Simulation and PFA Development Efforts at NIU: Status & Plans

1. Simulation and PFA Development Efforts at NIU: Status & Plans D. Chakraborty, for the NIU ILC detector group Main contributors: V. Zutshi, G. Lima, J. McCormick, R. McIntosh Fermilab, 22 Sep 2005

2. Outline • Simulation • GEANT4-based packages: LCDG4, TBMokka, (SLIC) • Parametric simulation of GeV-to-ADC: DigiSim • Particle-Flow Algorithms • Density-weighted Clustering in Calorimeter • Calorimeter-only (no track-seeding) • Same for ECal (e, g), and HCal (h+, h0 ). • Replace cal clusters with matching (MC) tracks, if any. • The Directed Tree Algorithm • Association of isolated “fragment”s or “satellite”s. • Work in Progress and Future Plans Simulation & PFA at NIU Dhiman Chakraborty

3. Simulation: Summary • Primarily interested in exploring the (semi-)digital hadron calorimeter option in general, with scintillator as the active material in particular. • When we started, there was no GEANT4-based simulation package to produce output compatible with the Java-based reconstruction framework. • LCDG4 was written at NIU starting from a LCDROOT developed at U. Colorado. LCDG4 fully conformed with all standard I/O requirements (geometry input & event output formats), offered new features like non-projective geometry & better links to MC truth, fixed many bugs. (GL,RM) Simulation & PFA at NIU Dhiman Chakraborty

4. Simulation: Summary (contd.) • NIU collaborated with DESY,LLR to produce TBMokka, derived from Mokka (the G4-based TESLA simulator) for simulation of the CALICE TB module. NIU also produced a stand-alone G4-based application for cross-check. (JM) • Thoughts on what is now SLIC started while JM was at NIU. JM was on SLAC-NIU joint appointment when SLIC was written. • DigiSim allows easy parametric simulation of the many effects between the G4 energy deposits and recorded data. Available in Java & C++. (GL) Simulation & PFA at NIU Dhiman Chakraborty

5. Tasks • Full-detector simulation: LCDG4 (for ALCPG) • Test-beam module simulation: TBMokka (with DESY/LLR, for CALICE) • Simulation of detector imperfections: DigiSim (for ALCPG, CALICE, …) Simulation & PFA at NIU Dhiman Chakraborty

6. Full detector simulation:LCDG4 • GEANT4 v7.0. • Most hadronic physics lists available, LCPhysics not tested yet. • Input format: binary STDHEP • Output format: .lcio (standard) or .sio (Gismo compatible) • Nominal American detector geometries are implemented via XML geometry files (continuous tubes+disks only). Simulation & PFA at NIU Dhiman Chakraborty

7. LCDG4: some nice features • Correct MC particle hierarchy, even when V0s and hyperon decays are forced at the event generation stage. • Energies deposited in absorbers are available for cross-checks. • Non-projective geometries available. • Simple analysis code and documentation available from CVS andhttp://nicadd.niu.edu/~lima/lcdg4/. Simulation & PFA at NIU Dhiman Chakraborty

8. non-projective projective LCDG4: Projective vs. non-projective • Barrel only. • Rectangular virtual cells with linear dimensions, controlled at run time (XML). • Ecal and HCal can be made projective or non-projective independently. Simulation & PFA at NIU Dhiman Chakraborty

9. LCDG4: XML geometry description Example: barrel HCal (dimensions in cm). ... <volume id="HAD_BARREL" rad_len_cm="1.133" inter_len_cm="0.1193"> <tube> <barrel_dimensions inner_r="144.0" outer_z="286.0" /> <layering n="34"> <slice material="Stainless_steel" width="2.0" /> <slice material="Polystyrene" width="1.0" sensitive="yes“ /> </layering> <!--segmentation cos_theta = "600" phi = "1200" /--> <cell_info length="1.0" height="1.0" offset="0." outer_r="246.5"/> </tube> <calorimeter type="had" /> </volume> ... Proj or NP selection either one, not both. Most of detector changes are very easy to make! Simulation & PFA at NIU Dhiman Chakraborty

