09 – p.18
Impact of priors flat in m 0 , m 1 / 2 flat in log( m 0 ) , log( m 1 / 2 ) CMSSM, µ >0, flat priors CMSSM, µ >0, log priors Trotta et al (2008) Trotta et al (2008) −5 −5 ZEPLIN−II ZEPLIN−II XENON−10 XENON−10 −6 −6 SI ) (pb) SI ) (pb) CDMS−II −7 −7 CDMS−II −8 −8 log( σ p log( σ p −9 −9 −10 −10 −11 −11 200 400 600 800 200 400 600 800 m χ (GeV) m χ (GeV) 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, 9 Feb ’09 – p.18
Non-Universal Higgs Mass (NUHM) ...many papers (Ellis et al, Munoz, et al, Baer et al.) L. Roszkowski, GGI, 9 Feb ’09 – p.19
Non-Universal Higgs Mass (NUHM) ...many papers (Ellis et al, Munoz, et al, Baer et al.) At M GUT ≃ 2 × 10 16 GeV : disunify Higgs soft masses from other scalars gauginos M 1 = M 2 = m e g = m 1 / 2 (c.f. MSSM) m 2 q i = m 2 l i = m 2 squarks, sleptons e e 0 m 2 H d , m 2 Higgs doublets H u 3–linear soft terms A b = A t = A 0 L. Roszkowski, GGI, 9 Feb ’09 – p.19
Non-Universal Higgs Mass (NUHM) ...many papers (Ellis et al, Munoz, et al, Baer et al.) At M GUT ≃ 2 × 10 16 GeV : disunify Higgs soft masses from other scalars gauginos M 1 = M 2 = m e g = m 1 / 2 (c.f. MSSM) m 2 q i = m 2 l i = m 2 squarks, sleptons e e 0 m 2 H d , m 2 Higgs doublets H u 3–linear soft terms A b = A t = A 0 radiative EWSB “ ” u ) tan 2 β Hd +Σ (1) m 2 − ( m 2 Hu +Σ (1) − m 2 µ 2 = d Z tan 2 β − 1 2 L. Roszkowski, GGI, 9 Feb ’09 – p.19
Non-Universal Higgs Mass (NUHM) ...many papers (Ellis et al, Munoz, et al, Baer et al.) At M GUT ≃ 2 × 10 16 GeV : disunify Higgs soft masses from other scalars gauginos M 1 = M 2 = m e g = m 1 / 2 (c.f. MSSM) m 2 q i = m 2 l i = m 2 squarks, sleptons e e 0 m 2 H d , m 2 Higgs doublets H u 3–linear soft terms A b = A t = A 0 radiative EWSB “ ” u ) tan 2 β Hd +Σ (1) m 2 − ( m 2 Hu +Σ (1) − m 2 µ 2 = d Z tan 2 β − 1 2 tan β, m 1 / 2 , m 0 , m H u , m H d , A 0 , sgn( µ ) 6+1 parameters: L. Roszkowski, GGI, 9 Feb ’09 – p.19
Non-Universal Higgs Mass (NUHM) ...many papers (Ellis et al, Munoz, et al, Baer et al.) At M GUT ≃ 2 × 10 16 GeV : disunify Higgs soft masses from other scalars gauginos M 1 = M 2 = m e g = m 1 / 2 (c.f. MSSM) m 2 q i = m 2 l i = m 2 squarks, sleptons e e 0 m 2 H d , m 2 Higgs doublets H u 3–linear soft terms A b = A t = A 0 radiative EWSB “ ” u ) tan 2 β Hd +Σ (1) m 2 − ( m 2 Hu +Σ (1) − m 2 µ 2 = d Z tan 2 β − 1 2 tan β, m 1 / 2 , m 0 , m H u , m H d , A 0 , sgn( µ ) 6+1 parameters: two more parameters than in CMSSM surprisingly rich phenomenological difference with CMSSM L. Roszkowski, GGI, 9 Feb ’09 – p.19
NUHM: DM Searches Bayesian posterior probability maps spin-independent c.s. L. Roszkowski, GGI, 9 Feb ’09 – p.20
NUHM: DM Searches Bayesian posterior probability maps spin-independent c.s. flat prior 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 flat prior −11 0.5 1 1.5 m χ (TeV) large region at m χ ∼ 1 TeV L. Roszkowski, GGI, 9 Feb ’09 – p.20
NUHM: DM Searches Bayesian posterior probability maps spin-independent c.s. log prior in m 1 / 2 , m 0 flat prior Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) −4 −4 EDELWEISS−I EDELWEISS−I −5 −5 ZEPLIN−II ZEPLIN−II XENON−10 XENON−10 −6 −6 ZEPLIN−III ZEPLIN−III SI (pb)] SI (pb)] CDMS−II CDMS−II −7 −7 Log[ σ p Log[ σ p −8 −8 −9 −9 NUHM, µ > 0 NUHM, µ > 0 −10 −10 flat prior log prior −11 −11 0.5 1 1.5 0.5 1 1.5 m χ (TeV) m χ (TeV) large region at m χ ∼ 1 TeV big shift towards smaller m χ L. Roszkowski, GGI, 9 Feb ’09 – p.20
