View
0
Download
0
Category
Preview:
Citation preview
B-Physics activitites of the Karlsruhe CDF
group - an Overview
Michael Feindt, Ulrich Kerzel, Kurt Rinnert
for the Karlsruhe CDF B group
University of Karlsruhe, Germany
Elementarteilchenphysik
- Förderschwerpunkt
Großgeräte der physikalischenGrundlagenforschung
mailto:kerzel@fnal.gov
10th February 2004
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 1
1. Tevatron and Luminosity
2. The CDF detector
3. B-Physics at Hadron colliders
4. B-physics program, Bs mixing
5. Particle ID, B-tagging
6. Observation and properties of X(3872)
7. Other activites: Grid, alignment, tracking, top
8. Conclusion
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 2
The Tevatron
CDF
@@@R
D0
@@@R
Tevatron
main injectorrecycler
· observe pp̄ collisions
· 2 detectors: CDF and D0
· RunI (’85-’96) :√
s = 1.8 TeV
· major upgrades
· RunII (’01-’09) :√
s = 1.96 TeV
0
50
100
150
200
250
300
0 50 100 150 200 250 300 350day
CD
F a
cqui
red
Lum
inos
ity (p
b-1)
2001
2002
2003
2004
2005
· better performance each year
· recorded ≈ 0.5fb−1
· (≈ 400pb−1 “good” data)
· expect ≈ 4− 8fb−1 by 2009
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 3
The CDF Detector
New
Old
Partially New
Time of Flight Drift Chamber
Plug Calor
CentralCalor
Solenoid
Muon
Silicon MicrostripTracker
Muon System
• extensive upgrades
between RunI and RunII
• excellent tracking:
vertex dectector,
drift chamber
• particle ID:
dE/dx, time-of-flight
• tracking used already at trigger level:
strong in hadronic B decays
(trigger on displaced tracks)
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 4
B Physics At Hadron Colliders
p (GeV)T,min
Total Inelastic Cross−Section
.. 5000x
σ
60 mb
4 nb
100 nb
µ~29 bσ (b)
T
Integrated
pp b + ....
Above Min−P
Tevatron: (|y|<1)
9.44 9.46
Mass (GeV/c2)
0
5
10
15
20
25
σ (e
+ e- → H
adro
ns)(n
b) ϒ(1S)
10.00 10.020
5
10
15
20
25
ϒ(2S)
10.34 10.370
5
10
15
20
25
ϒ(3S)
10.54 10.58 10.620
5
10
15
20
25
ϒ(4S)
Υ(4S)e e+ −
• large production rates:
σ(pp̄ → bX) ≈ 29µb
103 higher than Υ(4s)
• heavy states only
at Tevatron: Bs, Σb,Λb
• but:
– inelastic cross-sec 1000 times higher than signal
→ triggers are essential
– σFNALbb̄
≈ σLEPbb̄
at high pt
– events “polluted” by beam remnants, etc.
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 5
Trigger overview
Most important triggers for B physics:
• Dimuon: “easy” trigger, clean signal
( J/Ψ → µ+µ−)
low branching fraction
• Displaced track: semileptonic decay
need particle ID to identify lepton
(BR ≈ 20%)
• Two track: trigger displaced vertex
trigger for fully hadronic B decays
(BR ≈ 80%)
d0
PV track
track
unstable
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 6
B-physics programme at CDF
• B0 mixing
• Bs mixing
• Observation and
properties of X(3872)
• Λb → J/ΨΛ lifetime
• Λb branching ratios
• B masses
• B+ → J/Ψπ
• semilep. moments
• BR and CP violation
in B → hh
• Bc → J/Ψπ
• FCNC B → µ−µ+
• J/Ψ, B cross-sections
• Bs/B0 BR ratio
• excited states: B∗∗, Σb
• B meson lifetimes
• Λb → pK, pπ
• Bs → φφ
• PentaQuark searches
• . . .
