1. Search for FCNC Decays Bs(d) → μ+μ-
Motivation
Tevatron and CDF
Analysis Method
Results
Conclusion
Matthew Herndon, May 2006
University of Wisconsin
Carnegie Mellon University High Energy Physics Seminar
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
Predictions of the Standard Model have been verified to high precision
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3. Beyond the Standard Model
Why look for physics beyond the Standard Model
Standard Model fails to answer many fundamental questions
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
Look for new physics that would explain these mysteries:
SUSY, Extra Dimensions ...
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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
A unique window of opportunity to find new physics before the LHC
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5. Bs → μμ: Beyond the SM Look at decays that are suppressed in the
Standard Model: Bs(d) → μ+μ-
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) → μ+μ-) = 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 tan6β/(MA)4
Babu and Kolda, Phys. Rev. Lett. 84, 228
Tree: R-Parity violating SUSY
One of the best indirect search channels at the Tevatron
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6. CDF and the Tevatron - 1.96TeV pp collider CDF Integrated Luminosity
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
Bs(d) → μ+μ- benefits from new data
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7. CDF Detector Silicon Tracker Drift Chamber(COT) Muon coverage
|η|<2, 90cm long, rL00 = cm
Drift Chamber(COT)
96 layers between 44 and 132cm
Muon coverage
|η|<1.5
Triggered to |η|<1.0
Outer chambers: high purity muons
EXCELLENT TRACKING
TRIGGERED TO 1.5 GeV/c
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8. Rare B Decay Physics & Triggers
Large production rates
σ(pp → bX, |y| < 1.0, pT(B) > 6.0GeV/c) = ~30μb, ~10μb
All heavy b states produced
B0, B+, Bs, Bc, Λb, Ξb
Backgrounds: > 3 orders of magnitude higher
Inelastic cross section ~100 mb
Challenge is to pick one B decay from >103 other QCD events
Di-muon trigger
pT(μ) > ~1.5 GeV/c, |η| < ~1.0
B yields 2x Run I (lowered pT threshold, increased acceptance)
Di-muon triggers for rare decay physics
Bs(d) → μ+μ-, B+ → μ+μ-K+, B0 → μ+μ-K*0, Bs → μ+μ-φ, Λb→ μ+μ-Λ
Trigger on di-muon masses from near 0GeV/c2 to the above Bs mass
Reduce rate by requiring outer muon chamber hits: pT(μ) > ~3.0 GeV/c or pTμ > 5.0GeV/c -
TRIGGERS ARE CRITICAL
1 Billion B and Charm Events on Tape
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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
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10. Analysis Method Performing this measurement requires that we
Demonstrate an understanding of the background
Optimize the cuts to reduce background
Accurately estimate α and ε: 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
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11. Data Sample Start the with di-muon trigger Apply basic quality cuts
2 CMU muons or 1 CMU and 1CMX muon with pTμ > 5.0GeV/c
CMU: pT(μ) > ~1.5 GeV/c, |η| < ~0.6, CMX: pT(μ) > ~2.0 GeV/c, 0.6 < |η| < 1.0
1 CMUP muon: pT(μ) > ~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(μ+μ-) > 4.0 GeV/c, 3D Decay length significance > 2 …
In the mass region around the Bs: < Mμμ < GeV/c2
Blind region: 4σ(Mμμ), < Mμμ < GeV/c2
Sideband region 0.5 GeV/c2 on either side of the blinded region
Sample still background dominated
Expect < 10 Bs(d) → μ+μ- events to pass these cuts: based on previous limits
100M Events
23K Events
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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) → μ+μ-
9.8 X 107 B+ events
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13. Signal vs. Background - + PT() - L3D + PT() - LT
Need to discriminate signal from background
Reduce background by a factor of > 1000
Signal characteristics
Final state fully reconstructed
Bs is long lived (cτ = 438 μm)
B fragmentation is hard: few additional tracks
Background contributions and characteristics
Sequential semi-leptonic decay: b → cμ-X → μ+μ-X
Double semileptonic decay: bb → μ+μ-X
Continuum μ+μ-
μ + fake, fake+fake
Partially reconstructed, short lived, has additional tracks + -
L3D
primary vertex
di-muon vertex
PT()
23K Events LT
primary vertex
di-muon vertex + -
PT() -
Cut on mass, lifetime, how well pT points to the vertex and isolation
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14. Discriminating Variables
4 primary discriminating variables
Mass Mmm
choose 2.5σ window: σ = 25MeV/c2
exp(λ=cτ/cτBs)
α : |φB – φvtx| in 3D
Isolation: pTB/( trk + pTB)
exp(λ), α 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
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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
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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
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17. Background Estimate
Analysis dependent on an accurate 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
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18. Predicted Background CMU-CMX
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
LH cut
Predicted Background
CMU-CMU
Predicted Background CMU-CMX
LH > 0.90
13.3±1.3
10.4±1.1
LH > 0.98
1.0±0.5
1.0±0.3
LH > 0.99
0.7±0.3
0.4±0.2
Optimal point looks achievable
Cross-checks needed!
