bs mixing at tevatron - tu dresden main injector and recycler runi: 1992 – 1996 data taking period...
TRANSCRIPT
Bs mixing results from Tevatron
Ulrich Kerzel, University of Karlsruhe
Institutsseminar, Dresden, 18th May 2006
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B mixing overview⎛⎜⎝ d0s0b0
⎞⎟⎠ =⎛⎜⎝ Vud Vus VubVcd Vcs VcbVtd Vts Vtb
⎞⎟⎠ ·⎛⎜⎝ dsb
⎞⎟⎠Mass eigenstates differ from eigenstates of weak interaction ⇒ CKM matrix
¯̄̄B0
E=
1√2(|BHi+ |BLi)¯̄̄
B̄0E=
1√2(|BHi− |BLi)
Neutral B meson system:decaying 2-component system
Small mass difference: Δ m = mH – mL
“heavy”/”light” state evolve differently with time
Mixing via box diagrams:
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B mixing overview
Δmq ∝ mBqB̂Bqf2Bq¯̄̄VtbV
∗tq
¯̄̄2From theory:
Uncertainties cancel in ratio: ΔmsΔmd
∝ |Vts|2|Vtd|2
w/o CDF Δ ms measurement
From CKM fit (EPS 2005):
Δms= 18.3+6.5−1.5 ps
−1
measure Δ ms:• determine ratio of CKM elements• probe for new physics
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B mixing overviewexperimentally accessible:
need to:• reconstruct Bs
(including decay vertex/lifetime)• tag flavour at production and decay
challenges:• momentum resolution• vertex resolution• tagging power
A(t) =Nmix(t)−Nunmix(t)Nmix(t)+Nunmix(t)
∝ cos(Δmqt)
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B mixing overviewFigure of merit: significance of mixing signal Moser, Roussarie (NIMA 384 491)
signi =
rS²D2
2 e−(Δmsσcτ )2
2
rS
S+B
S√S+B
optimise Bs candidate selection
σcτ optimise resolution
ε D2 optimise tagging performance² = NRS+NWS
N
D =NRS−NWSNRS+NWS
efficiency
dilution: Ameas = D Atrue
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Δ mq measurement: Amplitude method
assume fixed value of Δmq → fit for Amplituderepeat for different values of Δmq
e.g. B0 → D- π+
A(t) ∝ A × cos(Δ mq t)
A = 1 for correct Δ mqA = 0 else
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Δms from combined amplitude
combines:• LEP• CDF RunI• SLD
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CDF D0
Tevatron
Main injectorand recycler
RunI: 1992 – 1996data taking period at
RunII: 2001 – 2009major upgrades tocollider anddetectors
√s = 1.8TeV
The Tevatron
√s = 1.96 TeV
pp̄ collisions
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Tevatron performance
Running well - both peak luminosity and integrated luminositybefore shutdown: ~15-20 pb-1 / week delivered
1 fb-1 delivered in beginning of June 2005 .
1 fb-1
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CDF:• precise tracking:
silicon vertex detector and drift chamber• important for B physics:
direct trigger for displaced vertices
D0:• excellent muon system and coverage• large forward tracking coverage• new in RunII: magnetic field
⇒ D0 has joined the field of B physics
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Physics at the Tevatron• large b production rates:
⇒ 103 times bigger than !
• spectrum quickly falling with pt
• Heavy and excited states not produced at B factories:
• enormous inelastic cross-section:
⇒ triggers are essential
• events “polluted” by fragmentation tracks, underlying events
⇒ need precise tracking and good resolution!
Υ(4S)
Bc, Bs, B∗∗,Λb,Σb, . . .
σ(pp̄, |η| < 1.0) ≈ 20μb
`S
Belle engineering run
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Trigger in hadronic environment
• Dimuon: “easy” trigger, clean signal• sensitive to J/ ψ→ μ+ μ-
• low branching fraction
• Semi-leptonic B decays CDF: (μ, e) + displaced trackD0 : single (μ, e)
• Fully hadronic B decays (CDF)• BR ≈ 80%• require two displaced tracks• needs high precision tracking
at trigger level !
Primary Vertex
Secondary Vertex
d0 = impact parameter
B
Lxy
require:• pt > 2 GeV/c• 120 μm<|d0|<1mm
(not used for mixing analysis)
almost “offline” resolution
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Some sample events
recorded by J/ψ→ μ+ μ- trigger (CDF)
J/ ψ→ μ+ μ-
what all this fuzz about hadronic environment...
