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Extraction of Parameters from Collider Data?

Extraction of Parameters from Collider Data?. NPF 2009 Heidelberg 02/26/2009 Klaus Desch and Dirk Zerwas Uni Bonn and LAL Orsay. Introduction Finding the right parameter set getting the errors right other applications Open questions.

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Extraction of Parameters from Collider Data?

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  1. Extraction of Parameters from Collider Data? NPF 2009 Heidelberg 02/26/2009 Klaus Desch and Dirk Zerwas Uni Bonn and LAL Orsay • Introduction • Finding the right parameter set • getting the errors right • other applications • Open questions • Definition 1: mSUGRA=toy (Klaus left to hide from Tilman) • Definition 2: MSSM=oset 4711

  2. Determination of Supersymmetric Parameters from edges, masses (etc) to fundamental parameters: e.g.: mSUGRA (from Klaus to Tilman with love) m(Smuon) = f(m0, m1/2, tanβ) m(Chargino) = f(m1/2, tanβ,…) correlations exp and theoretical treatment of theory errors!  global ansatz necessary Beenakker et al See Allanach arXiv:0805.2088[hep-ph] for complete list • mass spectra and decays: SOFTSUSY, SUSPECT, FeynHiggs, ISASUSY,SPHENO, SDecay, SUSY-HIT, HDECAY, NMSSMtools,… • NLO cross sections from Prospino2.0,… • dark matter: micrOMEGAS, DarkSUSY, IsaRED,… Search for parameter point, determine errors including treatment of error correlations: Pioneers: G. Blair, W. Porod and P.M. Zerwas(Eur.Phys.J.C27:263-281,2003) / Allanach et al. hep-ph/0403133 FITTINO: P. Bechtle, K. Desch, P. Wienemann with W. Porod(Eur.Phys.J.C46:533-544,2006) SFITTER: R. Lafaye, T. Plehn, M. Rauch, D. Z.(Eur.Phys.J.C54:617-644,2008) GFITTER: M. Goebel, J. Haller, A. Hoecker, K. Moenig, J. Stelzer (EW fit) Master Code: Buchmueller et al, Phys.Lett.B657:87-94,2007(my name is Vader, Dirk Vader) Super-Bayes: R.R. de Austri, R. Trotta, (MCMC, collider observables and dark matter…) CMSSM fits and weather forcasts: B. Allanach, K. Cranmer, C. Lester, A. Weber …

  3. A typical (optimistic) point “Physics Interplay of the LHC and ILC” G. Weiglein et al gluinos and squarks (not too heavy) heavy and light gauginos ~ • τ1 lighter than the lightest χ± : • χ± BR 100% τν • χ2 BR 90% ττ • cascade: • qLχ2 q  ℓR ℓq  ℓℓqχ1 ~ ~ ~ ~ Higgs boson mass at the LEP limit light sleptons Forced to talk about SPS1a(‘) by genetic pre-disposition (quote from Mihoko)

  4. SUSY already discovered? Global mSugra fit to measured things… (to compare with Buchmüller ea – consistent) dominated by g-2 and h2DM - but encouraging…

  5. From LE fit: predicted mass spectra no (g-2) no h2DM Prediction driven by g-2 and Ωh2

  6. LHC measurements NPF: Shoji, Dan LHC: lepton energy scale 0.1% LHC: jet energy scale 1% luminosity 100fb-1 SPS1a • LHC: • from edges/thresholds to masses: toy/fit

  7. Lagrangian@GUT scale: mSUGRA Hypothesis: SUSY particles discovered and non-discrete quantum numbers to be measured • First question: • do we find the right point? brute force method GRID: disadvantage: NP advantage: computer industry brute force method MINUIT: disadvantage: starting point Sign(μ) fixed ~300 toy experiments: convergence OK with MINUIT alone for LHC (largest errors)!

  8. Model discrimination What are fits to mSugra are good for after all? - rule out the most simple assumptions (degenerate masses…) - discriminate between different „digital“ assumptions, e.g. misinterpreation of edge replace sgn  = -1 sgn  = +1 with 1 fb-1: correct model (sgn =+) preferred with 96% probability with 1 fb-1: correct model preferred with 77% probability

  9. Lagrangian@GUT scale: mSUGRA Simulated annealing • Markov Chains (efficient sampling in high dimensions, linear in number of parameters) • Full dimensional exclusive likelihood map with the possibility of different types of projections: • marginalisation (Bayes) introduces a measure • profile likelihood (Frequentist approach) Ranked list of minima: • secondary minima exist (LHC) • discarded by χ2 alone • interplay with top mass (parameter!)