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 7
Exclusive states
Some examples of reconstructed states:
2) mass, GeV/cπKµµ(5.20 5.25 5.30 5.35
2ca
ndid
ates
per
2.5
MeV
/c
0
20
40
60
80
100
120
140
160
180
200
CDF Run II Preliminary -1pbL ~ 260*0 Kψ J/→ 0 B
39 sig.±1155candidatesFit prob: 29.7%
data
m(Sig)m(Swp)m(Bkg)
2) mass, GeV/cπKµµ(5.20 5.25 5.30 5.35
2ca
ndid
ates
per
2.5
MeV
/c
0
20
40
60
80
100
120
140
160
180
200
]2 candidate mass [GeV/cuB5.00 5.05 5.10 5.15 5.20 5.25 5.30 5.35 5.40 5.45 5.50
2E
vent
s/5
MeV
/c0
50
100
150
200
250
300
35052.6±N(Bu)=2264.1
-1CDF Run II Preliminary 220 pb
± Kψ J/→±B Fit Prob: 52.0%
]2
candidate mass [GeV/cbΛ5.3 5.4 5.5 5.6 5.7 5.8 5.9
2E
vent
s/6
MeV
/c
0
5
10
15
20
25
30
3510.3±)=88.6bΛN(
-1CDF Run II Preliminary 220 pb
Λ ψ J/→BΛFit Prob: 23.3%
2KK) mass, GeV/cµµ(5.3 5.4 5.5
2ca
ndid
ates
per
5.0
MeV
/c
0
10
20
30
40
50
60
CDF Run II Preliminary -1pbL ~ 260
φ ψ J/→ sB15 sig.±203
candidatesFit prob: 93.4%
data
m(Sig)m(Bkg)
2KK) mass, GeV/cµµ(5.3 5.4 5.5
2ca
ndid
ates
per
5.0
MeV
/c
0
10
20
30
40
50
60
M(Bs) [MeV]5340 5360 5380
Delphi 5374. ± 16. ± 2. 5374. ± 16. ± 2.
Aleph 5368.6 ± 5.6 ± 1.5 5368.6 ± 5.6 ± 1.5
Opal 5359. ± 19. ± 7. 5359. ± 19. ± 7.
CDF 5369.9 ± 2.3 ± 1.3 5369.9 ± 2.3 ± 1.3
CDF II (this) 5366.01 ± 0.73 ± 0.33 5366.01 ± 0.73 ± 0.33
Worldaverage 5369.6 ± 2.4 5369.6 ± 2.4
]2 [GeV/cφ φm5 5.2 5.4 5.6 5.8 6
2E
vent
s/24
MeV
/c1
2
3
4
5
6
7
CDF RunII Preliminary -110 pb±L = 179 12 events in search window
0.62±Expected BG events = 1.95
Bs → φφ
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 8
B mixing I
mass eigenstates 6= weak eigenstates
→ unitary CKM Matrix
→ unitarity triangle
(complex ρ, η plane)
d′
s′
b′
=
Vud Vus Vub
Vcd Vcs Vcb
Vtd Vts Vtb
·
d
s
b
Aim: overconstrain triangle:
→ measure parameters
→ test unitarity
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
-0.4 -0.2 0 0.2 0.4 0.6 0.8 1
α
∆md
εK
εK
|Vub/Vcb|
∆ms & ∆md
sin 2β
α
βγ
ρ
η
excl
uded
are
a ha
s C
L < 0.05 C K M
f i t t e rICHEP 2004
· sin(2β) from B0 → J/ΨK0s
·γ from B0 → π+π−, Bs → K+K−
·|Vtd| from B0/B̄0 mixing
· xs
xd≈ |Vts|2
|Vtd|2(x = ∆m
Γ)
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 9
B mixing II
Measure:
A(t) =Nmix(t)−Nunmix(t)
Nmix(t) + Nunmix(t)
∝ cos(∆mst)
−0.1
−0.05
0
0.05
0.1
0 2.5 5 7.5 10proper decay time, t [ps]
Mix
ed A
sym
met
ry
Bd mixing ∆md = 0.5 ps−1
Bs mixing ∆m s= 20 ps−1
Dilution: 0.05%
challenging task reconstruct Bs signal:
• low Bs branching fraction
• tag flavour at production/decay
→ need high quality taggers:
Aobs(t) = A(t)true ·D(D = 2 · Purity − 1), tagging power εD2
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 10
Flavour tagging methods
K−
Bs
SST
OSTa l−
b K+
• Jet Charge:
Qjet =∑
i wiQi∑i wi
wi: weight, e.g. pαt (2− Tp)
Tp: prob. primary track
(sum over all tracks in jet)
• (Soft) Lepton ID:
identify semilep. B decay:
B → lX
• Kaon ID:
K is leading fragmentation
partner of Bs
→ particle ID essential for Bs mixing
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 11
From Delphi to CDF . . .
Extensive experience with inclusive B physics at Delphi:
Expert-system BSAURUS
· 30 man-years of B physics experience
· provides 250 B physics related variables
· uses many neural nets to exploit all information
· TrackNet: track originates from B or not
· production decay flavour nets
· BDNet: discriminate secondary/tertiary vertex
· B species network
· . . .
→ transfer knowledge to CDF . . .