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Some sample events
recorded by J/ψ→ μ+ μ- trigger (CDF)
... well, usually events are like this...
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plot courtesy C. Lecci
B physics at low pt⇒ no “jet” structure
CDF RunII Simulation
Some sample events
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Hadronic vs. semi-leptonic B decays
hadronic decaysfully reconstructedhigh cτ resolutionlow candidate yield
semileptonic decayshigh yieldneutrino not reconstructed⇒ worse cτ resolution
Lxy =(~xdecay−~xprim)·~pt
|~pt|decay
PV
cτ = LxyM(B)
pt(B)
= LxyM(`D)
pt(`D)× K
hadronic
semi-leptonic
from simulation
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Bs reconstruction
Ds ≈ 26.7k events
D± ≈ 7.4k events
Exploit excellent muon coverage (single μ trigger):Bs → μ+Ds
-X, Ds- → φ π-, φ→ K+ K-
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Signal Yield Summary: Semi-leptonic
Bs → l Ds X Yield
Ds → (φπ) 32k
Ds → (K* K) 11k
Ds → (3π) 10k
Total 53k
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Signal Yield Summary: Hadronic
Yield
Bs→ Dsπ (φπ) 1600
Bs→ Dsπ (K* K) 800
Bs→ Ds π (3π) 600
Bs→ Ds3π (φ π) 500
Bs→ Ds3π (K*K) 200
Total 3700
oscill. fit range
partially reconstructed Bs
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Mixing measurement outline“same side”:(semi) exclusively reconstructed Bs
“opposite side”
1) decay flavour fromdecay products
2) proper time measurement
e, μoppositeside kaon
3) production flavour fromopposite side tag andsame-side Kaon tagger
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Flavour tagging methodssame side
oppositeside
opposite side: (CDF and D0)• jet charge
• (soft) lepton ID• flavour from semi-leptonic B decay (BR ≈ 20%)• dilution due to oscillation and cascade decays
(b → c → l X)
same side: (new at CDF)• same side Kaon tagging
kaon is (often) leading fragmentation partner of Bs⇒ particle ID is essential!
Qjet =Pi wiQiPi wi
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Flavour taggers at D0Deploy opposite side taggers:• lepton jet charge (in cone around lepton)
Ql = ∑i qi pti / ∑i pt
i
• secondary vertex jet chargeQSV = ∑i (qi pL
i)0.6 / ∑i (pLi)0.6
• event jet charge (outside cone around Bs)QEV = ∑i qi pt
i / ∑ pti
dtag=1−z1+z ; z = Πi
f b̄i (xi)
f bi (xi)combine via:
gives: ²D2 = 2.48± 0.21(stat)+0.08−0.06(syst)%
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Flavour taggers at CDF
Opposite side (OS) taggers: calibrated on l+SVT data• soft μ±, e±
• jet charge: with secondary vertex, with displaced tracks,other high pt jets
Same side (SS) tagger : calibrated in Bs → Ds π channel
most powerful tagger!• use one OS tag and SS tag to determine initial flavour,
• OS taggers mutually exclusive.
tagger ²D2[%]
combined OS 1.54± 0.04± 0.05SS Kaon 4.0+0.9−1.2
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Neural jet charge tagger
TrackNet:combines track quantities (e.g. pt,impact parameter, rapidity w.r.t., etc.)via NeuroBayes network⇒ probability to originate from B
B-Jet network:combines TrackNet + jet quantitiesvia NeuroBayes network⇒ identify B jets
⇒ significant improvementw.r.t. method based on impact parameter alone
note log-scale
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Same side kaon tagging
Kaon is (often) leading fragmentation partner• identified Kaon ⇒ identify Bs• charge of Kaon determines production flavour
⇒ very powerful tagger !