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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 > 0.5
Medium: LR > 0.9
Tight: LR > 0.99 + -
primary vertex
Fake di-muon
vertex
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20. Bg. Cross-Checks Cont. - OS+ vs. OS- OS+ vs. SS+


[cm]
For >0:
hist: OS
pts: SS
OS- Excellent control sample:
SS and fake muon could represent some bb backgrounds -
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21. Bg. Cross-Checks Cont. OS- and Fake μ samples
SS+,SS-: expect and find 0 events for tighter cuts
OS-
Predicted
Observed
Probability
LR > 0.50
840±16
821
28%
LR > 0.90
118±6
136 7%
LR > 0.99
8.7±1.9 17 2%
SS- LR > 0.50
8.7±1.7 6
27%
SS+ LR > 0.50
11.0±1.8 12
40%
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 FM
Predicted
Observed
Probability
LR > 0.50
221±8
196 7%
LR > 0.90
40±3 29 6%
LR > 0.99
5.1±1.1 9
12%
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22. B  hh Background Clearly peaks in signal region
Sideband estimates not useful
Observed/measured at CDF
Mode
N Bs Mass Window
N Bd Mass Window
Bd  
0.008
0.095
Bd  K
0.005
0.55
Bd  KK
Bs  
0.035
0.006
Bs  K
0.048
0.061
Bs  KK
0.089
0.66
Total
0.19±0.06
1.37±0.16
Convolute known branching ratios and acceptance with K and  fake rates. (estimated for LH > 0.99)
B  hh background substantial!
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23. Acceptance & Efficiencies
From MC
From MC:
Checked with Data
From Data
From Data
Second critical part of the analysis is estimating the efficiencies
: efficiency - from data where possible
Using J/ψ → μ+μ- and B+ → J/ψK+ data and special unbiased J/ψ triggers
Most efficiencies relative: Exception - and LH and K
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24. Example: SVX Efficiency
Measurement of the efficiency of adding silicon hits to COT tracks
Use J/ψ → μ+μ- data
( data)
( data)
Very low and went down with pT and time!
εSVX = 74.5 ± 0.3(stat) ± 2.2(sys)%( data)
Average efficiency for adding silicon to two tracks: In silicon fid.
