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Individual Particle Reconstruction

CHEP07, Victoria, September 6, 2007

Norman Graf

(SLAC)

Steve Magill

(ANL)

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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

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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.

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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

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fluctuations

Jet Energy Resolution – “Perfect” PFA

A = 15% ; Ah0 = 35%

=> (Ejet)/Ejet = 15%/Ejet

A = 15% ; Ah0 = 60%

=> (Ejet)/Ejet = 23%/Ejet

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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.

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Occupancy Event Display

tt six jets All Calorimeter Hits

Hits with more than one Monte Carlo particle

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

Readout

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Digital HCAL?

Analog linearity Digital 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

Analog Digital

Landau Tails+ path length

Gaussian

/mean ~22% /mean ~19%E (GeV) Number of Hits

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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.

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SiDLDC

GLD

5 Tesla B-fieldSi Strip/Disks TrackingW/Si ECAL, IR = 125 cmSS/RPC Digital HCAL

PFA-Motivated Detector Models

3 Tesla B-fieldTPC TrackingW/Scin ECAL, IR = 210 cmPb/Scin Analog HCAL

4 Tesla B-fieldTPC TrackingW/Si ECAL, IR = 160 cmSS/Scin Analog HCAL

Radius

B-f

ield

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) ECALHighly granular and segmented HCAL (SS, Pb, W) digital (gas) and analog (scintillator) readoutsMuon Systems outside of coil

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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, K0

L candidates)

• Jet algorithm– Reconstructed Particles used as input to jet algorithm, further analysis

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4.2 GeV K+4.9 GeV p6.9 GeV -3.2 GeV -

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

8.3 GeV n2.5 GeV KL

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 -2.4 GeV -4.0 GeV -5.9 GeV +

1.5 GeV n2.8 GeV n

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Mip trace/IL Photon Finding

Track-mip-shower Assoc. Neutral Hadrons

Overall Performance : ~33%/E central fit @ Z

Reconstruction Demonstration

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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.

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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.

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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.

cm

"combined" neutral hadron energy (GeV)

10 20 30 40 5014

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WRPC: 0.074 lambda/layer

WScint: 0.074 lambda/layer

SSRPC: 0.12 lambda/layer

SSScint: 0.12 lambda/layer

Transverse distance containing 90% of hits

10 20 30 40 506

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Transverse distance containing 75% of hits

Energy (GeV)

Transverse distance containing 90% ofhits

Scintillator

RPC

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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.

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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.

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From Hits to Reality

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What’s the reality here?

Additional Information

• 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