American LC calorimetry efforts: Simulation and Particle-flow algorithms

1. American LC calorimetry efforts: Simulation and Particle-flow algorithms Dhiman Chakraborty (dhiman@fnal.gov) Northern Illinois University / for the The 6th ACFA workshop 15-17 December, 2003 TIFR, Mumbai, India LC detector simulation and algorithm development efforts in the US

2. Outline • Overview • Particle-flow algorithm • Simulation, reconstruction, analysis tools • Simulation of technology & design options: • ECal: Scintillator, Si/Scint. hybrid, Si-W • HCal: Scintillator, RPC, GEM • Algorithm development • Analog vs. Digital HCal • Particle-flow algorithms • Critical issues, work in progress, … • Summary LC detector simulation and algorithm development efforts in the US

3. Overview Physics Requirements: • Need unprecedented energy and direction resolution for jets, photons, invisibles. • ΔE/E = ~30%/E (GeV) for jets to separate W & Z in their hadronic final states (w/o beam constraints). • Precise and accurate missing energy resolution for SM as well as new physics. • Must be able to find non-pointing photons – a tell-tale signature of GMSB. • New algorithms required to meet energy and angular resolution goals. • Hermeticity crucial to measure missing Energy. LC detector simulation and algorithm development efforts in the US

4. Particle-Flow Algorithm (PFA) • Charged particles in a jet are more precisely measured in the tracker • A typical jet consists of: • 64% charged particles • 21% photons • 11% neutral hadrons • Use tracker for charged, • Calorimeter for neutrals only • Must be able to separate charged particle energy from neutrals in the calorimeter=> fine 3d granularity LC detector simulation and algorithm development efforts in the US

5. Design considerations • Min inner radius of barrel limited by tracking resolution requirement, ~1.5 m. • Max outer radius limited by budget and desire for ~5T B field in entire cal, 2.5-3.0 m. • Similarly for length: ~6 m. • Fineness of transverse and radial segmentation limited by budget, technical challenges: ~0.25 cm2 (ECal), 1-15 (25?) cm2 (HCal). • The nominal SD design has >30M cal channels. • Accurate cell-by-cell energy measurement may be less important in the HCal: save cost by reducing dynamic range – “digital HCal” (1-4 bits instead of 12-15)? • dE/E<0.3E/sqrt(E) may be achievable. LC detector simulation and algorithm development efforts in the US

6. Simulation & Reconstruction tools STDHEP (pythia/pandora or particle gun) GISMO (SLAC) EGS, GEISHA xml geom. input Projective only Mokka (LLR) LCDMokka (DESY/ SLAC): G4, MySQL or xml geom. input LCDG4(NIU) G4, xml geom. input, Handles non-proj geometries Sio/lcio output for reco/analysis with JAS/Root/indep. AIDA-compliant code + several standalone simulation programs LC detector simulation and algorithm development efforts in the US

7. Technology/Design simulations: Si-W ECal (U. Oregon, SLAC) Total Energy deposited: EGS4 (MeV) G4: 4960 ± 40 MeV SD: 30 x 5/7 X0 SD vB: 20 x 5/7 X0 + 10 x 10/7 X0 Energy dep. in silicon: EGS4 (MeV) G4: 66 ± 5 MeV LC detector simulation and algorithm development efforts in the US

8. Technology/Design simulations: Scintillator-W ECal (U. Colorado) • 2mm thick, 5x5 cm2 tiles • Alternate layers offset by ½ len (for better position resolution) • 0.5 X0 thick tungsten • ~45 layers LC detector simulation and algorithm development efforts in the US

9. Position and Z mass resolution LC detector simulation and algorithm development efforts in the US

10. Technology/Design simulations: Scint-Si-W/Pb ECal (Kansas) • Silicon sensors to do fine pattern recognition and position resolution • Scintillator for fine sampling and timing • GEANT4 based box calorimeter • implemented • ‘pixelized’ ECAL • to do arbitrary segmentation Energy Depositions LC detector simulation and algorithm development efforts in the US

11. Smallest shower size in SD, but HY42 achieves almost the same E resolution with a slightly larger shower for 33% of the silicon cost Position resolution in Hybrid ECal LC detector simulation and algorithm development efforts in the US

