Search for FCNC Decays Bsd

1
Search for FCNC Decays Bs(d) ? µµ-

• Motivation
• Tevatron and CDF
• Analysis Method
• Results
• Conclusion

BEACH 04
J. Piedra
1
2
The Standard Model

• Successes of the Standard Model
• Simple comprehensive theory
• Explains the hundreds of common particles atoms,
  protons, neutrons, electrons
• Explains the interactions between them
• Basic building blocs
• 6 quarks up, down...
• 6 leptons electrons...
• Force carrier particles photon...

• All common matter particles are composites of the
  quarks leptons which interact by the exchange
  of force carrier particles

2
M. Herndon
CMU Seminar
3
Beyond the Standard Model

• Why look for physics beyond the Standard Model

• Gravity not a part of the SM
• What is the very high energy behaviour?
• At the beginning of the universe?
• Grand unification of forces?
• Where is the Antimatter?
• Why is the observed universe mostly matter?
• Dark Matter?
• Astronomical observations of gravitational
  effects indicate that there is more matter than
  we see

3
M. Herndon
CMU Seminar
4
Searches For New Physics

• How do you search for new physics at a collider?
• Direct searches for production of new particles
• Particle-antipartical annihilation
• Example the top quark
• Indirect searches for evidence of new particles
• Within a complex decay new particles can occur
  virtually

• Tevatron is at the energy frontier and
• a data volume frontier 1 billion B and Charm
  events on tape
• So much data that we can look for some very
  unusual decays
• Where to look
• Many weak decays of B and charm hadrons are very
  low probability
• Look for contributions from other low probability
  processes Non Standard Model

4
M. Herndon
CMU Seminar
5
Bs ? µµ Beyond the SM

• Look at decays that are suppressed in the
• Standard Model Bs(d) ? µµ-
• Flavor changing neutral currents(FCNC) to leptons
• No tree level decay in SM
• Loop level transitions suppressed
• CKM , GIM and helicity(ml/mb) suppressed
• SM BF(Bs(d) ? µµ-) 3.5x10-9(1.0x10-10)
• G. Buchalla, A. Buras, Nucl. Phys. B398,285
• New physics possibilities
• Loop MSSM mSugra, Higgs Doublet
• 3 orders of magnitude enhancement
• Rate ?tan6ß/(MA)4
• Babu and Kolda, Phys. Rev. Lett. 84, 228
• Tree R-Parity violating SUSY

5
M. Herndon
CMU Seminar
6
CDF and the Tevatron
-

• 1.96TeV pp collider
• Performance substantially improving each year
• Record peak luminosity in 2006 1.8x1032sec-1cm-2

• CDF Integrated Luminosity
• 1fb-1 with good run requirements
• All critical systems operating including silicon
• Analysis presented here uses 780pb-1
• Doubled data in 2005, predicted to double again
  in 2006

6
CMU Seminar
7
CDF Detector

• Silicon Tracker
• ?lt2, 90cm long, rL00 1.3 - 1.6cm
• Drift Chamber(COT)
• 96 layers between 44 and 132cm

• Muon coverage
• ?lt1.5
• Triggered to ?lt1.0
• Outer chambers high purity muons

7
CMU Seminar
8
Rare B Decay Physics Triggers

• Large production rates
• s(pp ? bX, y lt 1.0, pT(B) gt 6.0GeV/c) 30µb,
  10µb
• All heavy b states produced
• B0, B, Bs, Bc, ?b, ?b
• Backgrounds gt 3 orders of magnitude higher
• Inelastic cross section 100 mb
• Challenge is to pick one B decay from gt103 other
  QCD events
• Di-muon trigger
• pT(µ) gt 1.5 GeV/c, ? lt 1.0
• B yields 2x Run I (lowered pT threshold,
  increased acceptance)
• Di-muon triggers for rare decay physics
• Bs(d) ? µµ-, B ? µµ-K, B0 ? µµ-K0, Bs ?
  µµ-f, ?b? µµ-?
• Trigger on di-muon masses from near 0GeV/c2 to
  the above Bs mass
• Reduce rate by requiring outer muon chamber hits
  pT(µ) gt 3.0 GeV/c or
  ?pTµ gt 5.0GeV/c