NUHM: DM Searches Bayesian posterior probability maps spin-independent c.s. log prior in m 1 / 2 , m 0 flat prior Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) −4 −4 EDELWEISS−I EDELWEISS−I −5 −5 ZEPLIN−II ZEPLIN−II XENON−10 XENON−10 −6 −6 ZEPLIN−III ZEPLIN−III SI (pb)] SI (pb)] CDMS−II CDMS−II −7 −7 Log[ σ p Log[ σ p −8 −8 −9 −9 NUHM, µ > 0 NUHM, µ > 0 −10 −10 flat prior log prior −11 −11 0.5 1 1.5 0.5 1 1.5 m χ (TeV) m χ (TeV) large region at m χ ∼ 1 TeV big shift towards smaller m χ ⇒ NUHM: new higgsino LSP region at m χ ∼ 1 TeV ⇒ large prior dependence L. Roszkowski, GGI, 9 Feb ’09 – p.20
Indirect detection L. Roszkowski, GGI, 9 Feb ’09 – p.21
Indirect detection look for traces of WIMP annihilation in the MW halo detection prospects often strongly depend on astrophysical uncertainties L. Roszkowski, GGI, 9 Feb ’09 – p.21
Indirect detection look for traces of WIMP annihilation in the MW halo detection prospects often strongly depend on astrophysical uncertainties dark halo models? overdense regions (clumps)? L. Roszkowski, GGI, 9 Feb ’09 – p.21
Indirect detection look for traces of WIMP annihilation in the MW halo detection prospects often strongly depend on astrophysical uncertainties dark halo models? overdense regions (clumps)? DM density profile near Galactic center? L. Roszkowski, GGI, 9 Feb ’09 – p.21
CDM Halo Models ...not a settled matter L. Roszkowski, GGI, 9 Feb ’09 – p.22
CDM Halo Models ...not a settled matter fitting DM halo with a semi-heuristic formula: � r � γ � � r � α � ( β − γ ) /α ρ DM ( r ) = ρ c / 1 + a a α, β, γ - adjustable parameters ´ γ h “ ” α i ( β − γ ) /α ` r 0 , ρ 0 ∼ 0 . 3 GeV / cm 3 - DM density at r 0 R 0 ρ c = ρ 0 1 + a a a - scale radius - from num. sim’s or to match observations L. Roszkowski, GGI, 9 Feb ’09 – p.22
CDM Halo Models ...not a settled matter fitting DM halo with a semi-heuristic formula: � r � γ � � r � α � ( β − γ ) /α ρ DM ( r ) = ρ c / 1 + a a α, β, γ - adjustable parameters ´ γ h “ ” α i ( β − γ ) /α ` r 0 , ρ 0 ∼ 0 . 3 GeV / cm 3 - DM density at r 0 R 0 ρ c = ρ 0 1 + a a a - scale radius - from num. sim’s or to match observations • adiabatic compression due to baryon concentration in the GC: likely effect: central cusp becames steeper: “model” ⇒ “model-c” L. Roszkowski, GGI, 9 Feb ’09 – p.22
CDM Halo Models ...not a settled matter fitting DM halo with a semi-heuristic formula: � r � γ � � r � α � ( β − γ ) /α ρ DM ( r ) = ρ c / 1 + a a α, β, γ - adjustable parameters ´ γ h “ ” α i ( β − γ ) /α ` r 0 , ρ 0 ∼ 0 . 3 GeV / cm 3 - DM density at r 0 R 0 ρ c = ρ 0 1 + a a a - scale radius - from num. sim’s or to match observations • adiabatic compression due to baryon concentration in the GC: likely effect: central cusp becames steeper: “model” ⇒ “model-c” some most popular models: halo model a r 0 ( α, β, γ ) small r large r r ∝ r − γ r ∝ r − β ( kpc) ( kpc) r − 2 isothermal cored 3.5 8.5 (2 , 2 , 0) flat r − 1 r − 3 NFW 20.0 8.0 (1 , 3 , 1) r − 1 . 5 r − 3 NFW-c 20.0 8.0 (1 . 5 , 3 , 1 . 5) r − 1 . 5 r − 3 Moore 28.0 8.0 (1 , 3 , 1 . 5) r − 1 . 65 r − 2 . 7 (0 . 8 , 2 . 7 , 1 . 65) Moore-c 28.0 8.0 L. Roszkowski, GGI, 9 Feb ’09 – p.22
