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E.-C. Aschenauer, T. Barton, R. Darienzo, A. Kiselev BNL, 06/05/2013

Update on EIC detector Performance Simulations. E.-C. Aschenauer, T. Barton, R. Darienzo, A. Kiselev BNL, 06/05/2013. Contents. EicRoot framework development EIC detector solenoid modeling EIC smearing generator update TODO lists. EicRoot development. EIC in FairRoot framework.

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E.-C. Aschenauer, T. Barton, R. Darienzo, A. Kiselev BNL, 06/05/2013

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  1. Update on EIC detector Performance Simulations E.-C. Aschenauer, T. Barton, R. Darienzo, A. Kiselev BNL, 06/05/2013

  2. Contents • EicRoot framework development • EIC detector solenoid modeling • EIC smearing generator update • TODO lists A.Kiselev

  3. EicRoot development A.Kiselev

  4. EIC in FairRoot framework • FairRoot is officially maintained by GSI; dedicated developers • O(10) active experiments; O(100) users CbmRoot • ROOT • VMC (GEANT3, GEANT4) • VGM (ROOT, GEANT) • … R3BRoot FairRoot external package bundle FairBase C++ classes … PandaRoot EicRoot eic-smear -> Make best use of FairRoot development -> Utilize efficiently existing codes developed by EIC taskforce A.Kiselev

  5. End user view • No executable (steering through ROOT macro scripts) -> MC points simulation digitization reconstruction “PID” Pass -> Hits -> “Short” tracks -> Clusters -> “Combined” tracks -> Vertices @ IP • ROOT files for analysis available after each step • C++ class structure is well defined at each I/O stage A.Kiselev

  6. EIC detector layout (phase 2) A.Kiselev

  7. EIC detector layout (phase 1) A.Kiselev

  8. Detector view in EicRoot FEMC CEMC SOLENOID BEMC • EMC and tracking detectors implemented so far A.Kiselev

  9. Tracking in EicRoot A.Kiselev

  10. General • Magnetic field interface exists • Detector geometry is described in 0-th approximation: • Digitization exists (simple yet useable) • “Ideal” track reconstruction inherited from PandaRoot codes • Silicon vertex tracker • Silicon forward/backward tracker • TPC • GEM forward tracker A.Kiselev

  11. Vertex silicon tracker • MAPS technology; ~20x20mm2 chips, ~20mm 2D pixels • STAR upgrade “building blocks” (cable assemblies) MAPS R&D for EIC within BNL LDRD A.Kiselev

  12. Vertex silicon tracker • 6 layers at [30..160] mm radius • 0.37% X0 in acceptance per layer simulated precisely; • digitization: single discrete pixels, one-to-one from MC points A.Kiselev

  13. Other tracking elements forward/backward silicon trackers: • 2x7 disks with up to 280 mm radius • N sectors per disk; 200mm silicon-equivalent thickness • digitization: discrete ~20x20mm2 pixels TPC: • ~2m long; gas volume radius [300..800] mm • 1.2% X0 IFC, 4.0% X0 OFC; 15.0% X0 aluminum endcaps • digitization: idealized, assume 1x5 mm GEM pads GEM trackers: • 3 disks behind the TPC endcap • STAR FGT design • digitization: 100mm resolution in X&Y; gaussian smearing A.Kiselev

  14. Tracker zoomed view FGT FST VST BST TPC BGT A.Kiselev

  15. Tracking scheme • So-called ideal PandaRoot track “finding”: • PandaRoot track fitting code: • Monte-Carlo hits are digitized on a per-track basis • Effectively NO track finder MRS-B1 solenoid design used • Kalman filter • Steering in magnetic field • Precise on-the-fly accounting of material effects -> pretty much useable for acceptance and single-track resolution studies; -> less suitable for radiation length scans; -> hardly useful for efficiency and occupancy estimates; A.Kiselev

  16. Example plots from tracking code 1 GeV/c p+ tracks at h=0.5: <ndf> = 206 32 GeV/c p+ tracks at h=3.0: <ndf> = 9 -> look very reasonable from statistical point of view A.Kiselev

  17. Momentum resolution study (1) p+ track momentum resolution vs. pseudo-rapidity -> expect 2% or better momentum resolution in the whole kinematic range A.Kiselev

  18. Momentum resolution study (2) p+ track momentum resolution at h = 3.0 vs. Silicon thickness -> ~flat over inspected momentum range because of very small Si pixel size A.Kiselev

  19. Momentum resolution study (3) p+ track momentum resolution at h = 3.0 vs. Silicon pixel size -> 20 micron pixel size is essential to maintain good momentum resolution A.Kiselev

  20. Tracking TODO list • Perform geometry optimization • Implement more realistic digitization schemes • Think about track finder algorithms • Implement vertex builder • Account for beam particle parameter “smearing” A.Kiselev