-
8
M. Herndon
9
Bs ? µµ Experimental Challenge

• Primary problem is large background at hadron
  colliders
• Analysis and trigger cuts must effectively reduce
  the large background around mBs
  5.37GeV/c2 to find a possible handful of events
• Key elements of the analysis are determining the
  efficiency and rejection of the discriminating
  variables and estimating the background level

9
M. Herndon
CMU Seminar
10
Analysis Method

• Performing this measurement requires that we
• Demonstrate an understanding of the background
• Optimize the cuts to reduce background
• Accurately estimate a and e for triggers,
  reconstruction and selection cuts
• SM predicts 0 events, this is essentially a
  search
• Emphasis on accurately predicting the Nbg and
  performing an unbiased optimization
• Blind ourselves to the signal region
• Estimate background from sidebands and test
  background predictions in orthogonal samples

10
M. Herndon
CMU Seminar
11
Data Sample

• Start the with di-muon trigger
• 2 CMU muons or 1 CMU and 1CMX muon with ?pTµ gt
  5.0GeV/c
• CMU pT(µ) gt 1.5 GeV/c, ? lt 0.6, CMX pT(µ) gt
  2.0 GeV/c, 0.6 lt ? lt 1.0
• 1 CMUP muon pT(µ) gt 3.0 GeV/c and 1 CMU(X) muon
• Apply basic quality cuts
• Track, vertex and muon quality cuts
• Loose preselection on analysis cuts
• PT(µµ-) gt 4.0 GeV/c, 3D Decay length
  significance gt 2
• In the mass region around the Bs 4.669 lt Mµµ lt
  5.969 GeV/c2
• Blind region 4s(Mµµ), 5.169 lt Mµµ lt 5.469 GeV/c2
• Sideband region 0.5 GeV/c2 on either side of the
  blinded region
• Sample still background dominated
• Expect lt 10 Bs(d) ? µµ- events to pass these
  cuts based on previous limits

11
M. Herndon
CMU Seminar
12
Normalization Data Sample

• Normalize to the number of B ?J/?K
  events
• Relative normalization analysis
• Some systematic uncertainties will cancel out in
  the ratios of the normalization
• Example muon trigger efficiency the same for J/?
  or Bs muons for a given pT
• Apply same sample selection criteria as for Bs(d)
  ? µµ-

12
M. Herndon
13
Signal vs. Background

• Need to discriminate signal from background
• Reduce background by a factor of gt 1000
• Signal characteristics
• Final state fully reconstructed
• Bs is long lived (ct 438 µm)
• B fragmentation is hard few additional tracks
• Background contributions and characteristics
• Sequential semi-leptonic decay b ? cµ-X ? µµ-X
• Double semileptonic decay bb ? µµ-X
• Continuum µµ-
• µ fake, fakefake
• Partially reconstructed, short lived,
  has additional tracks

-
13
M. Herndon
CMU Seminar
14
Discriminating Variables

• Mass Mmm
• choose 2.5s window s 25MeV/c2
• exp(?ct/ctBs)
• ?a fB fvtx in 3D
• Isolation pTB/( ?trk pTB)
• exp(?), ?a and Iso used in
  likelihood ratio
• Optimization
• Unbiased optimization
• Based on simulated signal and data sidebands
• Optimize based on likely physics result
• a priori expected BF limit

• 4 primary discriminating variables

14
M. Herndon
CMU Seminar
15
CDF 3D Silicon Vertex Detector

• 8 layer silicon detector
• 4 double sided layers with stereo strips at a
  small angle
• 3 double sided layers layers with strips at 90
• 3D vertexing is a powerful discriminant

• 2D ?? from previous analysis

15
M. Herndon
CMU Seminar
16
Likelihood Ratio

• Probability distributions used to construction
  the likelihood ratio
• Use binned histograms to estimate the
  probabilities

