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36th ITEP Winter School of Physics (2008) 1 Bs  μ+μ- in LHCb PROGRAMA NACIONAL DE BECAS FPU Diego Martínez Santos (ДИЕГО МАРТИНЕЗ САНТОC) Universidade.

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Presentation on theme: "36th ITEP Winter School of Physics (2008) 1 Bs  μ+μ- in LHCb PROGRAMA NACIONAL DE BECAS FPU Diego Martínez Santos (ДИЕГО МАРТИНЕЗ САНТОC) Universidade."— Presentation transcript:

1 36th ITEP Winter School of Physics (2008) 1 Bs  μ+μ- in LHCb PROGRAMA NACIONAL DE BECAS FPU Diego Martínez Santos (ДИЕГО МАРТИНЕЗ САНТОC) Universidade de Santiago de Compostela (USC)

2 36th ITEP Winter School of Physics (2008) 2 Motivation LHCb conditions Bs  μμ search strategy Exclusion/discovery potential of LHCb SUSY implications examples ?

3 36th ITEP Winter School of Physics (2008) 3 Motivation: BR (Bs  μμ ) sensitive to New Physics (NP) SM:(3.55 ± 0.33)x10 -9BR (Bs  μμ ) in SM: (3.55 ± 0.33)x10 -9 (for Bd : (1.00 ± 0.14)x10 -10 ) BR< 4.7x10 -8 @ 90% C.L,Current limit (Tevatron) : BR< 4.7x10 -8 @ 90% C.L, (for Bd :BR < 1.5x10 -8 )  One order of magnitude from current upper limit to SM prediction !!

4 36th ITEP Winter School of Physics (2008) 4 Motivation: BR (Bs  μμ ) sensitive to New Physics (NP) + ? This BR can be affected by New Physics, in particular SUSY models. MSSM: Enhancement ~tanβ6  larger value Measurement/exclusion of BR (Bs  μμ ) implies constraints on NP (SUSY) parameters !!  Measurement/exclusion of BR (Bs  μμ ) implies constraints on NP (SUSY) parameters !!

5 36th ITEP Winter School of Physics (2008) 5 10 -7 2x10 -8 5x10 -9 Non Universal Higgs Masses (NUHM) J.Ellis et. al. CERN-PH-TH/2007-138 [arXiv:0709.0098v1 [hep-ph] ] (2007) J.Ellis et. al. CERN-TH/2002-81 [arXiv:hep-ph/0204192] (2002) Simliar to CMSSM, but avoiding the condition of universal Higgs masses  generalization of CMSSM useful for study the Higgs sector Best Chi2 of SUSY fit  BR ~ 10 -8

6 36th ITEP Winter School of Physics (2008) 6 Maximal CP violation Minimal Flavor Violation (MCPMFV)-MSSM [i] [i] J.Ellis et. al. CERN-PH-TH/2007-136 [ arXiv:0708.2079v1 [hep-ph] ] (2007) Even < than SM !! m1/2 = 250 GeV m0 = 100 GeV A0 = 100 GeV Φ AGUT = 0 FCNC’s mediated only by CKM (i.e., vanish for CKM = I 3 ) Then, added maximum number of CP violating phases Useful for CPV studies in SUSY Enhancement for large tanβ BR < SM allowed, (special case of cancellation between SM contribution and NP pseudoscalar contribution)

7 36th ITEP Winter School of Physics (2008) 7 LHCb conditions b physics experiment Low angle spectrometer, but at center of masses b produced at low angle ~5 x 10 11 bb/fb -1 (  ~ 10 12 per n.year) Trigger dedicated to select b events ( ~90% for reconstructed Bs  μμ) Total number of Bs  μμ events ~45 per n.y (if SM BR) (Interaction Point)

8 36th ITEP Winter School of Physics (2008) 8 Bs  μμ search strategy (I) Reconstruction of μ+ μ- combinations on triggered events Selection: apply some cuts on discriminant variables to remove the most important amount of background Classify each event using three properties (bins in a 3D phase space): Particle Identification (PID): Probability to be muons Geometrical properties Invariant Mass Get the number of expected bkg. in the Bs mass region using sidebands (events outside ±60 MeV of Bs Mass) Use of control channels to get the probability, for a signal event, to fall in each bin:  Geometry & Mass: Use of B  h+h-  PID: Calibration muons (MIPs in calorimeter, J/Ψ muons) Geometry Invariant Mass PID

9 36th ITEP Winter School of Physics (2008) 9 Compare expected bkg and signal with observed  No. of observed signal events or compatibility with only bkg. Translate No. of evts  BR using a known control channel (B+  J/Ψ K+)  (= normalization) Bs  μμ search strategy (II) Hadronization fractions ( fBs = P(b  Bs) ) Main source of uncertainty (13 %) for normalization with B+  J/Ψ K+ (or any other B+/Bd channel) fraction of reconstruction / selection & trigger efficiencies