  21. Calorimeters in EicRoot A.Kiselev

  22. General • Written from scratch • Unified interface (geometry definition, digitization, clustering) for all EIC calorimeter types • Rather detailed digitization implemented A.Kiselev

  23. Backward EM Calorimeter (BEMC) • PWO-II, layout a la CMS & PANDA • -2500mm from the IP • both projective and non-projective geometry implemented • digitization based on PANDA R&D 10 GeV/c electron hitting one of the four BEMC quadrants Same event (details of shower development) A.Kiselev

  24. Forward EM Calorimeter (FEMC) tower (and fiber) geometry described precisely • tungsten powder scintillating fiber sampling calorimeter technology • +2500mm from the IP; non-projective geometry • sampling fraction for e/m showers ~2.6% • “medium speed” simulation (up to energy deposit in fiber cores) • reasonably detailed digitization; “ideal” clustering code A.Kiselev

  25. FEMC energy resolution study 3 degree track-to-tower-axis incident angle • “Realistic” digitization: 40MHz SiPM noise in 50ns gate; 4m attenuation length; 5 pixel single tower threshold; 70% light reflection on upstream fiber end; -> good agreement with original MC studies and measured data A.Kiselev

  26. FEMC tower “optimization” original mesh -> optimized mesh design can probably decrease “constant term” in energy resolution optimized mesh A.Kiselev

  27. Barrel EM Calorimeter (CEMC) -> barrel calorimeter collects less light, but response (at a fixed 3o angle) is perfectly linear • same tungsten powder + fibers technology as FEMC, … • … but towers are tapered • non-projective; radial distance from beam line [815 .. 980]mm A.Kiselev

  28. CEMC energy resolution study 3 degree track-to-tower-axis incident angle -> simulation does not show any noticeable difference in energy resolution between straight and tapered tower calorimeters A.Kiselev

  29. Calorimeter TODO list • Tune geometry • Perform systematic resolution studies • Implement shower parameterization (fast MC) • Implement realistic cluster split algorithm • Add hadronic calorimeters A.Kiselev

  30. EicRoot overall TODO list • Prepare documentation • Take care about official release & installation • Perform geometry optimization • Implement IR (material and fields) • Implement PID algorithms (RICH, TPC dE/dx, …) • Start physics simulations A.Kiselev

  31. EIC solenoid modeling Richard E. Darienzo, SBU graduate student A.Kiselev

  32. EIC solenoid modeling main requirements: • Yield large enough bending for charged tracks at large h • Keep field inside TPC volume as homogeneous as possible • Keep magnetic field inside RICH volume(s) small -> use OPERA-3D/2D software Presently used design: MRS-B1 A.Kiselev

  33. EIC solenoid modeling Other options investigated, like 4-th concept solenoid design -> obviously helps to cancel “tails” A.Kiselev

  34. Solenoid modeling TODO list • Optimize coil geometry and currents • Check effects of adding iron shielding • Perform fine tuning of selected configuration • Come up with a consistent design matching all the experimental requirements A.Kiselev

  35. eic-smear package A.Kiselev

  36. MC generator output PYTHIA MC tree code: Builds ROOT tree containing events Smearer: Performs fast detector smearing Djangoh DPMjet PEPSI gmc_trans Milou Rapgap General architecture eic-smear • C++ code running in ROOT • Builds with configure/Make • Single libeicsmear.so to load in ROOT A.Kiselev

  37. Functionality built in • Easily configurable acceptance definitions • Kinematic variable smearing declarations (single) quantity, X, to smear: E, p, θ, φ Function defining σ(X) = f([E, p, θ, φ]) Acceptance for X in E, p, θ, φ, pT, pZ + + either a priori knowledge of detector resolutions is needed or parameterization based on a full GEANT simulation -> try out resolutions provided by EicRoot fits … A.Kiselev

  38. Lepton-hadron separation via E/p -> clearly separation becomes better in several kinematic regions all plots: 10GeV x 100GeV beams A.Kiselev

  39. Hadron identification with RICH consider hadrons in pseudo-rapidity range ~[1.0 .. 3.0] -> pion/kaon/proton identification should be possible up to momenta ~40 GeV/c A.Kiselev

  40. Migration in (x,Q2) bins 10GeV x 100GeV beams -> “survival probability” is above ~80% in the region where tracking has superior resolution compared to calorimetry A.Kiselev

  41. Smearing code TODO list • Implement vertex position smearing • Provide other (small) interface changes required for EicRoot integration if needed • Keep physics resolution studies up to date using input provided by EicRoot see https://wiki.bnl.gov/eic! A.Kiselev

  42. Smearing code TODO list • Implement vertex position smearing • Provide other (small) interface changes required for EicRoot integration if needed • Keep physics resolution studies up to date using input provided by EicRoot Details on detector performance requirements are summarized here: https://wiki.bnl.gov/eic/index.php/DIS:_What_is_important A.Kiselev

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