  10. Fittino: error determination from Toy Fits Parameter fits: 2 distribution as reliable „quality control“ for derived uncertainties - simulated annealing  find global minimum - Toy MC  map out spread of parameters when observables vary within their expt. errors MC Toy fits:

  11. mSUGRA: Theory Errors and Standard Model RFit Scheme: Höcker, Lacker, Laplace, Lediberder • No information within theory errors: flat distribution • use edges not masses (improvement: 10x)! • e-scale correlations at LHC 25-50% impact on error • remember the standard model (top quark mass 1GeV at LHC) 10%.

  12. MSSM 19 parametersat the EW scale no unification of the 1st and 2nd generation • mix techniques: • markov flat full Parameter space • MINUIT in 5 best points • Markov flat gaugino-higgsino space • MINUIT on 15 best points • BW pdf on remaining parameters • MINUIT on 5 best solutions (all parameters) • 3 neutralino masses at LHC • M1, M2, μ • 8 fold ambiguity in Gaugino-Higgsino subspace at the LHC!

  13. MSSM18 Tilman doesn‘t like mSugra – so could we fit (much) more general models? LHC: 300 fb-1 But remember: this maybe possible in the „best of all worlds“ (=SPS1a-like point) Much less will be possible if e.g. no dilepton edge is visible

  14. MSSM Running up to the GUT scale: G. Blair, W. Porod and P.M. Zerwas (Eur.Phys.J.C27:263-281,2003) P. Bechtle, K. Desch, P. Wienemann with W. Porod (Eur.Phys.J.C46:533-544,2006) SPS1a (SPA1): dashed bands: today’s theory errors included unification measured from low energy (TeV) data from LHC+ILC remember: all results valid within a well defined model/hypothesis

  15. SFitter+Michael Duehrssen The Higgs sector Measurements Experimental Errors Thanks Gavin  Theory Errors Applying same techniques: Likelihood map with reduction of dimensions either Bayesian of Profile Likelihood (more appropriate here in absence of true secondary minima)

  16. ZH/WH full sensitivity ZH/WH half sensitivity ZH/WH absent Profile likelihood 30fb-1 theory errors

  17. Questions for discussion • Several methods to scan n-dimensional parameter space. Enough? • Agree on error treatment (esp theory errors)? • extension from SUSY model A to SUSY model B easy • Higgs sector study ok • other non-susy models? • How to communicate (spectrum/observables) without re-inventing the wheel? • Do these models Z’, UED etc need n-dimensional scan tools? • Is it a particularity of SUSY that a measurement depends on N parameters? • Is the theoretical precision sufficient everywhere? • Inclusion of exclusion? • observed rates: clearly, how to predict N (SUSY pars) for fits? how to implement in fits? • interfacing to theory codes: SLHA quite good – problems if > 1 code is used in a fit (e.g. separate RGE running in SPheno + mastercode) • fast (parametrized) rate calculator – any ideas? • stability of Markov chain MCs (dependence on start values, alg. parameters, statistics,…)

  18. A difficult example: SUSY with heavy scalars Arkani-Hamed & Dimopoulos 2004 Giudice, Romanino 2004 N. Bernal, A. Djouadi, P. Slavich JHEP 0707:016,2007 E. Turlay et al. (Proceedings BSM-SUSY les Houches 2007) • Phenomenology: • scalar Mass scale 104 to 16 GeV • scalars are at MS • fermions O(TeV) • SM Higgs h • effective theory below MS • at MS matching with complete theory • and standard RGE • DSS parameters: • MS: decoupling scale, scalar masses • m1/2: gaugino mass parameter • μ: Higgs mass parameter • At: trilinear coupling at MS • tanβ: mixing angle between Higgs at MS ATLAS/CMS talks: Paul de Jong, Tapas Sarangi, Daniel Teyssier Parameter determination at LHC possible

  19. backup: LE input

  20. backup: MSSM18 fitted model parameters

  21. backup: LHC inputs note: - all inputs based on +- recent ATLAS/CMS simulation - of course „correct“ interpretation of the edges is assumed / wrong assignment has to be tested separately

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