CDM Halo Models ...not a settled matter fitting DM halo with a semi-heuristic formula: � r � γ � � r � α � ( β − γ ) /α ρ DM ( r ) = ρ c / 1 + a a α, β, γ - adjustable parameters ´ γ h “ ” α i ( β − γ ) /α ` r 0 , ρ 0 ∼ 0 . 3 GeV / cm 3 - DM density at r 0 R 0 ρ c = ρ 0 1 + a a a - scale radius - from num. sim’s or to match observations • adiabatic compression due to baryon concentration in the GC: likely effect: central cusp becames steeper: “model” ⇒ “model-c” some most popular models: halo model a r 0 ( α, β, γ ) small r large r r ∝ r − γ r ∝ r − β ( kpc) ( kpc) r − 2 isothermal cored 3.5 8.5 (2 , 2 , 0) flat r − 1 r − 3 NFW 20.0 8.0 (1 , 3 , 1) r − 1 . 5 r − 3 NFW-c 20.0 8.0 (1 . 5 , 3 , 1 . 5) r − 1 . 5 r − 3 Moore 28.0 8.0 (1 , 3 , 1 . 5) r − 1 . 65 r − 2 . 7 (0 . 8 , 2 . 7 , 1 . 65) Moore-c 28.0 8.0 Many open questions: clumps??, central cusp??, spherical or tri–axial??,. . . L. Roszkowski, GGI, 9 Feb ’09 – p.22
Our Milky Way example of a reasonable model (Klypin, et al., 2001) L. Roszkowski, GGI, 9 Feb ’09 – p.23
Our Milky Way example of a reasonable model (Klypin, et al., 2001) based on NFW model with angular mom. exchange between baryons and DM DM dominates only at large r , well beyond the solar radius DM likely to be subdominant in the inner regions if no exchange of angular mom.: more DM in the center (but problem with fast rotating bar?) L. Roszkowski, GGI, 9 Feb ’09 – p.23
Halo models 3 10 Klypin et al Moore 2 DM density (GeV/cm 3 ) 10 NFW isothermal 1 10 0 10 −1 10 −2 10 −3 10 −3 −2 −1 0 1 2 10 10 10 10 10 10 Radius (kpc) L. Roszkowski, GGI, 9 Feb ’09 – p.24
Halo models 3 10 Klypin et al Moore 2 DM density (GeV/cm 3 ) 10 NFW isothermal 1 10 0 10 −1 10 −2 10 −3 10 −3 −2 −1 0 1 2 10 10 10 10 10 10 Radius (kpc) steeper inner profie r − 1 . 8 ⇒ stronger DM annihilation at small r L. Roszkowski, GGI, 9 Feb ’09 – p.24
Diffuse GRs from the GC use Fermi/GLAST parameters Bayesian posterior probability maps L. Roszkowski, GGI, 9 Feb ’09 – p.25
Diffuse GRs from the GC use Fermi/GLAST parameters Bayesian posterior probability maps CMSSM, flat priors L. Roszkowski, GGI, 9 Feb ’09 – p.25
Diffuse GRs from the GC use Fermi/GLAST parameters Bayesian posterior probability maps CMSSM, flat priors NUHM, flat priors Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) Φ γ from GC −6 K l y p i n NUHM, µ > 0 Log[ Φ γ (cm −2 s −1 )] flat prior −8 Fermi/GLAST reach (1yr) −10 NFW −12 isothermal −14 ∆ Ω = 10 −5 sr E thr = 10 GeV −16 0.5 1 1.5 2 m χ (TeV) L. Roszkowski, GGI, 9 Feb ’09 – p.25
Diffuse GRs from the GC use Fermi/GLAST parameters Bayesian posterior probability maps CMSSM, flat priors NUHM, flat priors Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) Φ γ from GC −6 K l y p i n NUHM, µ > 0 Log[ Φ γ (cm −2 s −1 )] flat prior −8 Fermi/GLAST reach (1yr) −10 NFW −12 isothermal −14 ∆ Ω = 10 −5 sr E thr = 10 GeV −16 0.5 1 1.5 2 m χ (TeV) ⇒ WIMP signal at Fermi/GLAST: outcome depends on halo cuspiness at GC L. Roszkowski, GGI, 9 Feb ’09 – p.25
Impact of Priors use Fermi/GLAST parameters Bayesian posterior probability maps L. Roszkowski, GGI, 9 Feb ’09 – p.26
Impact of Priors use Fermi/GLAST parameters Bayesian posterior probability maps NUHM, flat priors Roszkowski, Ruiz, Trotta, Tsai & Varley (2009) Φ γ from GC −6 K l y p i n NUHM, µ > 0 Log[ Φ γ (cm −2 s −1 )] flat prior −8 Fermi/GLAST reach (1yr) −10 NFW −12 isothermal −14 ∆ Ω = 10 −5 sr E thr = 10 GeV −16 0.5 1 1.5 2 m χ (TeV) L. Roszkowski, GGI, 9 Feb ’09 – p.26
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