Event Reconstruction Event Reconstruction i in High Energy Physics - PowerPoint PPT Presentation

Event Reconstruction Event Reconstruction i in High Energy Physics Experiments in High Energy Physics Experiments I. Kisel I. Kisel GSI/Uni GSI/Uni- -Heidelberg, Germany Heidelberg, Germany CERN, February 07, 2008 CERN, February 07, 2008

HEP Research Centers HEP Research Centers Research Research Accelerator (GeV) Accelerator (GeV) Experiment Experiment Physics Physics Center Center PEP-II, e - x e + (9 x 3.1) SLAC, USA BaBar B-Physics D0 D0 Universal Universal Fermilab, F il b Tevatron, p x p (1000 x 1000) USA CDF Universal PHENIX Quark-Gluon-Plasma BNL, USA RHIC, Heavy Ions STAR Quark-Gluon-Plasma KEK-B, e - x e + (8 x 3.5) KEK, Japan BELLE B-Physics ATLAS Universal CMS Universal CERN, LHC, p x p (7000 x 7000) Switzerland ALICE ALICE Quark-Gluon-Plasma Quark Gluon Plasma LHCb B-Physics ZEUS Proton-Physics H1 Proton-Physics DESY, HERA e +/- x p (27 5 x 920) HERA, e x p (27.5 x 920) Germany HERMES Spin-Physics Different experiments for Different experiments for HERA-B B-Physics different physics, but with different physics, but with PANDA Quark-Physics GSI, common tracking problems common tracking problems SIS 100/300, p, Heavy Ions Germany CBM CBM Quark-Gluon-Plasma Quark Gluon Plasma 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni Ivan Kisel, GSI/Uni- -Heidelberg Heidelberg 2/32 /32

HEP Experiments: Collider and Fixed HEP Experiments: Collider and Fixed- -Target Target Beam Beam Beam Target Inelastic collisions 10 7 – 10 9 10 10 11 11 Reconstructed tracks with pt > 25 GeV Signal events Signal events 10 2 – 10 -2 High energy = high density + high rate High energy = high density + high rate High energy = high density + high rate High energy = high density + high rate CBM (FAI R/ GSI ) CBM (FAI R/ GSI ) ATLAS (CERN) ATLAS (CERN) 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni Ivan Kisel, GSI/Uni- -Heidelberg Heidelberg 3/32 /32

Methods for Event Reconstruction Methods for Event Reconstruction 2 1 Track finding Track finding Track fitting Track fitting Kalman Kalman Filter Filter Filter Filter Time Time consuming!! consuming!! ! Combinatorics Combinatorics + Precision + Precision = Speed ? = Speed ? S S d ? d ? Kalman Kalman 3 Filter Filter Vertex finding/ fitting Vertex finding/ fitting • • Global Methods Global Methods Global Methods Global Methods • all hits are treated equivalently • typical methods: • Conformal Mapping Conformal Mapping • Histogramming Histogramming • Hough Transformation Hough Transformation Hough Transformation Hough Transformation 4 • Local Methods Local Methods PI D: Ring finding PI D: Ring finding • sequential selection of candidates • typical methods: Combinatorics Combinatorics • Track following Track following • Kalman Filter Kalman Filter • Neural Networks Neural Networks • combine local and global relations • typical methods: • Perceptron • Perceptron Perceptron Perceptron • Hopfield network Hopfield network • Cellular Automaton Cellular Automaton • Elastic Net Elastic Net 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni- Ivan Kisel, GSI/Uni -Heidelberg Heidelberg 4/32 /32

Global Methods: Global Methods: Conformal Mapping + Histogramming Conformal Mapping + Histogramming Triggers Triggers Global methods are especially suitable for fast tracking in projections Example: Collider experiment with a solenoid, where tracks are circular trajectories Histogram: Histogram: g Conformal Mapping: Conformal Mapping: Conformal Mapping: Conformal Mapping: Collect a histogram of azimuth angles φ Transform circles into straight lines u = x/(x 2 + y 2 ) Find peaks in the histogram v = -y/(x 2 + y 2 ) Simple Simple Collect hits into tracks Fast Fast φ v y φ u x Useful implemented in hardware and for very simple event topologies Useful implemented in hardware and for very simple event topologies 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni Ivan Kisel, GSI/Uni- -Heidelberg Heidelberg 5/32 /32

Global Methods: Global Methods: Hough Transformation Hough Transformation Measurement Space Measurement Space Parameter Space Parameter Space y = a* x + b y = a* x + b b = b = -x* a + y x* a + y y b x a Useful implemented in hardware and for simple event and trigger topologies Useful implemented in hardware and for simple event and trigger topologies 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni Ivan Kisel, GSI/Uni- -Heidelberg Heidelberg 6/32 /32

