development of the parallel TPC tracking Marian Ivanov CERN

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 z 2 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 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 t i,p i – time - phase of the helix in DCA x1,y1, t1, p1 x2,y2, t2, p2 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