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Constraining CMSSM dark matter with direct detection results

This paper presents an analysis of the Constrained Minimal Supersymmetric Standard Model (CMSSM) and its implications for dark matter detection. It reviews how direct detection experiments, such as XENON1T and CDMS, can constrain WIMP properties including mass and cross-sections. We use statistical scanning methods to map the complex parameter space of CMSSM models, analyze existing experimental data, and discuss future detection strategies. The findings aim to enhance our understanding of dark matter properties through direct detection results, setting the stage for future phenomenological investigations.

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Constraining CMSSM dark matter with direct detection results

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  1. Constraining CMSSM dark matter with direct detection results Chris Savage Oskar Klein Centre for Cosmoparticle PhysicsStockholm University with Yashar Akrami, Pat Scott, Jan Conrad & Joakim Edsjö JCAP 1104:012, 2011 [arXiv:1011.4318]JCAP 1107:002,2011 [arXiv:1011.4297]

  2. Overview Direct detection signalN, Ek=1..N , Sm, , , ... Direct detection signalN, Ek=1..N , Sm, , , ... WIMP parameters m , SI,p , SD,p , SD,n Pheno space notfullymapped outby CMSSM WIMP parameters m , SI,p , SD,p , SD,n CMSSM (e.g.) parameters m0, m1/2, A0, tanβ, sign(μ) Phenomenology Particle (SUSY) Theory Experimental groupsJi-Haeng Huhtalk Well behaved parameter space:analytical methods for constraints? Messy parameter space:statistical scanning required This talk C Savage - DSU 2011 - CMSSM and Direct Detection

  3. Overview • How will future direct detection results constrain dark matter from supersymmetric theories? Realistic reconstruction of dark matter properties using CMSSM as case study • Outline • Basics: CMSSM, direct detection • Analysis: likelihoods, statistics and scanning • Phenomenological parameter constraints • Individual/combined experimental results • Statistical/scanning issues • Halo model, hadronic uncertainties • CMSSM parameter constraints C Savage - DSU 2011 - CMSSM and Direct Detection

  4. Basics andAnalysis Procedure C Savage - DSU 2011 - CMSSM and Direct Detection

  5. Detector WIMP Scatter WIMP Basics CMSSM (Constrained Minimal SupersymmetricStandard Model) • Simplest SUSY model: four parameters + one sign • Complicated parameter space: disconnected regions, sharp peaks,… • Results/issues representative of generic SUSY models(e.g. MSSM-7, BMSSM, NMSSM, etc.) Direct detection:future ton-scale experiments • XENON1T (Xe, neutron odd) [LUX, PANDA-X] • CDMS1T (Ge, neutron odd) [EDELWEISS, CRESST?] • COUPP1T (CF3I, proton odd) Not included: CoGeNT, CDEX, DAMA, KIMS -like (higher backgrounds) C Savage - DSU 2011 - CMSSM and Direct Detection

  6. Analysis See paper for technical details Realistic analysis • Typical thresholds and efficiencies • Finite energy resolution • Backgrounds at target levels (~ 2 events), known spectrum • Uncertainties in halo model (density, velocity distribution) • Hadronic uncertainties: WIMP-quark → WIMP-nucleon couplings Likelihoods • Direct detection • Nuisance parameters  Halo model  Nucleon structure  SM parameters • …also physicality constraints Number of events (Poisson) Event energies (spectrum) COUPP: no spectrum C Savage - DSU 2011 - CMSSM and Direct Detection

  7. Analysis Procedure • Select CMSSM models that give particular m and SI,p :benchmark models • Generate random experimental results • Reconstruct CMSSM model by scanningover CMSSM parameter space • DarkSUSY + SuperBayeS (MultiNest) Statistics • Scan: Bayesian (SuperBayes) • Results: Frequentist or Bayesian • Profile likelihood (frequentist) • Marginalized PDF (Bayesian) www.darksusy.orgwww.superbayes.org Most experimental analyses C Savage - DSU 2011 - CMSSM and Direct Detection

  8. Benchmark Models BM1: low m , high SI,p O(100-400) signal events BM2: low m , low SI,p O(1-3) signal events BM3: moderate m and SI,p O(20-30) signal events BM4: high m , high SI,p O(20-30) signal events + 2 background events (on average) Benchmarks still below most recent XENON constraints C Savage - DSU 2011 - CMSSM and Direct Detection

  9. Results(Constraints) C Savage - DSU 2011 - CMSSM and Direct Detection

  10.  true value max likelihood  posterior mean BM1: low m , high SI,p Profile likelihood: ■ 1σ■ 2σ Spin-independent/dependent cross-sections vs. mass • XENON: ~ 200 signal events (~ 7 SD events) C Savage - DSU 2011 - CMSSM and Direct Detection

  11. BM1: low m , high SI,p Profile likelihood: ■ 1σ■ 2σ Spin-independent/dependent cross-sections vs. mass • CDMS: ~ 140 signal events (~ 2 SD events) C Savage - DSU 2011 - CMSSM and Direct Detection

  12. BM1: low m , high SI,p Profile likelihood: ■ 1σ■ 2σ Spin-independent/dependent cross-sections vs. mass • COUPP: ~ 390 signal events (~ 120 SD events) C Savage - DSU 2011 - CMSSM and Direct Detection