10. LCDG4 output: general features • One particle collection and several hit collections (one hit collection per subdetector) • Each hit points to the contributing particles (except tracker hits from calorimeter back-scatters). • LCIO only: (x,y,z) coordinates stored for every hit • All secondaries above an energy threshold (now set at 1 MeV), except for shower secondaries, are saved in output with: • Particle id and generation/simulation status codes. • Production momentum, production* and ending position (*LCIO only). • Calorimeter entrance: position and momentum (SIO only). • Pointers to parent particles. Simulation & PFA at NIU Dhiman Chakraborty

11. X0 →L0 0 L0 L0 ΛKs0 LCDG4: zoom in on primary interaction Decays forced in generator correctly processed Simulation & PFA at NIU Dhiman Chakraborty

12. LCDG4: example: e+e- tt Simulation & PFA at NIU Dhiman Chakraborty

13. LCDG4: wrap-up • Being replaced by SLIC as the the ALCPG standard, but many analysis packages have yet to adapt to the new standards. • Code with doc + instructions available: http://nicadd.niu.edu/~lima/lcdg4 • Many interesting single particle and full event samples available, new ones can be requested: For detailed access instructions, seehttp://nicadd.niu.edu/~jeremy/admin/scp/index.html Simulation & PFA at NIU Dhiman Chakraborty

14. DigiSim • Goal: a program to simulate the signal collection and digitization process in the ILC detector(s). • Converts the GEANT4 output (energy depositions and space-time coordinates) into the same format AND as close as possible to real data from readout channels. • Same code can then be used to process MC & real data. • Serves a convenient and standardized hook for the user to model such detector effects as inefficiencies, non-uniformities, non-linearities, attenuation, noise, cross-talk, hot and dead channels, cell ganging, etc. involved in signal collection, propagation, and conversion/recording. Simulation & PFA at NIU Dhiman Chakraborty

15. Requirements and choices • Basic requirements: • Object-oriented design to simplify maintenance and implementation of new functionality • Should be usable for both ALCPG’s Java-based reco framework and CALICE’s MARLIN (C++) • To be tested for TB simulation • Usable in stand-alone and preprocessor modes • In stand-alone mode, it produces LCIO events in the sae format as real data. All input information is preserved. • In preprocessor mode, doesn’t produce any persistent output, event in memory passed on to reconstruction. • Very easy to configure and enhance through text-based driver files. Simulation & PFA at NIU Dhiman Chakraborty

16. DigiSim event loop DigiSimProcessor SimHitsLCCollection RawHitsLCCollection Modifiers TempCalHits RawCalo.Hits SimCalo.Hits TempCalHits • Calorimeter hits shown here, but applies just as well to other subdetectors. • TempCalHits are both input and output to each modifier • All processing is controlled by a DigiSimProcessor (one per subdetector) • Modifiers are configured at run time, via the Marlin steering file • New modifiers can be easily created for new functionality. Simulation & PFA at NIU Dhiman Chakraborty

17. threshold Smeared linear transformation Example modifiers Eoutput GainDiscrimination is a smeared lin.transf. + threshold on energy SmearedLinear is a func-based smeared linear transformation on energy and/or timing Einput Simulation & PFA at NIU Dhiman Chakraborty

18. DigiSim example: Scint. HCal1. Photodetection Simulation & PFA at NIU Dhiman Chakraborty

19. DigiSim example: Scint. HCal2. Noise and discrimination Mip peak ~ 15 PEs Simulation & PFA at NIU Dhiman Chakraborty

20. HCal scintillator digitization (prelim.) Scintillation: 104 / MeV Photodetector QE: 15% Effcoll = 0.0111 +/- 0.0020 Expo + gauss noise ¼ MIP threshold simulated digitized Simulation & PFA at NIU Dhiman Chakraborty

21. HCal scintillator digitization (prelim.) Scintillation: 104 / MeV Photodetector QE: 15% Effcoll = 0.0111 +/- 0.0020 Expo + gauss noise ¼ MIP threshold Simulation & PFA at NIU Dhiman Chakraborty