• Resulting likelihood with signal MC and data
  sidebands

16
M. Herndon
CMU Seminar
17
Background Estimate

• Estimate the background for any given set of
  requirements
• Typical method apply all cuts, see how many
  sideband events pass
• Optimal cuts may be chosen to reject 1-2 unusual
  events
• Flat background distribution allows use of wide
  mass window

• Need to show that mass is
  distribution
  is linear
• Need to investigate backgrounds
  that
  may peak in signal region B ? hh
• Design crosschecks to double check background
  estimates

17
M. Herndon
CMU Seminar
18
Background Estimate

• Expected number of combinatorial background in a
  60 MeV/c2 signal window
• Extrapolation from sideband
• 0.99 expected to be near optimal cut, 1
  background event typical of optimal search

18
M. Herndon
CMU Seminar
19
Background Cross-Checks

• Use independent data samples to test background
  estimates
• OS- opposite sign muons, negative lifetime
  (signal sample is OS)
• SS and SS- same sign muons,
  positive and negative lifetime
• FM OS- and OS fake µ enhanced,
  one µ fails the muon
  reconstruction quality cuts
• Compare predicted vs. observed of bg. events
  3 sets of cuts
• Loose LR gt 0.5
• Medium LR gt 0.9
• Tight LR gt 0.99

19
M. Herndon
CMU Seminar
20
Bg. Cross-Checks Cont.
OS vs. OS-
OS vs. SS

• OS- Excellent control sample
• SS and fake muon could represent some bb
  backgrounds

-
20
M. Herndon
CMU Seminar
21
Bg. Cross-Checks Cont.

• OS- and Fake µ samples
• SS,SS- expect and find 0 events for tighter cuts

• Using 150 MeV/c2 mass window for cross-check
• Combined CMU-CMU(X)
• Think about results with tight cuts and FM
• Investigated B ? hh carefully
• Generated inclusive MC to look for anomalous
  background sources - no surprises found

21
M. Herndon
CMU Seminar
22
B ? hh Background

• Clearly peaks in signal region
• Sideband estimates not useful
• Observed/measured at CDF

• Convolute known branching ratios and acceptance
  with K and ? fake rates. (estimated for LH gt 0.99)

22
M. Herndon
CMU Seminar
23
Acceptance Efficiencies

• ? efficiency - from data where possible
• Using J/? ? µµ- and B ? J/?K data and special
  unbiased J/? triggers
• Most efficiencies relative Exception - and ?LH
  and ?K

23
M. Herndon
CMU Seminar
24
Example SVX Efficiency

• Measurement of the efficiency of adding silicon
  hits to COT tracks
• Use J/? ? µµ- data

(2001-2002 data)
(2001-2002 data)

• eSVX 74.5 0.3(stat) 2.2(sys)(2001-2003
  data)
• Average efficiency for adding silicon to two
  tracks In silicon fid.

24
M. Herndon
25
New SVX Efficiency

• Improved silicon pattern recognition code and
  detector performance

2 and flat - improved code 5 eSVX improved
code and new quality cuts
eSVX 74.5 ? 88.5 2001-2003 ? 2001-2005 data
25
M. Herndon
CMU Seminar
26
LR Efficiency Cross-Check

• Efficiencies determined using realistic MC
• MC efficiencies validated by comparing with data
  using B ? J/?K events
• Pt and isolation distributions
  
  reweighed to match data
  
  Same treatment for Bs

• 4 systematic uncertainty assigned
• 5 from isolation reweighing

26
M. Herndon
CMU Seminar
27
Final Efficiencies Acceptances

• LR efficiencies determined using realistic MC
• Pt and isolation distributions reweighed to match
  data using Bs ? J/?? events

• For LR gt 0.99
• 39 efficiency, 7 sys
• Factor 2000 of rejection

• Acceptance ratio
• a(B/Bs) 0.297 0.006 (CMUCMU), 0.191 0.008
  (CMUCMX) 7 sys - generation model
• Most efficiency ratios near 1
• Two absolute efficiencies
• ?KCOT 95 and ?KSXV 96

