Download presentation

Presentation is loading. Please wait.

Published byGrace Dunkley Modified over 2 years ago

1
Combined tracking based on MIP. Proposal Marian Ivanov

2
Maximal information principle Basic principle use everything what you can, you will get the best we are maximalist Algorithms and data structures has to be optimized for fast access and usage of all of the relevant information's

3
Combined tracking based on MIP. Assumptions. 1)Current algorithms and supporting data structures for combined tracking don’t allow to apply MIP 1)Suitable only for primary tracks 2)Do we need algorithm based on MIP? 1)of course, we want the best 3)but, we are also realists 1)iterative approach preferable, framework has to work in each step of algorithm development 1)possibility to compare different algorithms 2)don’t bother other programmers 2)memory and CPU time restrictions 4)and - very important 1)If algorithm based on MIP gives worse results, the problem is in the algorithm not in MIP.

4
Outlook Possible sources of information Access when and how is the information available? Reliability Probabilistic interpretation Usage - on which probability level to discard some information Kink and V0 finder what can be used - examples general strategy

5
Sources of the information spatial characteristic of a track and sets of tracks px,py,pz,y,z parameters and covariance chi2 number of points on the track number of shared clusters on the track overlaps between tracks DCA for V0s, Kinks and Cascades … dEdx mean, sigma, number of points, number of shared points… reliability TOF of a track and sets of tracks derived variables Mass Probability that particle “ really exists” in some space interval (used for causality cuts) Invariant mass Pointing angle of neutral mother particle …

6
Access to the information How to query information? natural way – Query(“type”,AliDetectorTracker, AliDetectorTrack, AliDetectorCluster) When? AliDetectorTracker has to have access to relevant information's

7
DCA and Helix approximation V0, Kink and Cascade finder DCA calculation – helix approximation used Helix approximation not enough precise multiple scattering energy losses in the material non homogenous magnetic field presence of the fake clusters high multiplicity events – non correlated tracks Kink – track associated to daughter particle admixture of the clusters created by mother particle during forward propagation and vice versa for backward propagation Solution Iterative process Find V0, Kink and Cascade vertex using Helix approximation Refit tracks towards to the vertex refine DCA

8
Seeding To maximize fiducial volume for Kinks, V0s and cascades – seeding (track fragments finding) algorithm has to be implemented in each barrel tracking detector TPC - fast continuous seeding implemented ITS - standalone seeding and tracking with vertex constrain implemented necessary to speed it up new fast seeding without vertex constrain is currently tested TRD - extremely slow seeding with vertex constrain, not usable

9
Kinks – schematic view TRD TPC ITS TOF

10
V0s – schematic view TRD TPC ITS TOF

11
Combined tracking algorithm. Proposal (step 0) TPC seeding TPC tracking inward Kink and V0 finding DCA calculation rough cuts tracks refits toward to the vertex obtained in the first approximation refined (but still raw) cuts Tracks and V0s arrays defined tracks - with references to all possible V0 V0 - with references to the tracks

12
Combined tracking algorithm Proposal (step 1) ITS tracking inward with vertex constrain ITS seeding with vertex constrain ITS tracking without vertex constrain ITS seeding without vertex constrain Parallel tracking Kink and V0 finding part refined cuts for V0 candidates found in the TPC applied new DCA calculation in the fiducial volume rough cuts defined probability level for the “signal” tracks refits toward to the vertex obtained in the first approximation refined cuts

13
Combined tracking algorithm Proposal (step 2) TPC tracking backward ++ for mother particle of hypothetical kinks track fit towards the vertex (ITS information already included)

14
Combined tracking algorithm Proposal (step 3) TRD tracking backward TRD seeding Kink and V0 finding part refined cuts for V0 candidates new DCA calculation in the fiducial volume rough cuts tracks refits toward to the vertex obtained in the first approximation refined cuts

15
Combined tracking algorithm Proposal (step 4) TOF matching building of the tree of hypothesis “Parallel tracking” – as in ITS Kink and V0 finding part refined cuts for V0 candidates

16
Combined tracking algorithm Proposal (step 5) TRD inward tracking refined cuts for V0s TPC inward tracking refined cuts for V0s ITS inward tracking refined cuts for V0s

17
Time schedule TPC inward tracking – done kink and V0 finder in TPC fiducial volume Beta version of algorithm (based on AliHelix – see MI presentation on ALICE week in HEIDELBERG) implemented to be tuned ITS tracking - done fast ITS seeding implemented to be tuned kink and V0 finder in fiducial volume plan to finish it before September (CHEP conference 2004, next ALICE week)

18
Conclusion strategy for combined tracking based on MIP proposed time schedule for first stage defined overall time scheduled to be defined according experiences obtained from first stage

Similar presentations

OK

PHYSTAT2007, CERN Geneva, 27-29 June 2007 ALICE Statistical Wish-List I. Belikov, on behalf of the ALICE collaboration.

PHYSTAT2007, CERN Geneva, 27-29 June 2007 ALICE Statistical Wish-List I. Belikov, on behalf of the ALICE collaboration.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Convert pdf to ppt online adobe Ppt on acute and chronic diseases 360 degree customer view ppt on iphone Ppt on marketing class 12 Ppt on panel discussion presentation Ppt on any one mathematician jobs Ppt on mineral resources in india Ppt on conservation of wildlife and natural vegetation of north Ppt on human nutrition and digestion chapter Ppt on air powered vehicle