. . . but life much more difficult at hadron machines
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 12
NeuroBayes I
→ Enormous Experience with Neural Networks in KA
Advantage of Neural Networks:
· learn higher order correlation to training target
· learn (higher order) correlation between variables
· do not require complete information all the time
· allow to to include “quality variables”
(e.g. good/bad measuerement, fit, etc)
Karlsruhe development: NeuroBayes NN package
(→ spin-off company Phi-T, supported by BMBF)
· sophisticated, automated preprocessing
· Bayesian approach and regularisation
· Network estimate can be interpreted as probability
· . . .U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 13
NeuroBayes II - Bayesian Approach
Example: Exponential with Gaussian resolution
(lifetime of a particle)
t (true)
x (m
easu
red)
f(x|t)
f(t|x)
f(t|x)
f(x|t)
class. approachf(x|t) = f(t|x)approx valid farfrom phys. boundarieswith good resolution
�
Bayes’ statistics:uses a priori knowledge
− lifetime never negative− true distrib. is exponential
�
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 14
TrackNet
→ B hadron has finite lifetime (≈ 1.6 ps)
→ decays at secondary vertex−−b
+b
BD
lepton
K
π
pK
π
Opposite Side
Same Side
→ use 3 consecutive NeuroBayes
neural networks to identify tracks from B decay
-· initial probability for track
to be B decay track
· build intermediate sec. vertices
of best candiatates
· select tracks compatible with
this vertexefficiency
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1efficiency
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
puri
ty
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
→ construct best secondary vertex
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 15
JetCharge tagging with Neural Networks
Aim: select inclusive high purity b jets for JetCharge Tag
→ TrackNet: use NeuroBayes to
obtain probability for track
come from B decay
(very good Data/MC
agreement, note log-scale)Neural Network output
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
-310
-210
-110All tracks in jets
lepton+SVT datalepton+SVT MCsignalbackground
→ JetNet: use NeuroBayes to
select b jets
(combine TrackNet,
jet-type variables)
jetNet output0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
-210
-110
All Jetslepton+SVT datalepton+SVT MCbackgroundsignal
→ expect ≈ 10% improvement wrt. to current jet selection
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 16
Particle ID I
Time-of-flight:
· scintillation detectors at r = 140 cm
from interaction point
· combine: momentum from tracking
and time-of-flight → mass
→ 2σ K/π sepraration up
to pt ≈ 1.5GeV/chMass
Entries 395116
Mean 1.028
RMS 0.01923
inv. Mass KK [GeV]0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.070
1000
2000
3000
4000
5000
6000
hMassEntries 395116
Mean 1.028
RMS 0.01923
inv. Mass KK [GeV]0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.070
50
100
150
200
250
(TOF) Kaon > 0.0σ hMass0s_tofEntries 7184
Mean 1.026
RMS 0.01721
/ ndf 2χ 95.96 / 72
p0 11.1± -6821
p1 12.2± -4612
p2 12± 2.808e+04
p3 10± -1.66e+04
p4 7.7± 185.8
p5 0.000± 1.019
p6 0.000133± 0.003246
S/B = 1.02
(TOF) Kaon > 0.0σ
inv. Mass KK [GeV]0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.070
50
100
150
200
250
(TOF) Kaon > 0.5σ hMass05s_tofEntries 6159
Mean 1.026
RMS 0.01702
/ ndf 2χ 93.52 / 72
p0 10.3± -6772
p1 11.2± -4598
p2 11± 2.806e+04
p3 10± -1.665e+04
p4 7.2± 164.1
p5 0.000± 1.019
p6 0.000141± 0.003232
S/B = 1.03
(TOF) Kaon > 0.5σ
inv. Mass KK [GeV]0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.070
20
40
60
80
100
120
140
160
180
200
(TOF) Kaon > 1.0σ hMass1s_tofEntries 4576Mean 1.025RMS 0.01665
/ ndf 2χ 77.82 / 72
p0 8.8± -4539 p1 9.6± -6715
p2 9± 2.594e+04
p3 8± -1.465e+04
p4 6.6± 135.5
p5 0.000± 1.019 p6 0.00015± 0.00311
S/B = 1.11
(TOF) Kaon > 1.0σ
inv. Mass KK [GeV]0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.070
20
40
60
80
100
120
140
(TOF) Kaon > 1.5σ hMass15s_tofEntries 3309
Mean 1.024
RMS 0.01634
/ ndf 2χ 87.95 / 72
p0 7.4± -2400
p1 8.1± -8749
p2 8± 2.392e+04
p3 7± -1.275e+04
p4 5.8± 108.8
p5 0.000± 1.019
p6 0.000151± 0.002968
S/B = 1.18
(TOF) Kaon > 1.5σ
inv. Mass KK [GeV]0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.070
20
40
60
80
100
(TOF) Kaon > 2.0σ hMass2s_tofEntries 2198
Mean 1.023
RMS 0.01617
/ ndf 2χ 84.35 / 72
p0 5.9± 741.8
p1 6± -1.18e+04
p2 6± 2.095e+04
p3 5.5± -9877
p4 5.11± 78.62
p5 0.000± 1.019
p6 0.000178± 0.002858
S/B = 1.28
(TOF) Kaon > 2.0σ
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 17
Particle ID II
dE/dx: exploit energy-loss
according to Bethe-Bloch formula
µ π K p
e
D
e
Ene
rgy
depo
sit p
er u
nit l
engt
h (k
eV/c
m)
Momentum (GeV/c)
8
12
16
20
24
28
32
0.1 1 10in driftchamber (COT):
→ > 1.4σ K/π separation for pt > 1.4 GeV/c
→ 3σ e/π separation for p = 1GeV
in silicon detector (SVX):
→ up to 3σ separation
for p < 1 GeV
possible.
