1. KLOE Drift Chamber Review L.N.F. – March 9, 2001
Tracking report
P. Valente for the Tracking Group

2. Summary Present status Open problems & ongoing work
Tracking efficiencies
momenta and mass resolutions
Open problems & ongoing work
Improve KL efficiency
Error matrix
Timing (t0)
Plans for the future
dE/dx in Monte Carlo
dE/dx in data
Improved pattern recognition

3. Tracking efficiencies
|z|<125 KS KL
KLFV
KM 177
today
e2PR
0.955
0.961
0.834
0.904
0.816
0.894
e2TF
0.991
0.990
0.967
0.959
0.962
0.953
eVF
0.947
0.945
0.871
0.869
0.863
0.858
eTOT
0.896
0.702
0.753
0.677
0.731
Latest version of tracking code, Monte Carlo ’99, kG

4. Tracking efficiencies: KS to p+p-

5. Tracking efficiencies: KL to p+p-
Inefficiencies mainly due to
decays & spiralising tracks

6. Split tracks (KS) Geom. rejected 5.3% 6.0% Not split 85.8% 88.9%
Category
p+p- (’99 data)
p+p- (’99 MC)
Comment
Geom. rejected
5.3%
6.0%
Not split
85.8%
88.9%
Fake vertex
1.7%
1.0%
Outgoing p<10 MeV/c
Kink: p to m
5.1%
5.7%
Cut in M2(p in mn)
Split, not kink & not fake
6.7%
3.6%
More in data than in MC (need better error parametrisation)
> 1 vertex
10-3
(2 pattern candidates or kink finder?)

7. Split, not kink & not fake
Split tracks: KS
Category
p+p- (’99 data)
p+p- (’99 MC)
Comment
Split, not kink & not fake
6.7%
3.6%
More in data than in MC
(2 pattern candidates or kink finder?)
Using a radiative Bhabha data sample and turning off the
kink finder algorithm, this category is reduced to ~ 3% in data.

8. Track/cluster performances: KS
Category
Population
p+ average e
p- average e
’99 data
’99 MC
Not split
85.8%
88.9%
94.6%
95.0%
93.6%
95.1%
Fake vertex
1.7%
1.0%
92.3%
92.6%
90.0%
91.6%
Kink:
p to m
5.1%
5.7%
83.1%
84.0%
83.2%
Split
6.7%
3.6%
70.8%
80.0%
65.0%
70.0%
71.6%
81.4%
65.4%
72.8%
Good agreement Data/MC

9. Absolute timing: KS 1 track segment only to calorimeter (MC)
1 bunch off
>1 track segment
to calorimeter (MC)

10. Absolute timing: KS
dt2 vs dt1 (MC)
1 bunch off

11. Vertex resolution: KS KL to p+p- (MC)
KL normalised
to KS events KS cm

12. Momentum resolution: KS KL to p+p- (MC)
KL normalised
to KS events KS
MeV/c

13. Pulls: KS KL to p+p- (MC)
Transverse momentum: pull
Azimuth angle (f): pull

14. Pulls: KS KL to p+p- (MC)
The error on the polar angle,
used by the vertex fit, has to
be checked KL
Polar angle (cotgq): pull

15. Mass & acollinearity: KS KL to p+p- (MC)
KL normalised
to KS events KS
MeV/c2
Acollinearity (degrees)
n. of s

16. “Global” Constrained Fit
KL mass resolution: better “core” resolution, but
longer tails wrt KS...
First attempt to perform a kinematic fit taking into account p+p- momenta and mass constraints.

17. Tracking (correlations): KSKL to p+p- (MC)
D transverse
D longitudinal
Correlations:
error on longitudinal & transverse momentum
vs.
error on longitudinal & transverse vertex position

18. Tracking (correlations): KS KL to p+p- (MC)
MeV/c KS cm KL
Dp long. vs Dr long.
Dp transv. vs Dr long.

19. Tracking (correlations): KS KL to p+p- (MC)
MeV/c KS cm KL
Dp long. vs Dr transv.
Dp transv. vs Dr transv.

20. Acollinearity: KS KL to p+p- (MC)
degrees
degrees cm cm cm cm cm cm cm cm
D(acollinearity) vs. Dr transverse & longitudinal

21. p+p- opening angle (MC)
Rec.
MC true
Tail fraction vs q1 & q2

22. Constrained mass fit: KS
d MK (MeV)
d MK [old]
PK(MeV/c)

23. Constrained mass fit: KL
d MK (MeV)
d MK [old]
Pp(MeV/c)

24. Constrained mass fit: KSKL momenta
before
PK(MeV/c)
after

25. Future plans: Recover KL efficiency
Constrained fit improves rejection power by a
factor 2
Use KS flight direction first guess to perform a
second step of tracking for KL
KL flight time could be subtracted

26. Add ADC simulation in Monte Carlo
Future plans: dE/dx
Add ADC simulation in Monte Carlo
(gain fluctuations, charge collection...)
Use ADC information in data for PID:
Calibration
Discriminating variables