27
M. Herndon
CMU Seminar
28
Optimization Results

• Tried Likelihood ratios from 0.9-0.99 LR Cut 0.99

• Systematic uncertainties
• Bg. estimation 28
• Bg statistical error
• Sensitivity 16
• 12 fs/fu
• 7 acceptance
• 7 LR

• NB 5763 101
• eLR 39
• Backgrounds

28
M. Herndon
CMU Seminar
29
Bs(d) ? µµ- Search Result

• Results 1(2) Bs(d) candidates observed
  consistent with background expectation

• CDF 1 Bs result 3.0?10-6
• CDF Bs result 365pb-1 2.0?10-7
• D0 Bs result 240pb-1 4.1?10-7
• BaBar Bd result 8.3?10-8(90)

PRL 95, 221805 2005
DO Note 4733-Conf
PRL 94, 221803 2005
29
M. Herndon
CMU Seminar
30
Bs ? µµ Physics Reach

• Excluded at 95 CL
• BF(Bs ? ??- ) 1.0x10-7
• Dark matter constraints

R. Dermisek et al.hep-ph/0507233
R. Dermisek JHEP 0304 (2003) 037

• Strongly limits specific SUSY models SUSY SO(10)
  models
• Allows for massive neutrino
• Incorporates dark matter results

30
M. Herndon
CMU Seminar
31
Bs ? µµ Physics Reach

• In addition to limiting S0(10) models starting
  to impact standard MSSM scenarios CMSSM
• Blue BF(Bs ? µµ-)
• Light green b ? s?
• Cyan dark matter constraints
• Dashed red Higgs mass bound
• Red brown areas excluded
• Incorporates errors from fBs and mtop

J. Ellis Phys. Lett. B624, 47 2005
BF(Bs ? ??- ) 2.0(1.0)x10-7
31
M. Herndon
CMU Seminar
32
Conclusions
CDF B(s,d) ? µµ- results

• Best Bs and Bd results well ahead of D0 and the
  B factories
• Limit excludes part of parameter space allowed by
  SO(10) models
• Expanding sensitivity to interesting areas of
  MSSM parameter space
• Improving analysis to include selection NN and
  advanced lepton ID(from CMU Vivek)
• Improved fs results could also improve result by
  10-20(from CMU Karen)

M. Herndon
32
CMU Seminar
33
Muon Trigger Efficiencies

• L1 eff Use J/? ? µµ- trigger that only requires
  one muon
• L3 eff Use autoexcept trigger

• L1 Muon L1(pT,?), L3 1 number
• Convolute eL1 for each muon and eL3
• Systematic errors L1 and L3
• Kinematic difference between J/? ? µµ-
  and Bs(d) ? µµ-
• 2-Track correlations
• Sample statistics
• etrig 85 3
• Offline muon reconstruction
• From J/? ? µµ- L1 trigger with one muon found
• Systematic from comparison to Z
• emuon 95.9 1.3(stat) 0.6(sys)

33
M. Herndon
34
COT Efficiency

• Estimated by embedding COT hits from MC muons in
  real data
• Occupancy effects correctly accounted for
• Efficiency driven by loss of hits when hit
  density is high
• Demonstrate agreement between hit usage in data
  and MC
• Tunable parameters can lower or raise the hit
  usage and the efficiency to bracket the data

• eCOT 99.62 0.02
• Systematic errors
• Isolation dependence dominant systematic
• Pt dependence
• 2-track correlations
• Varying the simulation tuning
• Error 0.34 -0.91
• Consistent with true tracking eff. using high pt
  elections identified in the calorimeter

34
M. Herndon
35
Bs ? µµ Physics Reach

• In addition to limiting S0(10) models starting
  to impact standard MSSM scenarios mSugra
• Blue BF(Bs ? µµ-)
• Light green b ? s?
• Cyan dark matter constraints
• Dashed red Higgs mass bound
• Red brown areas excluded
• Incorporates errors from fBs and mt

J. Ellis Phys. Lett. B624, 47 2005
35
M. Herndon