Simulations for CBM CBM-India Meeting, Jammu, 12 February 2008 V. Friese

V. Friese CBM-India, 12 February Simulations: Why? Simulation Detector description Input (Signal, Background) Simulated signal Performance (acceptance, efficiency) Running experiment Simulation Input (Signal, Background) Required performance (S/B, SNR, eff.) Simulated signal Detector design Planned experiment

V. Friese CBM-India, 12 February Simulations for detector design Consequences: –Many different detector designs / setups need being investigated –The simulation framework must be flexible enough to enable an easy switch between geometries / digitisations –Challenge in particular for reconstruction (data structures,...)

V. Friese CBM-India, 12 February Simulation steps CBMROOT Monte-Carlo Event generator UrQMD, HSD, user defined,... GEN Particles (type, momentum, vertex) MC Transport (MC) GEANT3, GEANT4, FLUKA,... MCPoint, MCTrack RAW Detector response simulation Digi EDS Reconstruction Hits, Tracks, Vertices Analysis Histogram Experiment DAQ Step Data level Data structures

V. Friese CBM-India, 12 February CBMROOT CBMROOT is the CBM software framework for simulation, reconstruction and analysis It is based on (FAIR)ROOT and VMC Execution via ROOT macros Code is written in C++ Documentation system is DOXYGEN The (current) build system is cmake, the distribution system is subversion Supported platforms are (almost) all Linux flavours External packages used: –GEANT3 –GEANT4 –ROOT –CLHEP –VMC –PYTHIA

V. Friese CBM-India, 12 February CBMROOT and FAIRROOT MVDSTSRICHTracking....basegeobaseparbase detector specific (geometry, digitisation,...) core (run manager, I/O,...) MVDSTSRICHTracking.... basegeobaseparbase CBMROOT FAIRROOT CBMROOT PANDAROOT

V. Friese CBM-India, 12 February Event generators Produces a list of particles, each with type, start vertex and momentum at start vertex, as input for the transport Available generator interfaces: –Standalone (outside of the framework, with intermediate file) UrqmdGenerator (UrQMD output ftn14, ASCII) PlutoGemerator (PLUTO output, ROOT) ShieldGenerator (SHIELD output, ASCII) AsciiGenerator (self-written ASCII, defined format) –Integrated (inside the framework, on the fly, without intermediate file) ParticleGenerator (single particles) BoxGenerator (particles with flat distribution in p, p t, y, φ) An arbitrary number of generators can be used at the same time

V. Friese CBM-India, 12 February MC transport FAIRROOT employs the concept of Virtual Monte Carlo (VMC): The user can choose between different transport engines Available engines: –GEANT3 –GEANT4 –(FLUKA in preparation) The simulation run is controlled by the manager class CbmRunSim The output is a ROOT tree. Branches are CbmMCTrack (input + secondary tracks) and objects derived from CbmMCPoint –CbmMvdPoint –CbmStsPoint –....

V. Friese CBM-India, 12 February MC Transport step by step 1. Choose engine and create run CbmRunSim* fRun = new CbmRunSim(); fRun->SetName("TGeant3"); // Transport engine fRun->SetOutputFile(outFile); // Output file CbmRuntimeDb* rtdb = fRun->GetRuntimeDb(); fRun->SetMaterials/"media.geo"); 2. Define detector geometry CbmDetector* sts = new CbmSts("STS", kTRUE); sts->SetGeometryFileName(stsGeom); fRun->AddModule(sts); 3. Define magnetic field CbmFieldMap* magField = new CbmFieldMapSym3(fieldMap); magField->SetPosition(0., 0., fieldZ); magField->SetScale(fieldScale); fRun->SetField(magField); 4. Define input CbmPrimaryGenerator* primGen = new CbmPrimaryGenerator(); CbmUrqmdGenerator* urqmdGen = new CbmUrqmdGenerator(inFile); primGen->AddGenerator(urqmdGen); fRun->SetGenerator(primGen); and run fRun->Run(nEvents);

V. Friese CBM-India, 12 February Detector simulation (digitisation) describes the detector response to the simulated MCTracks to be defined according to knowledge on the detector output: CbmDigi for each active channel workaround: HitProducer (e.g. Gaussian smearing of point) CbmStsPoint CbmStsDigi CbmStsHit CbmStsDigitize CbmStsFindHits CbmStsPoint CbmStsHit CbmStsHitProducer

V. Friese CBM-India, 12 February The CBM setup: electrons magnet RICH TRD TOF STS + MVD ECAL

V. Friese CBM-India, 12 February The CBM setup: muons STS + MVD magnet TRD ECAL

V. Friese CBM-India, 12 February Status of detector description DetectorGeometry (MC)Digitisation MVD Monolithic stationsHitProducer STS Segmented (sectors), support, cables Projective strip geometry, hit finder RICH Monolithic (PM plane)HitProducer MUCH Monolithic stations Projective pad geometry, avalanche simulation, cluster reconstruction TRD Segmented (sectors)HitProducer TOF SegmentedHitProducer ECAL Over-segmentedShower parameterisation

V. Friese CBM-India, 12 February Example: STS

V. Friese CBM-India, 12 February Example: MUCH

V. Friese CBM-India, 12 February Event reconstruction is currently done in one step with digitisation (macro) manager class is CbmRunAna input is MC data (output of transport simulation) The user defines tasks which are –initialised at the beginning of the run –executed for each event –finalised after the last event output is a ROOT tree with branches for all data structures registered to the run manager by the class

V. Friese CBM-India, 12 February Reconstruction step by step 1. Create run CbmRunAna *run= new CbmRunAna(); run->SetInputFile(inFile); run->SetOutputFile(outFile); 2. Register task(s) CbmTask* stsDigitize = new CbmStsDigitize(iVerbose); run->AddTask(stsDigitize); 3. Initialise and start run run->LoadGeometry(); run->Init(); run->Run(0,nEvents);

V. Friese CBM-India, 12 February Available reconstruction algorithms TaskAlgorithmsOutput Local STS tracking Cellular Automaton Hough Transform CbmStsTrack Local TRD tracking Cellular Automaton Track following CbmTrdTrack Local MUCH trackingTrack followingCbmMuchTrack RICH ring finding Hough Transform Elastic Net CbmRichRing Global trackingGlobal TrackerCbmGlobalTrack Main vertex findingKalman FilterCbmVertex

V. Friese CBM-India, 12 February Some results Track reconstruction efficiency in MVD+STS, CA algorithm

V. Friese CBM-India, 12 February Some more results RICH ring reconstruction with Hough transform

V. Friese CBM-India, 12 February Electron identification capabilities Composition of identified electrons Pion suppression

V. Friese CBM-India, 12 February Performance of di-electron measurements Low-mass vector mesons Acceptance for ρ meson

V. Friese CBM-India, 12 February Performance of di-electron measurements (2) Charmonia Acceptance for J/ψ meson

V. Friese CBM-India, 12 February Open charm results D 0, c τ=127 μm K-K- π+π+

V. Friese CBM-India, 12 February Open charm results (2)  c +  pK -  + D 0 ->K -  -  +  + D + ->K -  +  +