1. Status of the Offline Pattern Recognition Code GSI, December 10th 2012
Gianluigi Boca
GSI & Pavia University
Gianluigi, 10 Dec 2012

2. Progress
At the last collaboration meeting in Paris I showed the problems
of the Offline PR :
1) strange behaviour with 0.3 GeV/c tracks;
The strange behaviour with 0.3 GeV/c tracks was caused by the use
of a wrong geometry flag for the EMC : in practice it was as if
the EMC was not present. Fixed.
2) very large CpuTime consumption.
10 Dec 2012

3. Paris meeting Track Reconstruction Efficiency
% of reconstructed tracks. In these plots ‘reconstructed track’ means
AT LEAST 80% of the TRUE Stt+Mvd hits.
Paris meeting
MC generation : Box Generator;
multiplicities from 1 up to 8; momenta from 0.3 to 10 GeV/c.
Low efficiency
at 0.3 GeV/c ! Why ?
Low efficiency
at 0.3 GeV/c ! Why ?
No Bkg
Bkg added

4. Track reconstruction efficiency after fixing the geometry problem
% of reconstructed tracks. In these plots ‘reconstructed track’ means
AT LEAST 80% of the TRUE Stt+Mvd hits.
MC generation : Box Generator;
multiplicities from 1 up to 8; momenta from 0.3 to 10 GeV/c.
No Bkg
Bkg added

5. Cpu time consumption after fixing the geometry problem
Cpu times on an Intel Xeon 2.13 GHz 64 bit Lenny machine
MC generation : Box Generator;
multiplicities from 1 up to 8; momenta from 0.3 to 10 GeV/c.
No Bkg
Bkg added

6. Reducing the large cpu time consumption by changing the fitter
The fitter plays the main role here, according to a
valgrind Cpu time profile run some time ago.
Change the fitter and try a Hough Transform method used as
a fitter, (so called ‘Legendre transform’ method actually).
10 Dec 2012

7. Reducing the large cpu time consumption by changing the fitter
The Legendre transform method in practice means :
for candidate tracks go to the Conformal Space
U  X / (X2 + Y2 ) ; V  Y / (X2 + Y2 )
apply the method of the accumulation histogram to the tangents
to the drift radius (this is the same method Yuti Liang
uses for the online PR).
10 Dec 2012

8. Example with the Stt axial straws
The accumulation plot is in 2 dimensions, with the variables R and q R 
Stt region
SciTil
region
MC truth
Mvd Pixel
Mvd Strip
Stt Parallel
SciTil

9. (in reality the Mvd hits are
Example with the Mvd hits
Mvd region  R
Mvd Pixel
Mvd Strip
Stt Parallel
SciTil
(in reality the Mvd hits are
much smaller in size)

10. Reducing the large cpu time consumption by changing the fitter
I apply this method to already ‘selected’ track candidates with the
road method , NOT globally to all hits. Effectively I use it only
as a fitter.
Let me remind you that in this PR there are 3 moments in which a
fit is used :
do clasterization with STT axial + SciTil hits only, then do the fit;
attach to track candidate possibly Mvd hits, redo the fit;
attach skew Stt hits, do the fit in SZ.
10 Dec 2012

11. new Hough transform fit with axial STT only1)
new Hough transform fit with axial STT only
example with 1 track,
0.3 GeV/c
11 Oct 2012

12. Things seem to work well, a peak stends out close to the true value :q
10 Dec 2012

13. new Hough transform fit with axial STT + Mvd2)
new Hough transform fit with axial STT + Mvd
example with 1 track,
0.3 GeV/c
11 Oct 2012

14. enlarges considerably
Things don’t seem to work well, a peak stends out but NOT
so close to the true value :
contribution of
the Mvd hits q
the scale in R
enlarges considerably
because of the Mvd hits R
peak
10 Dec 2012

15. In XY plane some axial Stt hits are subsequetly NO MORE associated
to the track :
lost Stt axial hits
MC truth
reco track
10 Dec 2012

16. (in reality the Mvd hits are
The reason of this apparent contraddiction (when I add the more precise
Mvd hits I lose the track) is perhaps evident from the conformal plot :
Mvd region
Stt region
SciTil
region
MC truth
reco track
Mvd Pixel
Mvd Strip
Stt Parallel
SciTil
(in reality the Mvd hits are
much smaller in size)