22. DigiSim: Usage Instructions • Download/install/build java 1.5, Maven, org.lcsim(see http://www.lcsim.org for details) • Drivers needed: (all available from org.lcsim.digisim)CalHitMapDriver, DigiSimDriver and CalorimeterHitsDriver, plus LCIODriver (for standalone run, saving output file) or YourAnalysisDrivers (as an on-the-fly preprocessor) • DigiSim configuration file stored on LCDetectors: digisim/digisim.steer • Run it: • From command line: after setting the CLASSPATH (see docs for details)java org.lcsim.digisim.DigiSimMain <inputfile>an output file ./digisim.slcio will be produced, to be used for analysis or reconstruction • From inside JAS: DigiSimExample is available from Examples -> org.lcsim examples Simulation & PFA at NIU Dhiman Chakraborty

23. Particle Flow Algorithm Development • ECal: • 30 layers, silicon-tungsten. • 5mm x 5mm transverse segmentation. • HCal: • 34 layers, scintillator-steel • 1 cm x 1 cm transverse segmentation. • Magnetic field: 5T • Support structures, cracks, noise, x-talk, attenuation, inefficiencies,… not modelled. Simulation & PFA at NIU Dhiman Chakraborty

24. + energy resolution as function of energy for different (linear) cell sizes Simulation & PFA at NIU Dhiman Chakraborty

25. + energy resolution vs. energy Two-bit (“semi-digital”) rendition offers better resolution than analog Simulation & PFA at NIU Dhiman Chakraborty

26. Nhit vs. fraction of + E in cells with E>10 MIP:Gas vs. scintillator 2-bit readout affords significant resolution improvement over 1-bit for scintillator, but not for gas Simulation & PFA at NIU Dhiman Chakraborty

27. + energy resolution vs. energy Multiple thresholds not used Simulation & PFA at NIU Dhiman Chakraborty

28. Non-linearity • In scint, Nhit/GeV varies with energy. • This will introduce additional pressure on the “constant” term. • For scintillator, the non-linearity can be effectively removed by “semi-digital” treatment. Simulation & PFA at NIU Dhiman Chakraborty

29. p±angular width: density weighted Simulation & PFA at NIU Dhiman Chakraborty

30. Simulation Summary • Large parameter space in the nbit-segmentation-medium plane for hadron calorimetry. Optimization through cost-benefit analysis? • Scintillator and Gas-based ‘digital’ HCals behave differently. • Need to simulate detector effects (noise, x-talk, non-linearities, etc.) • Need verification in test-beam data. • More studies underway. Simulation & PFA at NIU Dhiman Chakraborty

31. Clustering (~2 yrs now) • Seeds: maxima in local density: di = S (1/Rij) • Membership of each cell in the seed clusters decided with a distance function. • Only unique membership considered. • Calculate centroids. • Iterate till stable within tolerance. Simulation & PFA at NIU Dhiman Chakraborty

32. Separability of clusters Best separability is achieved when width of a cluster is small compared to distances between clusters. J = Tr{Sw-1Sm} Simulation & PFA at NIU Dhiman Chakraborty

33. Separability of clusters (contd.) where Sw= Si WiSi Si = covariance matrix for cluster ci(in x, y, z) Wi = weight of ci(choose your scheme) Sm = covariance matrix w.r.t. global mean Simulation & PFA at NIU Dhiman Chakraborty

34. GeV cm cm cm Two (parallel) p+’s in TB sim: Simulation & PFA at NIU Dhiman Chakraborty

35. Two (parallel) p+’s in TB prototype sim:separability (J) vs. track distancefor different cell sizes Simulation & PFA at NIU Dhiman Chakraborty

36. S+ pp0 PFA Cal only DHCal: Particle-flow algorithm (NIU) • Nominal SD geometry. • Density-weighted clustering. • Track momentum for charged, • Calorimeter E for neutral particles. Simulation & PFA at NIU Dhiman Chakraborty