12. Technology/Design simulations:Scintillator semi-Digital HCal (NIU) • Studies with GISMO and G4 based simulations • Detailed comparisons of GISMO-LCDG4-Mokka • Steel(2cm)-Scint(5mm) sandwich with varying transverse segmentation LC detector simulation and algorithm development efforts in the US

13. Scintillator (semi)Digital HCal: cell size and thresholds Non-projective geometry • Single charged pions • Plain cell-counting only • 12-16 cm2 acceptable • 3 thresholds optimal LC detector simulation and algorithm development efforts in the US

14. ArCO 2 0. 00 6.5mm 5 1 Cu . 0. 0 00 Kapton 5 G10 ArCO2 3.4 mm GEM 3.1 mm Technology/Design simulations:GEM Digital HCal (UTA) • Replaced scintillator with GEM’s – in Tesla TDR (Mokka) • Full & mixture approximation compared • Single pion studies to understand response and resolution • Analog vs Digital comparisons LC detector simulation and algorithm development efforts in the US

15. GEM DHCal: Energy measurement • ELive=SEEM+ W SGEHCAL • Obtained the relative weight W using two Gaussian fits to EM only vs HCAL only events • Perform linear fit to mean values as a function of incident pion energy • Extract ratio of the slopes  Weight factor W • E = C* ELive Analog GEM: LC detector simulation and algorithm development efforts in the US

16. GEM DHCal: digital response n_hit vs. E in the HCal ECal & HCal response • Single charged pions • 1 cm2 cells in HCal LC detector simulation and algorithm development efforts in the US

17. GEM DHCal: response & E resolution • Single charged pions • 1 cm2 cells in HCal LC detector simulation and algorithm development efforts in the US

18. Steel 20 mm 1.6 mm Steel 20 mm G10 3.0 mm 1.1 mm Glass 1.6 mm Gas 1.2 mm Glass 1.1 mm Scintillator 6.4 mm Technology/Design simulations:RPC DHCal (ANL, Chicago, BU, FNAL) • GEANT4 simulation of calorimeter module (1.5 m x 1.5 m x 3.0 m) • 1cm2 readout pad size • Studied gas and scintillator in the analog and digital paradigms LC detector simulation and algorithm development efforts in the US

19. HCal: gas vs. scintillator Needs revision with current algorithms RPC Scint. Analog: Digital: This is not a measure of ability to separate showers in a jet LC detector simulation and algorithm development efforts in the US

20. DHCal: Density-weighted Clustering(NIU) di = k S (1/Rij) • Density-based clustering in both ECal and HCal • Clusters matched to tracks replaced by their generated p • For ECal clusters, use energy of assoc. cells • For HCal clusters, use nHit based E estimate LC detector simulation and algorithm development efforts in the US

21. DHCal: Particle-flow algorithm (NIU) • Nominal SD geometry • Density-weighted clustering S+ pp0 PFA Cal only • Track momentum for charged, • Calorimeter E for neutrals LC detector simulation and algorithm development efforts in the US

22. DHCal: Particle-flow algorithm (NIU) Photon Reconstruction inside jets Excellent agreement with Monte Carlo truth: LC detector simulation and algorithm development efforts in the US

23. DHCal: Particle-flow algorithm (NIU)Reconstructed jet resolution ZZ  4j events Cal only Digital PFA LC detector simulation and algorithm development efforts in the US

24. Track-first Particle-flow algorithm (ANL, SLAC) Step 1: Track extrapolation through Cal – substitute for Cal cells (MIP + ECAL shower tube + HCAL tube; reconstruct linked MIP segments + density-weighted hit clusters) - analog or digital techniques in HCAL – Cal granularity/segmentation optimized for separation of charged & neutral clusters Step 2: Photon finder - use analytic long./trans. energy profiles, ECAL shower max, etc. Step 3: Jet Algorithm - tracks + photons + remaining Cal cells (neutral hadron contribution) - Cal clustering not needed -> Digital HCAL? LC detector simulation and algorithm development efforts in the US