  13. BM1: low m , high SI,p Profile likelihood: ■ 1σ■ 2σ Spin-dependent couplings: neutron vs. proton • an ≈ -ap : CMSSM prediction (not experimental constraint) • O(5) [CDMS/XENON] vs. O(100) [COUPP] SD events C Savage - DSU 2011 - CMSSM and Direct Detection

  14. BM2: low m , low SI,p Profile likelihood: ■ 1σ■ 2σ Spin-independent/dependent cross-sections vs. mass • ~ 1.4 / 2.1 / 3.0 signal events (~ 0 / 0 / 0.1 SD) C Savage - DSU 2011 - CMSSM and Direct Detection

  15. BM3: moderate m and SI,p Profile likelihood: ■ 1σ■ 2σ Spin-independent/dependent cross-sections vs. mass • ~ 17 / 23 / 32 signal events (~ 0 / 0 / 0.6 SD) C Savage - DSU 2011 - CMSSM and Direct Detection

  16. BM4: high m , high SI,p Profile likelihood: ■ 1σ■ 2σ Spin-independent/dependent cross-sections vs. mass • ~ 19 / 25 / 36 signal events (~ 0 / 0 / 0.3 SD) C Savage - DSU 2011 - CMSSM and Direct Detection

  17. Issues C Savage - DSU 2011 - CMSSM and Direct Detection

  18. Issue: sampling/coverage • Mass constraint from energy spectrum:degeneracy for heavy WIMPs BM3 BM4 Phenomenological parameter scanPatoet al., PRD 83, 083505 (2011) C Savage - DSU 2011 - CMSSM and Direct Detection

  19. Issue: sampling/coverage • Scan points without DD likelihood • BM4 in poorly sampled region • BM3 in higher sampled region • Degeneracy: • BM3 & BM4 should givesimilar DD signals (N, Ei) • BM4 scan: • Good fit around BM3 • Nothing to draw scan towardsBM4 region • Too few models to properlyevaluate profile likelihood C Savage - DSU 2011 - CMSSM and Direct Detection

  20. Issue: sampling/coverage • Real priors and/or effective priors affect scan region • Scan may miss some regions of interest or cover them too coarsely • Can lead to significant over/under-coverage of confidence regions (frequentist) or credible regions (Bayesian) • Possibly improved by higher statistics • …if higher statistics gives sharper likelihood contours (can overcome real/effective priors) • Not for previous case C Savage - DSU 2011 - CMSSM and Direct Detection

  21. Issue: nuisance parameters • Halo model • Local density, velocity distribution • Standard Halo Model (SHM): isothermal sphere • 3 velocity parameters: v0, vobs, vesc • Structure? • Annual modulation (DAMA, CoGeNT) • Directional detection (DRIFT) • Hadronic matrix elements • Used in calculating SI & SD from -quark couplings • 6 relevant matrix elements (only 3 are important) • Affect CMSSM constraints, not pheno constraints (at least not directly) Halo models + direct detection:see Strigari & Trotta (2009)and various works by A. Green See Ellis, Olive & CS, PRD 77, 065026 (2008) C Savage - DSU 2011 - CMSSM and Direct Detection

  22. Halo model uncertainties Profile likelihood: ■ 1σ■ 2σ With / without uncertainties in halo model (nuisance parameters) • Local DM density most significant • See e.g. Strigari & Trotta, JCAP 11, 019 (2009) C Savage - DSU 2011 - CMSSM and Direct Detection

  23. Hadronic uncertainties Profile likelihood: ■ 1σ■ 2σ With / without hadronic uncertainties (nuisance parameters) • No change: affects only CMSSM parameter constraints C Savage - DSU 2011 - CMSSM and Direct Detection

  24. Hadronic uncertainties Profile likelihood: ■ 1σ■ 2σ With / without hadronic uncertainties (nuisance parameters) • Only directly affects CMSSM parameter constraints C Savage - DSU 2011 - CMSSM and Direct Detection

  25. CMSSMConstraints C Savage - DSU 2011 - CMSSM and Direct Detection

  26. CMSSM constraints Profile likelihood: ■ 1σ■ 2σ No direct detection likelihood (priors and nuisance only) C Savage - DSU 2011 - CMSSM and Direct Detection

  27. CMSSM constraints (BM1) Profile likelihood: ■ 1σ■ 2σ With direct detection likelihood • Gaugino mass (m1/2) best constrained (related to m) • Weaker constraints on m0, A0, tanβ C Savage - DSU 2011 - CMSSM and Direct Detection

  28. CMSSM constraints • Can combine with other observational data: • Indirect detection: cosmic-rays, neutrinos • Accelerators • Relic density, etc. See Trottaet al., JHEP 0812:024 (2008) -rays (Fermi-LAT) Segue 1 analysis Scott et al. (2009) Neutrinos (IceCube) IC collab + Edsjö, Scott, CS, in prep. Accelerator (LHC: ATLAS) SU3 benchmark analysis Bridges et al. (2010) C Savage - DSU 2011 - CMSSM and Direct Detection

  29. Summary • Examined realistic reconstruction of darkmatter properties in SUSY (e.g. CMSSM)theories using direct detection results • Can reconstruct WIMP properties reasonablywell in some cases, not so well in others • Coverage, sampling issues:Accuracy affected by scanning technique • Nuisance parameters • Combine DD results with other observationsto better constrain SUSY theory parameters C Savage - DSU 2011 - CMSSM and Direct Detection

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