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

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Presentation on theme: "1 Individual Particle Reconstruction CHEP07, Victoria, September 6, 2007 Norman Graf (SLAC) Steve Magill (ANL)"— Presentation transcript:

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

2 2 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 ZHH

3 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 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. M Z – M W – still largest contribution to jet E resolution

5 5 fluctuations Jet Energy Resolution – “Perfect” PFA A  = 15% ; A h0 = 35% =>  (E jet )/E jet = 15%/  E jet A  = 15% ; A h0 = 60% =>  (E jet )/E jet = 23%/  E jet

6 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 cm 2 transverse cells Steel, Tungsten, Lead for HCal –30-50 layers, 1 – 9 cm 2 transverse cells Need high segmentation to minimize the number of cells receiving energy deposits from more than one initial particle.

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

8 8 Scintillator No timing or threshold cutRPC Not sensitive to neutrons! Readout

9 9 Digital HCAL? Analog linearityDigital linearity 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 AnalogDigital Landau Tails + path length Gaussian  /mean ~22%  /mean ~19% E (GeV)Number of Hits

10 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 11 SiD LDC GLD 5 Tesla B-field Si Strip/Disks Tracking W/Si ECAL, IR = 125 cm SS/RPC Digital HCAL PFA-Motivated Detector Models 3 Tesla B-field TPC Tracking W/Scin ECAL, IR = 210 cm Pb/Scin Analog HCAL 4 Tesla B-field TPC Tracking W/Si ECAL, IR = 160 cm SS/Scin Analog HCAL Radius B-field a la T. Yoshioka 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

12 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, K 0 L candidates) Jet algorithm –Reconstructed Particles used as input to jet algorithm, further analysis

13 GeV K+ 4.9 GeV p 6.9 GeV  GeV  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  8.3 GeV n 2.5 GeV K L 0 _ 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  1.9 GeV  GeV  GeV  GeV  GeV n 2.8 GeV n _ Mip trace/IL Photon Finding Track-mip-shower Assoc.Neutral Hadrons Overall Performance : ~33%/  E central Z Reconstruction Demonstration

14 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 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 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. Energy (GeV) Transverse distance containing 90% of hits Scintillator RPC

17 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 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 19 From Hits to Reality

20 20 What’s the reality here?

21 Additional Information ILC Detector Simulation ILC Forum Wiki JAS3 WIRED4 AIDA

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