Local Methods: Kalman Filter for Track Finding Local Methods: Kalman Filter for Track Finding One Processing Unit Consecutively hit by hit One Processing Unit Consecutively hit by hit Seeding Planes Seeding Planes Detector Detector Efficiency Efficiency ??? ??? Detector Detector Detector Detector Efficiency Efficiency ??? ??? Ooop Ooop s Useful for final track fitting and for Monte Carlo analysis of an experiment Useful for final track fitting and for Monte Carlo analysis of an experiment 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni Ivan Kisel, GSI/Uni- -Heidelberg Heidelberg 7/32 /32

Neural Networks: Neural Networks: Cellular Automaton Cellular Automaton – – Game „Life“ Game „Life“ M. Gardner, Scientific American, 223 (October 1970), 120 123 M Gardner Scientific American 223 (October 1970) 120-123 Each cell has 8 neighboring cells, 4 adjacent orthogonally, 4 adjacent diagonally. The rules are: Survivals. Every counter with 2 or 3 neighboring counters survives for the next generation. Deaths. Each counter with 4 or more neighbors dies from overpopulation. Every counter with 1 neighbor or none dies from isolation none dies from isolation. Births. Each empty cell adjacent to exactly 3 neighbors is a birth cell. TRACKING ! TRACKING ! It is important to understand that all births and deaths occur simultaneously . GHOST TRACK ? RECO TRACK TRACK NOISE ! NOISE ! RECO TRACK RECO TRACK TRACK ! TRACK ! TRACK ! TRACK ! no convergence ! 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni Ivan Kisel, GSI/Uni- -Heidelberg Heidelberg 8/32 /32

Neural Networks: Neural Networks: Cellular Automaton Cellular Automaton – – Animation Animation 2. Segments 1. Hits 3. Counters 1 2 4 3 5. Tracks 4. Track-candidates Useful for analysis of experiments with real data Useful for analysis of experiments with real data 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni Ivan Kisel, GSI/Uni- -Heidelberg Heidelberg 9/32 /32

Competition Competition CATS( CATS(CA CA)/ RANGER( )/ RANGER(KF KF)/ TEMA( )/ TEMA(HT HT) (HERA ) (HERA- -B, DESY) B, DESY) Tracking efficiency Tracking efficiency Tracking quality Tracking quality cy cy Efficienc Efficienc N inel inel x 50 tracks x 50 tracks Time consumption Time consumption Time/event (sec) ) Time/event (sec) The reconstruction package CATS CATS based on the Cellular Automaton for track finding Cellular Automaton for track finding and g the Kalman Filter for track fitting Kalman Filter for track fitting outperforms alternative packages (SUSi, HOLMES, L2Sili, OSCAR, RANGER, SUSi, HOLMES, L2Sili, OSCAR, RANGER, TEMA TEMA) ) based on traditional methods in efficiency, accuracy and speed in efficiency accuracy and speed N inel inel x 50 tracks x 50 tracks 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni Ivan Kisel, GSI/Uni- -Heidelberg Heidelberg 10 10/32 /32

Data Acquisition System Data Acquisition System 50 kB 50 kB/ / ev ev RU RURU RU RURU RU RURU RU RURU RU RU RU RU Detector Detector RURU RU RU RURU RU RURU RURU RU RU RU RU RU 10 7 ev/ s 10 7 ev/ s 10 10 ev/ s ev/ s 100 100 ev ev/ / slice slice SF n Δ t SF n Δ t SF n Δ t SF n Δ t SF n Δ t MAPS STS RI CH TRD ECAL Event Event E E t t N x M N x M Scheduler Scheduler Builder Builder Network Network available SF n Δ t MAPS STS RI CH TRD ECAL SF n Farm Control System 5 M 5 MB B/ / slice slice Sub Farm Sub-Farm Sub Farm Sub-Farm Sub Farm Sub-Farm Sub-Farm Sub Farm Sub-Farm Sub Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm 10 5 sl 10 sl/ s / s PC Farm PC Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm ~ 1000 ~ 1000 PCs PCs PCs PCs 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni- Ivan Kisel, GSI/Uni -Heidelberg Heidelberg 11 11/32 /32

CBM: CBM: PC Sub PC Sub- -Farm Farm Scheduler I nput Data Farm Control System -Farm Sub-Farm Sub-Farm Sub-Farm P/AB P/AB P/AB P/AB P/AB P/AB P/AB P/AB /AB /AB /AB /AB HWP HWP HWP HWP HWP HWP HWP HWP HWP HWP HWP HWP Pnet Pnet Pnet SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP SWP ready ⇒ HLT C+ + , Framework, GEANT started ⇒ L1 CPU C+ + , Framework, GEANT future ⇒ L1 FPGA C+ + , SystemC, SystemCrafter, VHDL 07 February 2008, CERN 07 February 2008, CERN Ivan Kisel, GSI/Uni Ivan Kisel, GSI/Uni- -Heidelberg Heidelberg 12 12/32 /32