10 36th ITEP Winter School of Physics (2008) 10 Bs  μμ Event Selection A (almost) common selection for Bs  μμ, B+  J/Ψ K+& B  h+h- is used: Avoid different biases in geometrical properties for B  h+h- & Bs  μμ Reduces uncertainties in the fraction of selection efficiencies when computing BR Uses basicaly B hadron properites & 2 track vertex properties Also IPS of muons (Bs  μμ), hadrons (B  hh) or kaon (B+  J/ΨK+) Some (necessary) differences : J/Ψ invariant mass cut (B+  J/ΨK+), hits in muon chambers (Bs  μμ, B+  J/Ψ (  μμ)K+) Cut Eff (B+  J/Ψ K+) B mass(500 MeV)97.3 % JPsi Chi2 < 1498.3 % B ips < 697.4 % DOFS(JPsi) > 1274.0 % Bpt > 700 MeV98.4 % K+ IPS > 3.592.1 % JPsiMass (60 MeV)94.2 % Cut Eff (Bs  μμ) B mass(600 MeV)97.3 % BsChi2 < 1497.9 % B ips < 698.8 % DOFS(Bs) > 1275.5 % Bs pt > 700 MeV97.7 % mu+,mu- IPS > 3.593.0 %

11 36th ITEP Winter School of Physics (2008) 11 Geometrical Variables lifetime muon Impact Parameter Significant (IPS) DOCA: distance between tracks making the vertex B Impact Parameter (IP ) to PV Isolation: Idea: muons making fake Bs→μμ might came from another SV’s  For each muon; remove the other μ and look at the rest of the event: How many good - SV’s (forward, DOCA, pointing) can it make? (arbitrary normalization) Red: signal Blue: bb inc. Black: b  μ b  μ Green: Bc+  J/Ψμν less μIPS Bs IP (mm) Isolation lifetime (ps) DOCA (mm)

12 36th ITEP Winter School of Physics (2008) 12 Geometrical Likelihood Those variables combined in a likelihood (procedure in backup slides) Geometry Likelihood (GL) for signal (& B  h+h-) is flat from 0 to 1 Bkg peaks at 0 High discriminant power Sensitive region (SR): ~ GL > 0.5, (& Invariant mass inside ± 60 MeV window) (arbitrary normalization) SR Expected events per nominal LHCb year in SR: Signal (SM) : 22.8 Bkg: 150 Note: 22.8/ sqrt(150) = 1.9

13 36th ITEP Winter School of Physics (2008) 13 “One day” experiment Each Bin : (Bkg Expected – Observed)/ max(1, sqrt(Observed) ) SR Example of ~one nominal day experiment (full simulation of bb  dimuon, only bkg) Expected bkg from sidebands 0 events observed in SR Nsig < 4.5 @ 95 % CL Nsig < 3.5 @ 90% CL  BR < 1.2 x 10 -7 @ 95 % CL  BR < 9.4 x 10 -8 @ 90 % CL

14 36th ITEP Winter School of Physics (2008) 14 LHCb potential BR excluded at 90 % CL Expected limit at the end of Tevatron BR observed at 3σ Note: 1 nominal year = 2 fb-1

15 36th ITEP Winter School of Physics (2008) 15 BR excluded at 90 % CL Expected limit at the end of Tevatron LHCb potential 10 -7 2x10 -8 5x10 -9

16 36th ITEP Winter School of Physics (2008) 16 mSUGRA-implications example CMSSM parameter values chosen: m 1/2 in [0, 1400 GeV] m 0 in [0,1400 GeV] A 0 = 0 μ >0 Other constraints: h0 > 114 GeV mW = 80.398 ± 0.025 GeV calculations using the program SoftSUSY from Ben Allanch (Cambridge) ; BR’s computed using program from Athanasios Dedes (Durham )

17 36th ITEP Winter School of Physics (2008) 17 In case of Bs  μμ Observation mSUGRA Phase Space is strongly reduced as function of the BR seen (and its accuracy) BR ~ 3x10 -9 Phase Space region compatible with BR(Bs  μμ) ~ 1x10 -8 BR observed at 3σ

18 36th ITEP Winter School of Physics (2008) 18 Backup Slides

19 36th ITEP Winter School of Physics (2008) 19 Correlation for signal (very small for background) signal independent Gaussian variables (for signal) signal independent Gaussian variables (for background)  Same procedure making a 2D Gaussian for Background

20 36th ITEP Winter School of Physics (2008) 20

21 36th ITEP Winter School of Physics (2008) 21 CMSSM and relation with g-2 CMSSM: Relation with Muon Anomalous Magnetic Dipole Moment a μ = (g -2)/2 Current value of aμ - aμ(SM)  if tanß ~ 50 gaugino mass are in ~400 – 600 GeV  BR(Bs  μμ) ~ 1-4 x 10 -8 Sensitive to several other models aμ - aμ(SM) BR (Bs  μμ )

22 36th ITEP Winter School of Physics (2008) 22

23 36th ITEP Winter School of Physics (2008) 23 Method for variable-combination For constructing Geometry & PID likelihoods, we have made some operations over the input variables. Trying to make them uncorrelated A very similar method is described by Dean Karlen, Computers in Physics Vol 12, N.4, Jul/Aug 1998 The main idea: s1 s2 s3. sn b1 b2 b3. bn x1 x2 x3. xn n input variables (IP, DOCA…)  n variables which, for signal, are independent and Gaussian (sigma 1) - distributed  χ 2 S = Σ s i 2  same, but for background  χ 2 B = Σ b i 2 χ 2 = χ 2 S - χ 2 B And make it uniform for signal (  flat distribution)


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