tracking in the cbm experiment i. kisel kirchhoff institute of physics, university of heidelberg...

Tracking Tracking in the CBM experimentin the CBM experiment

I. KiselI. Kisel Kirchhoff Institute of Physics, Kirchhoff Institute of Physics,

University of HeidelbergUniversity of Heidelberg(for the CBM Collaboration)(for the CBM Collaboration)

TIME05, Zurich, SwitzerlandOctober 03-07, 2005

KIPKIPCBCBMM

03-07 October 2005, TIME0503-07 October 2005, TIME05 Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 22/12/12

FFacility for acility for AAntiproton and ntiproton and IIon on RResearch (GSI, esearch (GSI, Darmstadt)Darmstadt)

Future accelerator complex FAIR at GSI, Darmstadt:

Research program includes:Research program includes:

• Radioactive Ion beams:Radioactive Ion beams: Structure of nuclei far from stability

• Anti-proton beams:Anti-proton beams: hadron spectroscopy, anti hydrogen

• Ion and laser induced plasmas:Ion and laser induced plasmas: High energy density in matter

• High-energy nuclear collisions:High-energy nuclear collisions: Strongly interacting matter at high baryon densities

SIS 100 Tm

SIS 300 Tm

U: 35 AGeV

p: 90 GeV

Compressed Baryonic Matter (CBM) Experiment


FFacility for acility for AAntiproton and ntiproton and IIon on RResearchesearch

Photomontage of the existing and the planned research facility at GSI/FAIR.


Tracking Nuclear CollisionsTracking Nuclear Collisions

Tracking challenge:Tracking challenge:

107 Au+Au reactions/sec

~ 1000 charged particles/event momentum measurement with resolution < 1% secondary vertex reconstruction ( 30 m) high speed data acquisition and trigger system

Physics Observables

In-medium modifications of hadrons:onset of chiral symmetry restoration

, , e+e- (μ+ μ-)open charm: D0, D±

Strangeness in matter:enhanced strangeness production K, , , ,

Indications for deconfinement:anomalous charmonium suppression ? D0, D±, J/ e+e- (μ+ μ-)

Critical point:event-by-event fluctuations , K

Open charm measurement: one of the prime interests of CBM, and one of the most difficult tasks


The Compressed Baryonic Matter (CBM) ExperimentThe Compressed Baryonic Matter (CBM) Experiment

Tracking, momentum measurement, vertex reconstruction: Radiation hard silicon pixel/strip detectors in a magnetic dipole Radiation hard silicon pixel/strip detectors in a magnetic dipole fieldfield

Electron ID: RICH & TRD (& ECAL)RICH & TRD (& ECAL)

Hadron ID: TOF (& RICH)TOF (& RICH)

Photons, 0, : ECALECAL

High interaction rates

beambeam

targettarget

STSSTS(5, 10, 20, 40, 60, 80, 100 cm)(5, 10, 20, 40, 60, 80, 100 cm)

TRDsTRDs(4,6, 8 m)(4,6, 8 m)

TOFTOF(10 m)(10 m)

ECALECAL(12 m)(12 m)

RICHRICH

magnetmagnet


Modular Structure of DAQModular Structure of DAQMAPS, STS RICH ECAL

DetectorDetector

PC FarmPC Farm

Event Builder Event Builder NetworkNetwork

101077 ev/s ev/s

101055 slsl/s/s

50 50 kBkB//evev

100 100 evev//sliceslice

5 M5 MBB//sliceslice

N x MN x MN x MN x MSchedulerSchedulerSchedulerScheduler

TRD

Sub-FarmSub-Farm

RURURURURURURURURURURURURURURURU

RURURURURURURURURURURURURURURURU

Sub-FarmSub-Farm Sub-FarmSub-Farm Sub-FarmSub-Farm Sub-FarmSub-Farm

Farm Control System

Sub-FarmSub-FarmSub-FarmSub-Farm Sub-FarmSub-Farm Sub-FarmSub-Farm Sub-FarmSub-Farm




SF

n

availab

le

SFnt MAPS STS RICH TRD ECAL

SFnt MAPS STS RICH TRD ECAL

SFntSFnt SFnt SFnt

HLTHLT C++, Framework, GEANT

L1 CPUL1 CPU C++, Framework, GEANT

L1 FPGAL1 FPGA C++, SystemC, VHDL


Cellular Automaton method for track findingCellular Automaton method for track finding

1. Generate a set of tracklets (similar to seeding). Tracklets are created everywhere – all chambers are seeding chambers. The same set of cuts can be applied as in the Kalman Filter track finder – the cuts reflect geometrical acceptance of a detector that is common for all methods. As hits are sorted, tracklets are generated in groups with the same leftmost hit (due to inserted loops over chambers). Therefore, every hit stores two pointers – to the first and last tracklets of his group. Every tracklet has a counter meaning possible position on a track (initially 0).

2. Extrapolate tracklets back to the previous layer. Usually tracklets are created (as in KF) starting from the downstream chambers and moving to the target. Therefore, during generation of the next portion of tracklets (one or two chambers closer to the target) the algorithm applies the track model to the tracklets with a common point (simple selection of the tracklets using the stored pointers, see 1).