Challenges:• need good PID to identify Kaon: dE/dx + ToF (no RICH!)• has to be calibrated using simulation
→ difficult in hadronic environment
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Calibrating SSKTuse combined PID likelihood (ToF + dE/dx), select most “kaon-like” track in cone around B as tagging trackverify kinematic distributions (pT, tagging track pT, multiplicity, isolation) of light B mesons in Pythia simulationverify particle ID simulationtest for dependences on:
fragmentation modelbb production mechanismsdetector/PID resolutionmultiple interactionsPID content around B mesondata/MC agreement
test on high statistics lightB meson sample
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Δ ms measurement⇒ all ingredients there, determine Δ ms
• Amplitude method: scan Δ ms range, fit for asymmetry amplitude
• unbinned likelihood fit:
signal
combinatorial background
prompt background
“physics” background(other particle decays)
each part: contribution from mass, cτ, σ(cτ)
L = fsigLsig +fcombLcomb+fpromptLprompt+fphysLphys
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Bs mixing result
most probable value: Δ ms = 19 ps-1
17 < Δ ms < 21 ps-1 at 90% C.L.
resolution not sufficientto measure oscillationin this region
A/σA (19 ps-1) = 2.5
probability of bg fluctuation: (5.0 ± 0.3)%
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A/σA (17.25 ps-1) = 3.5
Bs mixing result
Δms = 17.33 +0.42 (stat) ± 0.07 (syst) ps-1-0.21
Δms in [17.00, 17.91] ps-1 at 90% CL Δms in [16.94, 17.97] ps-1 at 95% CL
Probability of bg fluctuation: 0.5%
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Systematic uncertainties on Δms
evaluated using toy MCsample compositionindividual event vertex uncertaintydilutiondetector resolution
Result is limited by statisticsdominant: lifetime measurement
Syst. Unc.
SVX Alignment 0.04 ps-1
Track Fit Bias 0.05 ps-1
PV bias from tagging 0.02 ps-1
All Other Sys < 0.01ps-1
Total 0.07 ps-1
All relevant systematic uncertainties are common between hadronic and semileptonic samples
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Impact on CKM fit
prior to new CDF result: with new CDF result:
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Impact on CKM fit
|Vtd||Vts| = 0.208
+0.08−0.07 (stat ⊕ syst)
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Further improvementsCDF:
Improved selection in hadronic modes using Neural NetworksUse partially reconstructed hadronic modesUse semileptonic events from other triggersImprove vertex resolution
D0:Addition of other taggersUse of other semileptonicdecay modesUse of hadronic decay modesImprove vertex resolution (inclusion of Layer0)
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Further improvementsdevelop inclusive B analysis package using NeuroBayes inspired by BSAURUS (DELPHI)
exploit all availableinformation vianeural networks
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NeuroBayes (1)Inspired by nature:Neuron in brain “fires” if stimuli received from other neurons exceed threshold. (very simple model. . . )
Construct Neural NetworkOutput of node j in layer n is given by weighted sum of output of all nodes in layer n-1:
xnj = g³P
k wnjk · xn−1k + μnj
´g(t) μnjsigmoid function threshold (“bias-node”)
→ information is stored in connections
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NeuroBayes (2)
Output
Input
Sign
ifica
nce
cont
rol
Postprocessing
Preprocessing
f t
Probability that hypothesisis correct
(classification)or probability densityfor variable t
t
Historic orsimulated data
Data seta = ...b = ...c = .......t = …!
NeuroBayesNeuroBayes®®TeacherTeacher
NeuroBayesNeuroBayes®®ExpertExpert
Actual (new real) data
Data seta = ...b = ...c = .......t = ?
ExpertiseExpertise
Expert system
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Classical ansatz:f(x|t)=f(t|x)
approximately correctat good resolution
far away fromphysical boundaries
Bayesian ansatz:takes into accounta priori- knowledge f(t):•Lifetime never negative•True lifetime exponentially
distributed
NeuroBayes (3)example: measured lifetime distribution
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Inclusive B analysisidentify B decay products and construct “best” B vertex
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Inclusive B analysisflavour tagging on track level:
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Inclusive B analysis(preliminary MC studies,for illustration only)
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Inclusive B analysisexploit all information
combined flavour tag
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Conclusions
Good performance of Tevatron and detectorsΔ ms (almost) measured
D0: two sided limit: 17 < Δ ms < 21 ps-1
A/σA ≈ 2.5 (for amplitude method) at 19 ps-1
Probability of background fluctuation: 5.0 ± 0.3 %
CDF: A/σA ≈ 3.5 (for amplitude method)Probability of background fluctuation: 0.5%
Aim for “5σ” discovery at summer conferencesextensive list of further improvements... no “new physics” here, it seems...
