B-Physics activitites of the Karlsruhe CDF group - an Overviekerzel/Talks/kerzel-heidelberg.pdf · Time of Flight Drift Chamber Plug Calor Central Calor Solenoid Muon Silicon Microstrip

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:[email protected]

10th February 2004

U.Kerzel, University of Karlsruhe Hochenergiephysik Gruppenseminar 1

mailto:[email protected]

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


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


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)


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.


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


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

• . . .


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


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)


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


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


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


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

�


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


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


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


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


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


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


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.


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


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) ?


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


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


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)



Documents

B-Physics activitites of the Karlsruhe CDF group - an Overviekerzel/Talks/kerzel-heidelberg.pdf · Time of Flight Drift Chamber Plug Calor Central Calor Solenoid Muon Silicon Microstrip