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25. New SVX Efficiency
Improved silicon pattern recognition code and detector performance
+2% and flat - improved code
+5% εSVX improved code and new quality cuts
old avg (88%)
w/ same data
(2003)
εSVX = 74.5% → 88.5%
→ data
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26. LR Efficiency Cross-Check
Efficiencies determined using realistic MC
MC efficiencies validated by comparing with data using B+ → J/ψK+ events
Pt and isolation distributions reweighed to match data Same treatment for Bs
LR Cut
eLR(data)
eLR(MC)
Ratio
LR>0.90
0.70±0.01
0.71±0.01
0.98
LR>0.92
0.68±0.01
1.00
LR>0.96
0.60±0.01
0.61±0.01
LR>0.98
0.52±0.01
0.53±0.01
LR>0.99
0.41±0.01
0.43±0.01
0.96
Agreement good
4% systematic uncertainty assigned
5% from isolation reweighing
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27. Final Efficiencies & Acceptances
LR efficiencies determined using realistic MC
Pt and isolation distributions reweighed to match data using Bs  J/ events
Very good efficiency for large background rejection
LR Cut
eLR(CMUCMU)
eLR(CMUCMX)
LR>0.90
0.65±0.01
0.68±0.01
LR>0.95
0.57±0.01
0.61±0.01
LR>0.98
0.47±0.01
0.49±0.01
LR>0.99
0.37±0.01
0.41±0.01
For LR > 0.99
39% efficiency, 7% sys
~Factor 2000 of rejection
Acceptance ratio:
a(B+/Bs) = ± (CMUCMU), ± (CMUCMX): 7% sys - generation model
Most efficiency ratios near 1
Two absolute efficiencies:
K+COT = 95% and K+SXV = 96%
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28. Combinatoric Background
Optimization Results
Tried Likelihood ratios from : LR Cut 0.99
Optimized for 1fb-1
NB+: 5763 ± 101
εLR: 39%
Backgrounds:
Systematic uncertainties
Bg. estimation: 28%
Bg statistical error
Sensitivity: 16%
12% fs/fu
7% acceptance
7% LR
Decay
Bhh Background
Combinatoric Background
Total Background Bs
0.19±0.06
1.08±0.36
1.27±0.36 Bd
1.37±0.16
2.45±0.39
a priori Bs limit: 1.1x % CL
Previous CDF result 2.0x10-7
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29. Bs(d) → μ+μ- Search Result
Decay
Total Background
Observed Bs
1.27±0.36 1 Bd
2.45±0.39 2
Results: 1(2) Bs(d) candidates observed consistent with background expectation
BF(Bs  +- ) < 1.0x10-7 at 95% CL
BF(Bd  +- ) < 3.0x10-8 at 95% CL
Worlds Best Limits!
CDF 1 Bs result: 10-6
CDF Bs result: 365pb-1 2.010-7
D0 Bs result: 240pb 10-7
BaBar Bd result: 10-8(90%)
PRL 95,
DO Note 4733-Conf
PRL 94,
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30. Looks like the theorists
Bs → μμ: Physics Reach
BF(Bs  +- ) < 1.0x10-7 at 95% CL
Excluded at 95% CL
BF(Bs  +- ) = 1.0x10-7
Dark matter constraints
Looks like the theorists
have a little work to do!
R. Dermisek JHEP 0304 (2003) 037
R. Dermisek et al.hep-ph/
Strongly limits specific SUSY models: SUSY SO(10) models
Allows for massive neutrino
Incorporates dark matter results
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31. Bs → μμ: Physics Reach
In addition to limiting S0(10) models – starting to impact standard MSSM scenarios: CMSSM
Blue BF(Bs → μ+μ-)
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,
BF(Bs  +- ) < 1.0x10-7 at 95% CL
BF(Bs  +- ) = 2.0(1.0)x10-7
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32. Conclusions CDF B(s,d) → μ+μ- results
BF(Bs  +- ) < 1.0x10-7 at 95% CL
BF(Bd  +- ) < 3.0x10-8 at 95% CL
Worlds Best Limits!
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)
Goal for 1fb-1 analysis: BF(Bs  +- ) < 7.0x10-8 at 95% CL
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33. Muon & Trigger Efficiencies
L1 eff: Use J/ψ → μ+μ- trigger that only requires one muon
L3 eff: Use autoexcept trigger
L1 Muon L1(pT,η), L3 1 number
Convolute εL1 for each muon and εL3
Systematic errors L1 and L3:
Kinematic difference between J/ψ → μ+μ- and Bs(d) → μ+μ-
2-Track correlations
Sample statistics
εtrig = 85 ± 3%
Offline muon reconstruction
From J/ψ → μ+μ- L1 trigger with one muon found
Systematic from comparison to Z
εmuon = 95.9 ±1.3(stat) ± 0.6(sys)%
trigger efficiency (%)
PT (GeV)
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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
εCOT = ± 0.02%
Systematic errors
Isolation dependence dominant systematic
Pt dependence
2-track correlations
Varying the simulation tuning
Error:
Consistent with true tracking eff. using high pt elections identified in the calorimeter
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35. Bs → μμ: Physics Reach
In addition to limiting S0(10) models – starting to impact standard MSSM scenarios: mSugra
Blue BF(Bs → μ+μ-)
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,
BF(Bs  +- ) < 1.0x10-7 at 95% CL
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