Δms= 17.33+0.42−0.21(stat)± 0.07(syst.)
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• observe at• 1 fb-1 luminosity delivered early June• huge inelastic cross-section:≈ 5000 times bigger than for⇒ triggers are essential!
• events “polluted” by fragmentation tracks, underlying events⇒ need precise tracking and good resolution
pp̄ collision
Physics at the Tevatrons√s = 1.96 TeV
• dedicated trigger for J/Ψ→ μ+ μ-
• trigger events where m(μ+μ-) around m(J/Ψ)⇒ high quality J/Ψ events with large statistics
• channel J/Ψ→ e+e- much more challenging in hadronic environment
bb̄
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SVT based Triggers hadronic channelrequire two SVT tracks
pT>2GeV/cpT1+pT2 > 5.5 GeV/copposite charge 120 μm < SVT IP < 1 mmLxy > 200 μm
semileptonic channel require 1 Lepton + 1 SVT track
1 muon/electron pT> 4 GeV1 additional SVT track with
pT > 2 GeV120 μm < SVT IP < 1 mm
DP.V.
Lepton
Bν
SVT trackDP.V.
SVT track
B SVT track
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“Classic” B Lifetime Measurement
reconstruct B meson mass, pT, Lxycalculate proper decay time (ct)extract cτ from combined mass+lifetime fitsignal probability:psignal(t) = e-t’/τ⊗ R(t’,t)
● background pbkgd(t) modeled from sidebands
pp collision B decays
ct · pt/m
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Hadronic Lifetime Measurement
SVT trigger, event selection sculpts lifetime distributioncorrect for on average using efficiency function:
p = e-t’/τ⊗ R(t’,t) ·ε(t)efficiency function shape contributions:
event selection, triggerdetails of efficiency curve
important for lifetime measurementinconsequential for mixing measurement
pattern limit|d0| < 1 mm
“trigger” turnon
0.0 0.2 0.4proper time (cm)
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Hadronic Lifetime Results
ModeLifetime [ps](stat. only)
B0 → D- π+ 1.508 ± 0.017
1.638 ± 0.017
1.538 ± 0.040
B- → D0 π-
Bs → Ds π(ππ)
World Average:
B0 → 1.534 ± 0.013 ps-1
B+ → 1.653 ± 0.014 ps-1
Bs → 1.469 ± 0.059 ps-1
Excellent agreement!
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Semileptonic Lifetime Measurement
neutrino momentum not reconstructed
correct for neutrino on average
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lDs ct* Projections
Bs lifetime in 355 pb-1: 1.48 ± 0.03 (stat) psWorld Average value: 1.469 ± 0.059 ps
Lepton
Ds- vertex
P.V.Bs vertex
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Unbinned Likelihood Δmd Fits
hadronic: Δmd = 0.536 ± 0.028 (stat) ± 0.006 (syst) ps-1
semileptonic: Δmd = 0.509 ± 0.010 (stat) ± 0.016 (syst) ps-1
world average: Δmd = 0.507 ± 0.005 ps-1
fit separately in hadronic and semileptonic sampleper sample, simultaneouslymeasure
tagger performanceΔmd
projection incorporatesseveral classes of tags
semileptonic, lD-, muon tag
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Systematic uncertaintiesSilicon detector alignment
Effect of imperfect alignment of silicon vertex detector on lifetime measurement. Tested by introducing distortions into realistic simulation measuring lifetime with default alignment
Track-fit biasMis-measurement of pt introduces mis-measurement of Lxy and lifetime. Tested with realistic simulation.
Primary vertex biasMis-measurement of primary vertex leads to mis-measurement of Lxyand lifetime, cause a bias when tracks from opposite side are incorporated into primary vertex. Studied with large sample of fully reconstructed B events comparing reconstructed primary vertex with average beam position
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Conditional probability densities f(t|x)
Conditional probability densities f(t|x) are functions of x, but also depend on marginal distribution f(t).
Conditional probability densities f(t|x) are functions of x, but also depend on marginal distribution f(t).
Conditional probability density for a special case x
(Bayesian Posterior)
Conditional probability density for a special case x
(Bayesian Posterior)
Inclusive distribution(Bayesian Prior)
Inclusive distribution(Bayesian Prior)
Marginal distribution f(t)Marginal distribution f(t)
Bayesian approach