25. Analog vs Digital Energy Resolution GEANT 4 Simulation of SD Detector (5 GeV +) -> sum of ECAL and HCAL analog signals - Analog -> number of hits with 7 MeV threshold in HCAL - Digital Analog Digital Landau tails + path length fluctuations Gaussian LC detector simulation and algorithm development efforts in the US

26. Single 10 GeVp+: event display comparison Blue: density = 1 Red: density = 2,3 Green: density > 3 Energy weighted Density weighted LC detector simulation and algorithm development efforts in the US

27. Neutral Hadrons in Z  jj decays • Simple Neutron/K0L Estimator: • Remove hits from Photon Clusters • Remove hits around tracks within Fixed Cone 0.1 (EM) and Cone 0.3 (HAD) • Assign remainder to Neutrons/K0L N/K0L Candidate Energy (GeV) MC Truth Neutron/K0L Energy (GeV) LC detector simulation and algorithm development efforts in the US

28. Overlapping tracks & n/K0L Two approaches being investigated: • Put calorimeter and track properties in a neural net • Remove track and gamma hits from the calorimeter (‘snark’ inspired) LC detector simulation and algorithm development efforts in the US

29. Photon Reconstruction • Simple cone algorithm to cluster cells in the ECal • Currently using fixed cone of 0.03 • Splitting based on distance of cell from cone axis • Plan to use seed- energy-dependent radius LC detector simulation and algorithm development efforts in the US

30. PFA: Cluster identification (SLAC, U. Pennsylvania) • Make contiguous hit clusters • Attempt to identify particle type that created cluster based on a set of discriminators • NN, trained on single particles, being used presently LC detector simulation and algorithm development efforts in the US

31. PFA: Z mass usingCluster identification LC detector simulation and algorithm development efforts in the US

32. Muon Reconstruction in ECal(U. Iowa) • GISMO-JAS framework • Muons generated in the 1-10 GeV range, q ~ 4-174o • Constructed MIPs using the Monte Carlo information as seeds • Plan to integrate this into a full Eflow framework LC detector simulation and algorithm development efforts in the US

33. Full detector simulation (NIU):energies per cell (LCDG4 vs. LCDMokka) ECal LC detector simulation and algorithm development efforts in the US

34. Full detector simulation (NIU):energies per cell (LCDG4 vs. LCDMokka) HCal LC detector simulation and algorithm development efforts in the US

35. CALICE test-beam calorimeter + tail-catcher simulation (NIU): LC detector simulation and algorithm development efforts in the US

36. CALICE test-beam calorimeter + tail-catcher simulation (NIU): LC detector simulation and algorithm development efforts in the US

37. Issues, plans, work in progress • Need worldwide concensus on software design philosophy & implementation, tools, sharing of information, ideas, and code … • Collaboration started between Europe & America • Flexible, robust, transparent geometry description common to simulation & reconstr. (run-time specification until designs converge) • Unique challenges posed by v. large # of channels • GDML (being revived by G4 team)? MySQL (Mokka)? • Event Data Model • Analysis platform • Should not be limited to ROOT/JAS/… • AIDA-compliant tuples and analysis code (standard interface)? LC detector simulation and algorithm development efforts in the US

38. Issues, plans, work in progress • More realistic simulation • Detectors with support structures, cable routing • Noise, inefficiencies, cross-talk, … • Large MC samples for benchmark processes, single particles • Standardized “class” of PFAs • Mutual optimization (w.r.t. detector design) • Evaluation in terms of Physics impact • Powerful (interactive) visualization tool • Parametrized MC (for v.v.large # of events) LC detector simulation and algorithm development efforts in the US

39. Summary • Extensive simulation efforts on specific hardware options, both standalone and world-wide compatible. • Digital calorimetry can be as good as analog, may be even better, especially for particle-flow algorithms. • Several independent approaches to PFAs expected to result in a large software library of algorithms and reconstruction techniques. • Much work ahead – international collaboration is crucial to our success. LC detector simulation and algorithm development efforts in the US

40. For more information on the American efforts on calorimetry, simulation, software etc, visit www.slac.stanford.edu/xorg/lcd/calorimeter/ orcontact the speaker at dhiman@fnal.gov THANK YOU! LC detector simulation and algorithm development efforts in the US