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ECAL software development

ECAL software development . Yuri Kharlov IHEP Protvino. Goals of simulations. ECAL consists of O(10 4 ) channels of 3 different lateral sizes Each channel contains O(10 2 ) samples

erin-osborn
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ECAL software development

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  1. ECAL software development Yuri Kharlov IHEP Protvino

  2. Goals of simulations • ECAL consists of O(104) channels of 3 different lateral sizes • Each channel contains O(102) samples • TSR geometry contains 11712 cells of 3x3 cm2, 6448 cells of 6x6 cm2, 5592 cells of 12x12 cm2: in total 23752 cells. • Each cell contains 200-400 layers of Lead+Tyvek+Scintillator • Total 2107 volumes • ECAL performance strongly depends on the lateral and longitudinal segmentation • Goal 1: optimize overall detector layout • Goal 2: optimize detector granularity and sampling • Goal 3: simulate ECAL performance for physics signal detection

  3. TSR geometry

  4. Why 109 volumes instead of 1? • Main features of any calorimeter: • Energy resolution E/E • Position resolution x • Shape of a shower produced by different particles • Energy resolution depends on the cell sampling • Position resolution is defined as a shower gravity center • E/E and x affects the ECAL ability to measure particle spectra (, 0) • The granularity determines the detector cost • As long as final granularity is still a subject to study, we use 11-cm2 cells  109 volumes

  5. Shower shape • ECAL response to particles is a shower • Electromagnetic shower involves up to 25 cells • Hadronic shower may fire 1-5 cells • In high-multiplicity environment showers overlap • Shower shape is used in the reconstruction algorithm for overlapped shower unfolding and correct calculation of electomagnetic component of a shower • Although e.m. shower shape can be parametrized, such parameterization does not include fluctuations. Hadronic showers are not parameterized at all. • Only full simulation can provide correct ECAL response and thus allows to study ECAL performance

  6. Geometry: ascii file vs C++ code • Description of sizes and positions of 109 volumes is useless • Data base with such a straightforward description will slow down simulations • Coding of geometry in C++ program using profits from geometry optimization tools provided by G3 and TGeo • All 109 volumes are described in a very optimal geometry hierarchy of 5 volumes embedded one into another • Performance test: 109 volumes vs 106 slow down simulation of 1 electron just in 4 times

  7. Parameterized geometry vs current det.geo files Any technical drawing contains just basic sizes which allows to calculae the size and position of any element (e.g. array of elements can be described by one element size, step size and the number of elements) The same numbers should be stored in DCDB or ADB for further geometry versioning User code should read the basic geometry parameters and let a user to code the geometry based on these parameters

  8. CbmEcalv2 CbmModule class CbmEcalv2 : public CbmEcal public: virtual void ConstructGeometry(); virtual Bool_t ProcessHits(CbmVolume* vol = 0); CbmDetector CbmEcal CbmEcalv2

  9. CbmEcalv2::ConstructGeometry() void CbmEcalv2::ConstructGeometry() { ... gGeoManager->Mixture("Polystyrene",aP,zP,dP,nP,wP,kMatPoly); gGeoManager->Medium("Scintillator", kMedScin, kMatPoly, 1, 0, 0., 10.0, 0.1, 0.1, 0.1, 0.1); gGeoManager->Volume("ECAL1", "BOX", 1, par, 3); gGeoManager->Division("ECAL1column","ECAL1",1,-1,-par[0],cellSize,0,"S"); gGeoManager->Node("ECAL1", 1, "ECAL", 0.,+yPos,0.,0,kTRUE,buf,0); ... }

  10. CbmEcalv2::ProcessHits() ECAL MC point is stored each time when some energy is deposited: (gMC->Edep() != 0) MC point: trackId cellId eLoss where cellId = iEcalBlock*1000000 + iColumn*1000 + iRow;

  11. CbmEcalv2 volume hierarchy

  12. Volumes One cell 11 cm2, 300 sampling layers

  13. Proposals for the framework • Detectors should be able to create simple geometry as well as detailed one • Simple geometry can be used just a material budget for simulations of other detectors, or for fast MC • Detailed geometry is needed for optimizing the internal detector structure to provide a realistic response function • Coding the geometry in C++ classes is inevitable for detailed geometry to profit from geometry optimization tools, and the framework should provide tools for it • Coding the geometry should be virtualized from a particular geometry description (G3, G4, TGeo, Fluka) to be independent on it • Geometry parameters stored in ascii files should be simular to the dimensions of the technical drawings

  14. Possible solution AliModule may have methods • Mixture • Material • Medium • Volume • Position • Divide with similar signatures as those in G3 or TGeo

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