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Individual Particle Reconstruction

Individual Particle Reconstruction. Norman Graf (SLAC). Steve Magill (ANL). CHEP07, Victoria, September 6, 2007. ZHH. Precision Physics at the ILC. e + e - : ~background-free but can contain complex multi-jet events Includes final states with heavy bosons W, Z, H

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Individual Particle Reconstruction

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  1. Individual Particle Reconstruction Norman Graf (SLAC) Steve Magill (ANL) CHEP07, Victoria, September 6, 2007

  2. ZHH Precision Physics at the ILC • e+ e- : ~background-free but can contain complex multi-jet events • Includes final states with heavy bosons W, Z, H • Statistics limited so must include hadronic decay modes (~80% BR)  multi-jet events • In general no kinematic fits  full event reconstruction

  3. Individual Particle Reconstruction • The aim is to reconstruct individual particles in the detector with high efficiency and purity. • Recognizing individual showers in the calorimeter is the key to achieving high di-jet mass resolution. • High segmentation favored over compensation. • Loss of intrinsic calorimeter energy resolution is more than offset by the gain in measuring charged particle momenta. • Use this approach to design complete detector with best overall performance/price.

  4. Overview • 1 to 1 correspondence between measured detector objects and particle 4-vectors Detector Jet == Particle Jet • Combines tracking and 3-D imaging calorimetry • Excellent tracking efficiency & momentum resolution for charged particles (~60% of jet E) • p (tracking) <<< E for photons or hadrons in CAL • Good EM Calorimetry for photon measurement (~25% of jet E) • E for photons < E for neutral hadrons • Good separation of neutral and charged showers in E/HCAL • CAL objects == particles • 1 particle : 1 object  small CAL cells • Adequate E resolution for neutrals in HCAL (~13% of jet E) • E < minimum mass difference, e.g. MZ – MW • still largest contribution to jet E resolution

  5. Jet Energy Resolution – “Perfect” PFA A = 15% ; Ah0 = 35% => (Ejet)/Ejet = 15%/Ejet A = 15% ; Ah0 = 60% => (Ejet)/Ejet = 23%/Ejet fluctuations

  6. Calorimeter Segmentation • Highly segmented calorimeters constructed of materials which induce compact shower size are necessary. • Si-W default for electromagnetic calorimeter. • 20-40 layers, .01 – 1 cm2 transverse cells • Steel, Tungsten, Lead for HCal • 30-50 layers, 1 – 9 cm2 transverse cells • Need high segmentation to minimize the number of cells receiving energy deposits from more than one initial particle.

  7. Occupancy Event Display ttsix jets All Calorimeter Hits Hits with more than one Monte Carlo particle

  8. Readout Scintillator No timing or threshold cut RPC Not sensitive to neutrons!

  9. Digital HCAL? GEANT 4 Simulation of Si Detector (5 GeV +) -> sum of ECAL and HCAL analog signals - Analog -> number of hits with 1/3 mip threshold in HCAL - Digital Analog linearity Digital linearity Analog Digital Landau Tails + path length Gaussian /mean ~22% /mean ~19% E (GeV) Number of Hits

  10. Detector models • Calorimeters drive the whole detector design! • Using Si-W as default electromagnetic calorimeter. • Investigating several hadronic calorimeter designs Absorbers Readouts Steel RPC Tungsten Scintillator Lead GEM • Varying inner radius of barrel, aspect ratio to endcap, strength of B Field, readout segmentation.

  11. PFA-Motivated Detector Models a la T. Yoshioka SiD LDC GLD B-field 5 Tesla B-field Si Strip/Disks Tracking W/Si ECAL, IR = 125 cm SS/RPC Digital HCAL 4 Tesla B-field TPC Tracking W/Si ECAL, IR = 160 cm SS/Scin Analog HCAL • Common features : • Large B-field (3-5 Tesla) with calorimeter inside • -> Suppresses beam backgrounds in detector • -> Separates charged hadrons • State-of-the-Art tracking (TPC, Si-Strips) • Dense (W), fine-grained (Si pixels, Scin strips) ECAL • Highly granular and segmented HCAL (SS, Pb, W) • digital (gas) and analog (scintillator) readouts • Muon Systems outside of coil 3 Tesla B-field TPC Tracking W/Scin ECAL, IR = 210 cm Pb/Scin Analog HCAL Radius

