1. road – Hough transform- c2
Bochum, September 10th , 2013
Status of the
Pattern Recognition
with the hybrid
road – Hough transform- c2
method
Gianluigi Boca
Pavia University
Gianluigi Boca, 10/9/2013

2. Outlook of this talk Presentation of the block diagram of the Pattern
Recognition for the central region;
Presentation of the new idea of an ‘annealing filter’ type
of algorithm for the fit in the Z space;
Final considerations on the present status and future steps
to further improve and todo list.
Gianluigi Boca, 10/9/2013

3. Pattern Recognition code and the ‘annealing filter’ for the Z fit
Scheme of
the present structure
of the
Pattern Recognition code
and
the ‘annealing filter’ for the
Z fit
and performances

4. Present scheme of the offline PR for the central region
STT Axial hit Cluster Finder
loop over Axial Stt hit clusters and find track candidate in XY
cleanup of track using the XY info on the track candidate
loop over XY track candidate and associate skew STT hits;
find the complete trajectory Helix with Skew Stt + Mvd + SciTil hits
cleanup of track using the full info on the track candidate

5. Present scheme of the offline PR for the central region
STT Axial hit Cluster Finder
loop over Axial Stt hit clusters and find track candidate in XY
cleanup of track using the XY info on the track candidate
loop over XY track candidate and associate skew STT hits;
find the complete trajectory Helix with Skew Stt + Mvd + SciTil hits
cleanup of track using the full info on the track candidate

6. loop over Axial Stt hit clusters and find track candidate in XY
Hough method (Legendre) with Cluster hits
to fit & find track parameters in XY
use track parameters to associate Axial STT hits to track and decide
the point on the drift circle where the track passed
Associate Mvd hits
Fit again with 2 & find final track parameters in XY
Associate again Mvd hits
Associate SciTil hits hits
Order Mvd + STT axial hits
Cleanup XY track candidate

7. Present scheme of the offline PR for the central region
STT Axial hit Cluster Finder
loop over Axial Stt hit clusters and find track candidate in XY
cleanup of track using the XY info on the track candidate
loop over XY track candidate and associate skew STT hits;
find the complete trajectory Helix with Skew Stt + Mvd + SciTil hits
cleanup of track using the full info on the track candidate

8. Present scheme of the offline PR for the central region
STT Axial hit Cluster Finder
loop over Axial Stt hit clusters and find track candidate in XY
cleanup of track using the XY info on the track candidate
loop over XY track candidate and associate skew STT hits;
find the complete trajectory Helix with Skew Stt + Mvd + SciTil hits
cleanup of track using the full info on the track candidate

9. loop over XY track candidate and associate skew STT hits;
find the complete trajectory Helix with Skew Stt + Mvd + SciTil hits
use track parameters to associate Skew STT hits to track
Fit in Z space with 2 & find remaining track parameters
WITH ANNEALING FILTER OF AT MOST 1 MVD HIT
Reject spurious Skew Stt and Mvd hits based on the result of fit in Z

10. 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
Mvd noise hit eliminated by

11. 1) the treatment of the left/right ambiguity : calculate a 2 for each
combination; choose the minimum 2 among them.
2) ‘Annealing filter‘ scheme is also implemented : drop 1 Mvd hit from
2 calculation and replace it with a penalty term ( = 36) . It works
fine agains noise Mvd hits. .
possible point of tangency of
the trajectory to the Skew Stt . . . . . . . . . . . . . .
MC truth
reco track
skew Stt
Mvd Pixel
Mvd Strip
Stt Parallel
SciTil
Mvd noise hit eliminated by
‘annealing filter’ algorithm

12. % of reconstruct-ed tracks
Performance : Track Reconstruction Efficiency
MC Box Generator; % of
reconstructed tracks
(‘reconstructed track’
means a found
track associated to
a MC truth track);
Total P
GeV/c
Generated
tracks
per event
Total #
good tracks
gene-rated
% of reconstruct-ed tracks
fake
per
good
event
Wrong
|| hits associ-ated
(%)
Wrong skew
hits associ-ated
0.3 1
3981
99.4
0.0
0.1 4
3986
94.9
1.2
4.2 8
3983
87.8
0.2
2.6
9.8
1.0
3871
3874
94.7
1.3
3.5
3892
88.2
2.7
8.3
2.0
3875
99.5
0.4
3858
94.5
3.6
3866
89.4
2.8
7.9
5.0
3872
99.2
3831
94.2
10.0
3886
0.5
Events
without
Background
( == pileup)

13. % of reconstruct-ed tracks
Performance : Track Reconstruction Efficiency
MC Box Generator; % of
reconstructed tracks
(‘reconstructed track’
means a found
track associated to
a MC truth track);
Total P
GeV/c
Generated
tracks
per event
Total #
good tracks
gene-rated
% of reconstruct-ed tracks
fake
per
good
event
Wrong
|| hits associ-ated
(%)
Wrong skew
hits associ-ated
0.3 1
3981
88.6
2.5
8.8
14.6 4
3986
85.1
2.8
9.7
17.4 8
3983
80.2
3.1
11.9
22.7
1.0
3871
89.4
8.2
13.7
3874
85.2
2.9
10.6
17.2
3892
81.6
3.2
12.2
21.9
2.0
3875
88.8
9.3
14.0
3858
85.9
16.7
3866
82.4
13.6
21.3
5.0
3872
9.0
13.2
3831
86.3
10.9
16.6
10.0
3886
90.2
9.5
13.8
Events
with
Background
( == pileup)
performance
slightly better
than previous
algorithm but
more spurious hits

14. Performance : Track Reconstruction Efficiency
% of the reconstructed tracks having 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.
I don’t quite
understand
Improvement
in efficiency!.
No Bkg
Bkg added

15. Cpu time consumption
Cpu times measured on an Intel i7-2600K 3.4 GHz 64 bit
MC generation : Box Generator;
multiplicities from 1 up to 8; momenta from 0.3 to 10 GeV/c.
No Bkg
Bkg added

16. By the way:
noise hits in Mvd put in NOT correctly, this may influencethe Pattern Recognition heavily; waiting for someone to fix this.

17. Final considerations on the present status and future steps
to further improve
The idea of the ‘annealing filter’ for the Z works nicely
for the event without background (better efficiency);
it is not understood why it works poorerly instead for
the events with background; working on it !
I am also working at the Cleanup algorithm for the events
with background; not enough progress to report here today;
The next step will be the parallelization.
Gianluigi Boca, 10/9/2013