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Changes in TPC tracking(2) Motivated by V0 studies in TPC Increase tracking efficiency for secondary particles new (combinatorial) seeding implemented track primary particles decaying deep inside of the TPC continuous seeding in TPC added improve momentum and position resolution for secondary particles eliminate systematic shifts due to the vertex constrain controversially - speed-up tracking code 2.2 min for full event - -g option 1.2 min –o2 option
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development of the parallel TPC tracking
Marian IvanovCERN
Changes in TPC tracking(1) Preparation for the PARALEL combined
tracking new functionality added forward and backward propagation SetIO function to specify input and output for
tracking – only place in the code related to IO algorithm independent of IO ESD input and output enabled in parallel possible to write “standard” output for
tracking –TreeT… …
Changes in TPC tracking(2) Motivated by V0 studies in TPC
Increase tracking efficiency for secondary particles
new (combinatorial) seeding implemented track primary particles decaying deep inside of
the TPC continuous seeding in TPC added
improve momentum and position resolution for secondary particles
eliminate systematic shifts due to the vertex constrain controversially - speed-up tracking code
2.2 min for full event - -g option 1.2 min –o2 option
Changes in TPC tracking(3) AliHelix implemented
special class for geometrical calculation track propagation DCA calculation current momenta calculation
interfaced to AliKalmanTrack, TParticle and AliTrackReference
easier to compare reconstruction with MC data
Changes in TPC tracking(4)
necessary to implement new TPC comparison
correlation analysis with user defined cuts enabled
many x many problem solved curling track are multiple
reconstructed (properly or improperly)
generated output – TTree with branches for track MC and reconstructed information
Changes in TPC tracking(5) new classes implemented
AliTPCGenInfo contain relevant MC information for given track:
TParticle, container with track references, digit information (map of padrows which were hitted by track +queries –first … last pad row, number hitted pad-rows…), mean Nprim (~dEdx)
AliTPCRecInfo AliTPCtrack + derived preprocessed information
necessary for easier correlation study AliTPCV0Info
contain AliTPCGenInfo for mother and daughter particle
characteristic of the vertex
New seeding with vertex constrain
goals: don’t seed ‘evidently’ secondary particles reduce N2 problem speed-up factor 10 for dNdy 8000
before loop over clusters in layer 2 geometrical transformation coefficient calculated
shift, rotation, shrink vertex[0,0,0], X1 [1,0,z1]
fast cuts implemented z2 coordinate of cluster2 2 given by position of
vertex not used point near intersection of
“hypothetical” track with middle pad-row required
additional cut after kalman tracking between layer 1-2
if track does not point to z vertex founded clusters are reused used by fast MakeSeed without vertex constrain
New seeding without vertex constrain old seeding
fast but … low efficiency for strongly inclined tracks due to the
angular effect correlating errors between neighboring pad-rows
solution – combinatorial seeding to minimize correlation
distance between seeding pad-rows small (tested with 7 padrows)
hypothetical required cluster at the middle calculated using linear aproximation
more efficient but slower than old seeding used only after “fast” seeding with vertex constrain
New tracking strategy (2) loop over different seeding region
seeding with vertex constrain tracking of seeds down to the innermost sector updating statistical information
mean track quantities and their dispersions (number of accepted clusters, cluster density, chi2)
goal - to have unique cuts for different multiplicities sign clusters belonging to tracks with
acceptable quality (n-sigma cut, with n as parameter)
similar loop over different seeding region – seeding without vertex constrain
Efficiency (dNdy=2000)
left side – efficiency for tracking of primaries decayed in TPC at radius r right side– efficiency for tracking of secondaries created in TPC at radius r integral efficiency according old criteria (defined in AliTPCComparison.C)
99.9% for primaries 99.5% primaries + secondaries
Kink and V0 finding strategy step 1: tracking
looking for all possible – even very short track candidates several seeding in different region of TPC necessary to find
both mother and also daughter particles for step 2: combinatorial search for Kink and V0
fiducial volume – given by tracking efficiency, track parameter precision and track density
kink 120-220 cm minimal DCA
cut on n (currently 6) sigma N2 problem
causality cut probability that primary track continue after DCA point and that
secondary has prolongation even before DCA based on the track - cluster density before, respectively after DCA should be optimized for different track densities
Kink fiducial volume volume given by
seeding and tracking efficiency for “short” track
better seeding and tracking strategy
Kink vertex resolution(1)
better r resolution (0.18 cm comparing to 0.3 reported during last offline week)+ non systemetic effects
Kink vertex resolution (2) OK, but:
improvement because we stop tracks with high chi2 and non acceptable space resolution to don’t take clusters from other tracks
also non secondary tracks can be stopped not sufficient information about the track overlaps
worse dEdx resolution for high multiplicity event after kink and finding – the tracks have to be
post processed kink and V0 finding in the TPC volume has to be
performed during TPC tracking
New kink finder - strategy N2 problem with combinatorial search
very fast cut necessary Linear loop:
AliHelix defined during linear track preprocessing N2 loop:
fast analytical calculation of track intersection or DCA in rφ projection
rough cut on nearest point radii in rφ projection analytical calculation of DCA in two or one local minima
from rφ direction – calculated in 3 dimension stronger cut applied on R and distance DCA calculation using hessian approximation final cuts on DCA Kink properties calculation
AliHelix N2 problem with combinatorial search of
V0 and kink finder AliHelix
definition during sequential loop track preprocessing – or reading
used for all DCA geometrical calculation data layout optimized for fast computation of
DCA global coordinate system used – no
transformation - rotation needed during time critical combinatorial search
DCA calculation in rφ projection
three considered situation x, y – global position of
the DCA in rφ ti,pi – time - phase of the
helix in DCA
x1,y1, t1, p1
x2,y2, t2, p2
x1,y1, t1, p1
x1,y1, t1, p1
Linear versus Hessian DCA calculation
started directly from the two local minima
linear DCA approximation faster
the resolution on the level of slower Hessian calculation – (three iteration used)
both are implemented in AliHelix
Parallel incremental tracking – AliBarrelTracker Tracking using information from different
detectors Requirements
as fast as possible as efficient as possible as “good” as possible as modular as possible all other criteria (backward compatibility, dependency
problems) lower priority – taken only as technical complication
in TPC tracker – already implemented some of the basic functionality
Conclusion AliTPCtracker strongly updated
cvsa diff AliTPCtrackerMI 4000 lines improvement in efficiency and pt resolution for secondary
particles speedup of the code (seeding, error parametrization, faster
navigation through the clusters using look-up table, …) because of reported problems with dEdx, commit planned
only after implementation of V0 finder during TPC tracking AliHelix – stand alone class
ready to commit now new comparison
planned commit after conversion to the new IO V0 finder – to be committed together with TPC tracker
as integral part