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 18
Particle ID III
Soft Lepton Tagging: electron ID
→ semilep. B decay: b → lX, J/Ψ → e+e−
Very difficult: huge background
· < 10% e− per event (mainly π±)
· conversion electrons
· Bremsstrahlung
efficiency0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
efficiency0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
puri
ty
0.8
0.85
0.9
0.95
1
@@Icut based approach
Approach: use NeuroBayes to identify electrons
→ exploit information about:
· calorimeter
· dE/dx
· time-of-flight
· curvature change in material
→ use same technique to build soft muon IDU.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 19
B-Tagging for SingleTop
Search: electroweak top production
→ need to identify jet containing b� �
�
�
�
�
�
� �
�
��� ��
�
��� ���
Main background:
(after reconstr. second. vertex)
Wbb̄ 33%
Wcc̄ 12%
Wc 12%
mistag (uds) 26%
non-W 14%
Di-Boson 3%
→ 50% background from u, d, s, c
→ use NeuroBayes to
enrich events with b jets
efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
puri
ty0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 20
Observation of X(3872)
New state: X(3872) → J/Ψπ+π−
Observed by Belle, confirmed by CDF
)2
Mass (GeV/c-π+πψJ/3.65 3.70 3.75 3.80 3.85 3.90 3.95 4.00
2C
andi
date
s/ 5
MeV
/c
0
500
1000
1500
2000
2500
3000
3.80 3.85 3.90 3.95900
1000
1100
1200
1300
1400
1500
-1~200 pbCDF II
measured by CDF:
· mass: 3871± 0.7± 0.4 MeV/c2
· lifetime: 439± 107µm
· long lived fraction: 16.1± 4.9 (stat)± 2.0 (syst) %
But what is it??
· cc̄ charmonium state?
→ very close to DD̄∗ threshold
· “molecular” state (1977: Glashow et al.) ?
· “Deuson” (DD̄∗ bound by π exchange) ?
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 21
Properties of X(3872)
m(π+π−) spectrum :
· peaks at high values
· ρ like ?
Determination of JPC: Helicity analysis
exploit information about:
· decay angles
· m(π+π−) spectrum
→ predicted distribution varies with
assumed JPC and decay
→ discriminate between different
assumptions
)ΨJ/Θcos(-1 -0.8 -0.6 -0.4 -0.2 -0 0.2 0.4 0.6 0.8 1
arb.
uni
ts
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
0.022
ρ via -0
s)ππ via (+0ρ via +0
)ΨJ/Θangular distribution: cos(
Φ∆0 1 2 3 4 5 6
arb.
uni
ts0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
0.022
ρ via -0
s)ππ via (+0ρ via +0
Φ∆angular distribution:
→ challenge: low X(3872) yield
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 22
Conclusion
• Rich and diverse B-physics programme
both at CDF in general and Karlsruhe
• Measured ∆md, on the way for measuring ∆ms
• Karlsruhe group very active:
· tracking, alignment,
· Grid,
· neural networks
· particleID, flavour tagging,
· exclusive B states, Bs-mixing,
· X(3872) properties
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 23
What I did not talk about . . .
• Tracking:
KA main tracking developers
• Alignment
• Grid activities:
· successfull operation of SAM datahandling system
· ≈ 23TB of data at GridKa Tier1 centre
· (almost) autonomous operation of German group
· next step: fully GRID enabled
• Top-Group:
· focus on electroweak top production
· development of Physics Analysis Expert (PAX)
(with CMS group at KA, Aachen)
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 24
U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 25
Recommended