3. Find neighbors and increase the counter. If neighbors (possible track continuations) are found, a counter of a current tracklet is incremented with respect to a neighbor with the largest counter.

4. Continue to 1 for all chambers.5. Collect track candidates. Start with tracklets having the largest counter (max_counter), for each of them take a neighbor

(at the right) which has a counter=max_counter-1, continue similar to the Simple Kalman Filter but follow counters (!), make branches, but no empty layers, keep the best (chi2) track for each initial tracklet with the largest counter.

6. Apply competition between track-candidates. After step 5 a set of track-candidates of the same length is created, therefore chi2 is well suitable criterion to sort them. After sorting start with the best (chi2) track and flag all hits of the track as used. Continue with the next track (with lower chi2), check if number of used hits is less than X (parameter, depends on track density) and flag his hits as used or delete the track. Proceed with the next track-candidate etc.

7. Continue to step 5, but collect tracks starting with the counter = max_counter-1. Proceed 5-7 decrementing max_counter until the shortest tracks (usually of length=tracklet_length+1) are collected.

8. Merge clones if necessary. In case of significant detector inefficiency merge short tracks into long tracks.9. Kill ghost. Apply additional cuts to kill ghost tracks, most of them are short tracks.

Implementations:• ARES (NIM A329, 1993) • NEMO (NIM A387, 1997) • HERA-B (NIM A489, 2002; NIM A490, 2002) • LHCb (LHCb note 2003-064, 2003)• CBM (CHEP04, 2004)(see http://www-linux.gsi.de/~ikisel/reco/ )

Drawing analogy to the Kalman method one can consider steps 1-4 as FilterFilter, 5-7 as SmootherSmoother, and 8-9 as CleanerCleaner.

http://www-linux.gsi.de/~ikisel/reco/




Performance of track findingPerformance of track finding

Track categoryTrack category Efficiency, %Efficiency, %

Reference set 99.45

All set 96.98

Extra set 89.46

Clone 0.01

Ghost 0.61

ALL MC TRACKSALL MC TRACKSRECONSTRUCTABLE TRACKS

Number of hits >= 4

REFERENCE TRACKS

Momentum > 1 GeV

S. Gorbunov, I. Kisel and I. Vassiliev, Analysis of D0 meson detection in Au+Au collisions at 25 AGeV, CBM-PHYS-note-2005-001


Tracking in non-homogeneous magnetic fieldTracking in non-homogeneous magnetic field

Method Residuals Pulls

p/p x y tx ty q/p x y tx ty

Runge-Kutta 4 0.64

27 24 1.5 1.5 1.17

1.05

1.01

1.02

1.00

Analytic 3 0.64

27 24 1.5 1.5 1.18

1.05

1.00

1.02

1.00

Analytic 2 0.68 27 24 1.5 1.5 1.30 1.08 1.01 1.03 1.00

Analytic 1 0.94 30 25 1.5 1.5 1.90 1.37 1.03 1.10 1.02

Analytic Light 0.64

27 24 1.5 1.5 1.19

1.05

1.00

1.02

1.00

Analytic Central 2.49 38 25 1.7 1.5 3.77 2.23 1.03 1.33 1.00

• The precision of extrapolation does not depend on a shape of the magnetic field.• One can cut off the higher-order terms in the series.

S. Gorbunov and I. Kisel, An analytic formula for track extrapolation in an inhomogeneous magnetic field, CBM-SOFT-note-2005-001


Elastic net for the traveling salesman problemElastic net for the traveling salesman problem

Discrete ENDiscrete EN

(*) Pentium IV/2.4 GHz(*) Pentium IV/2.4 GHz

R. Durbin and D. Willshaw, An analogue approach to the travelling salesman problem, Nature, 326 (1987) 689


Standalone elastic net ring finder in RICHStandalone elastic net ring finder in RICH

All set: N hits ≥ 5Ref set: N hits ≥ 15Extra set: 5 ≤ N hits < 15Reconstructed: ≥ 70% hits from the same MCClone: MC reconstructed few timesGhost: < 70% hits from the same MC

Reference set 94.3

All set 74.0

Extra set 65.5

Clone rate 0.8

Ghost rate 12.8

Hits/event 1394

Found MC rings/event 39

Time/event 5.4 ms

Time/hit 3.9 s

S. Gorbunov and I. Kisel, Elastic net for standalone RICH ring finding, CBM-SOFT-note-2005-002


SummarySummary

• High track density at high rateHigh track density at high rate• Most crucial blocks of the (off-line) reconstruction code readyMost crucial blocks of the (off-line) reconstruction code ready• Work on detector optimizationWork on detector optimization• CBM notes and other publications on reconstruction at:CBM notes and other publications on reconstruction at: http://www-linux.gsi.de/~ikisel/reco/ • Participants from the CBM experiment:Participants from the CBM experiment: Walter Müller, Johann Heuser, Iouri Vassiliev and Ivan KiselWalter Müller, Johann Heuser, Iouri Vassiliev and Ivan Kisel


tracking in the cbm experiment i. kisel kirchhoff institute of physics, university of heidelberg...

Documents

kip cbm slide

time05ivan kisel

rich magnet slide

radioactive ion beams

ion research gsi

vhdl slide

ion research photomontage

tof rich ecal photons