17. (in reality the Mvd hits are
The Mvd hits occupy a much larger region in the conformal space,
therefore the straight lines become more ‘sparse’ and inaccurate. I tried
to increase the number of straight lines per Mvd (ans Stt) hit and also
to generate tracks all over the Pixel (Strip) surface but it didn’t work.
Mvd region
Stt region
SciTil
region
MC truth
reco track
Mvd Pixel
Mvd Strip
Stt Parallel
SciTil
(in reality the Mvd hits are
much smaller in size)

18. Therefore for the axial Stt + Mvd fit I tried a conventional
2 fit instead ; as you all know the parameters are calculated
with a simple formula (inversion of a 2x2 matrix)
and very fast.
Details of the 2 fit :
- use the previous STT alone fit to associate Mvd hits to the
tracklet;
use the previous STT alone fit to resolve the right/left
ambiguity of the STT ( use the point on the drift radius closer
to the trajectory);
- do the 2 fit including the Mvd hits.
10 Dec 2012

19. Conformal space : some points on the drift radius closer to the initial
trajectory are shown .
point on the drift
radius closer to
the initial
trajectory .
reco track
Stt Parallel
SciTil
27 Nov 2012

20. attach the skew STT hits3)
attach the skew STT hits
and do fit in FZ space

21. Present scheme of the offline PR, FZ fit
In the present PR there are essentially 3 steps :
do clasterization with STT axial + SciTil hits only, then do the fit;
attach to track candidate possibly Mvd hits, redo the fit;
attach skew Stt hits, do the fit in FZ.
The association of the STT Skew hits to the each track found so far in
the XY is done in the usual way;
the subsequent fit is performed in a much faster way again with
a 2 fit .
10 Dec 2012

22. is a straight line :  = 0 + K z
The projection of the trajectory on the lateral face of the Helix cylinder
is a straight line :  = 0 + K z
0 is the polar angle of the trajectory at z = 0; it is obtained from the
prevous fit in XY; NOT parameter of the fit;
K = parameter of the fit
MC truth
reco track
Mvd Pixel
Mvd Strip
Stt Parallel
SciTil

23. the treatment of the left/right ambiguity : calculate a 2 for each
combination; choose the minimum 2 among them. This is much
faster than previous GLPK minimization. .
possible point of tangency of
the trajectory to the Skew Stt . . . . . . . . . . . . . .
MC truth
reco track
skew Stt
Mvd Pixel
Mvd Strip
Stt Parallel
SciTil

24. track reconstruction efficiency
Cpu time consumption
and
track reconstruction efficiency
with new fitters

25. Cpu time consumption, new algorithm, a factor 9 better
Cpu times on an Intel Xeon 2.13 GHz 64 bit Lenny machine
MC generation : Box Generator;
multiplicities from 1 up to 8; momenta from 0.3 to 10 GeV/c.
No Bkg
Bkg added

26. Track Reconstruction Efficiency, the same as before
% of reconstructed tracks. In these plots ‘reconstructed track’ means
AT LEAST 80% of the TRUE Stt+Mvd hits.
MC generation : Box Generator;
multiplicities from 1 up to 8; momenta from 0.3 to 10 GeV/c.
No Bkg
Bkg added

27. The factor 9 better in speed is nice but yet not enough.
How can the algorithm be
further improved ?

28. 110 times Change the way the cluster search in XY plane is performed
Presently each Stt axial hit is considered as possible cluser seed (unles
already included in a previous track). For instance in this event the
cluster finding - tracklet finding is performed 110 times :
110 times
MC truth
reco track
Mvd Pixel
Mvd Strip
Stt Parallel
SciTil
event : 1 track, P = 0.3 GeV/c + BKG

29. 13 times Change the way the cluster search in XY plane is performed
Improvement : only Stt axial hits on the geometrical boundary of the
axial Stt region are considered as possible cluster seed.
13 times
MC truth
reco track
Mvd Pixel
Mvd Strip
Stt Parallel
SciTil

30. The CpuTime still need and can be improved by changing the way
the track candidates are found with the ‘road’ methods :
a) use as seed only hits in the axial Stt at the boundary of the
detector;
b) confirm track with a SciTil hit at the earliest possible stage ;
c) reject ghost tracks with ‘holes’ at the earlier possible stage (cleanup);
d) parallelize the code.
10 Dec 2012

31. Summary
New faster fitting methods, non-recursive (non-GLPK, non-Minuit)
has been used;
2) Progress has been made in the cpu time consumption; a factor
of 9 has been gained by replacing the fitting methods;
3) a further possibly large factor can be gained by changing the
search of clusters belonging to tracklets;
4) this should also clear the path to the parallelization of the code.
10 Dec 2012