  12. Reconstruction Strategy • Track-linked mip segments • Find mip hits on extrapolated tracks, determine layer of first interaction based solely on cell density (no clustering of hits) (  candidates) • Photon Finder • Nearest Neighbor clustering (with local equivalence relations) of EM Cal hits • Analytic longitudinal H-matrix fit to layer E profile with ECAL clusters as input, transverse moment analysis, track match, E/p ( , 0, e+/- candidates) • Track-linked EM and HAD clusters • substitute for Cal objects (mips + ECAL shower clusters + HCAL shower clusters), reconstruct linked mip segments + clusters iterated in E/p • Analog or digital techniques in HCAL ( +/- candidates) • Neutral Finder algorithm • cluster remaining CAL cells, merge, cut fragments ( n, K0L candidates) • Jet algorithm • Reconstructed Particles used as input to jet algorithm, further analysis

  13. _ 8.3 GeV n 2.5 GeV KL0 _ 1.5 GeV n 2.8 GeV n Reconstruction Demonstration Mip trace/IL 6.6 GeV  1.9 GeV  1.6 GeV  3.2 GeV  0.1 GeV  0.9 GeV  0.2 GeV  0.3 GeV  0.7 GeV  Photon Finding 1.9 GeV  3.7 GeV  3.0 GeV  5.5 GeV  1.0 GeV  2.4 GeV  1.3 GeV  0.8 GeV  3.3 GeV  1.5 GeV  4.2 GeV K+ 4.9 GeV p 6.9 GeV - 3.2 GeV - 1.9 GeV - 2.4 GeV - 4.0 GeV - 5.9 GeV + Track-mip-shower Assoc. Neutral Hadrons Overall Performance : ~33%/E central fit @ Z

  14. Reconstruction Framework • Analysis shown here done within the general ALCPG simulation & reconstruction environment. • Framework exists for the full reconstruction chain which allows modular implementation of most aspects of the analysis. • Interfaces allow different clustering algorithms to be swapped in and alternate strategies to be studied. • Goal is to facilitate cooperative development and reduce time & effort between having an idea and seeing the results.

  15. Simulations & Test Beams • Results are very sensitive not only to the reconstruction algorithms but to the shower simulations as well. Need data! • “thick” target data – Calorimeter prototypes • Enable shower model Comparisons • “thin” target data – e.g. MIPP • produce particle production differential cross sections vs energy, angle, etc. • Needed to tune the shower models themselves.

  16. Hadron Shower Transverse Size • Number of issues still unresolved with choice (or not) of showering models. • For many neutral hadrons LHEP (Gheisha-inspired) is only choice. • Transition regions still worrisome. Transverse distance containing 90% of hits Scintillator RPC Energy (GeV)

  17. Summary • Jet reconstruction will be crucial to our understanding of physics at the ILC. • To live up to its potential as a precision instrument for the physics of the future, an ILC detector must include hadronic decays of massive particles in physics analyses as well as leptonic modes • The PFA approach to jet reconstruction is seen as a way to use the components of an ILC detector in an optimal way, achieving unprecedented mass resolution from dijet reconstruction. • PFA development is an R&D project itself, and much progress has been made in efforts parallel to those in detector hardware. • PFAs are beginning to show that they do, indeed, result in much improved jet reconstruction for simulated events and jets that are expected at the ILC. • Dependencies on various detector parameters are now being studied, which will ultimately influence our choice of technologies for ILC detector component design – in particular the calorimeters.

  18. Conclusions • Unambiguous separation of charged and neutral hadron showers is the crux of this approach to detector design. • hadron showers NOT well described analytically, fluctuations dominate # of hits, shape • also investigating highly-segmented compensating calorimeter designs. • Calorimeters designed for optimal 3-D shower reconstruction : • granularity << shower transverse size • segmentation << shower longitudinal size • Critically dependent on correct simulation of hadronic showers • Investing a lot of time and effort understanding & debugging Geant4 models. • Timely test-beam results crucial to demonstration of feasibility. • Full Simulations + Reconstruction  ILC detector design • Unique approach to calorimeter design • Ambitious and aggressive approach, strong desire to do it right. • Flexible simulation & reconstruction package allows fast variation of parameters.

  19. to Reality From Hits

  20. What’s the reality here?

  21. ILC Detector Simulation http://www.lcsim.org • ILC Forum http://forum.linearcollider.org • Wiki http://confluence.slac.stanford.edu/display/ilc/ • JAS3 http://jas.freehep.org/jas3 • WIRED4 http://wired.freehep.org • AIDA http://aida.freehep.org Additional Information

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