37. DHCal: Particle-flow algorithm (NIU) Photon Reconstruction inside jets Excellent agreement with Monte Carlo truth: Simulation & PFA at NIU Dhiman Chakraborty

38. PFA Jet Reconstruction summary (old) • Cone clustering in the calorimeters, • Flexible definition of weight (energy- or density-based), • Generalizable to form “proto-cluster” inputs for higher-level algorithms. • Replace cal clusters with matching MC track, if any. • Based on projective geometry. • New clustering algorithms available. Simulation & PFA at NIU Dhiman Chakraborty

39. ZZ  4j events Cal only Digital PFA DHCal: Particle-flow algorithm (NIU) Reconstructed jet resolution Simulation & PFA at NIU Dhiman Chakraborty

40. The Directed Tree Algorithm • Define neighborhood for a cell • Discard cells below threshold (0.25 MIP) • Calculate density for each cell i • If(density==0) ? else calculate (Dj – Di )/dij where j is in the neighborhood find max [] Simulation & PFA at NIU Dhiman Chakraborty

41. The Directed Tree Algorithm (contd.) • If max[] is –ve i starts a new cluster if max[] is +ve j is the parent of I if max[] == 0 avoid circular loop attach to nearest Simulation & PFA at NIU Dhiman Chakraborty

42. Single hadrons in the ECal Generated clusters Reconstructed clusters Clusters from single hadrons are reconstructed well. Some “fragment”s or “satellite”s remain unassociated. Simulation & PFA at NIU Dhiman Chakraborty

43. EM clusters in Zqq Events Generated clusters Reconstructed clusters Only a few highest-E clusters shown. Simulation & PFA at NIU Dhiman Chakraborty

44. The confusion term • Internal to calorimeter. • Reconstruct “gen” and “rec” clusters, • A “gen” cluster is a collection of cells which are attached to a particular MCparticle. All detector effects are included in this cluster. • Find centroids and match to nearest “rec” cluster, making sure that no cluster gets associated twice. • Somewhat conservative. Simulation & PFA at NIU Dhiman Chakraborty

45. Calculate Erec/Egen for each generated cluster Enter into histogram with weight Egen/Etotal. Zqq Events Simulation & PFA at NIU Dhiman Chakraborty

46. Cluster Matching and Merging • Stage 1: one-to-one gen-reco matching based on distances (3D or angular)→ unassociated clusters (“satellites”) • Stage 2: attach satellites to reco clusters based on angular distances: possible cuts on angular separation, satellite energies, number of hits,... Simulation & PFA at NIU Dhiman Chakraborty

47. Preliminary ECal Analysis • 500 events, with 2-pions 10 cm apart at ECal face, using SDNPHOct04 detector • neighborhood definition: (dφ=5, dz=5, dlayer=9) • discard events with decays or interactions before Ecal • Look at: • eratio1: Erec/Egen after stage 1 (matching) • eratio2: Erec/Egen after stage 2 (merge satellites) Simulation & PFA at NIU Dhiman Chakraborty

48. Preliminary HCal Analysis • 500 events, with 2-pions 10cm apart at Ecal face, using SDNPHOct04 detector • neighborhood definition: (dφ=2, dz=2, dlayer=2) • discard events with decays or interactions before Ecal • Look at: • eratio1: Erec/Egen after stage 1 (matching) • eratio2: Erec/Egen after stage 2 (merge satellites) Simulation & PFA at NIU Dhiman Chakraborty

49. Current Status • Analysis of complex events shows some problems with too many satellites – how to associate them with the right parent shower? • Clustering algorithm ported to org.lcsim, to be certified. Committed to LCSim CVS repository. • More manpower for the PFA development effort. • Work in progress, a lot to do ... Simulation & PFA at NIU Dhiman Chakraborty

50. Current Status (contd.) Zqq σ(E) = 4.3 GeV • Scint, 1 cm x 1cm. • Density-weighted clustering, but analog E calculation in both ECal & HCal. • Min 5 cells for cluster. • Cell threshold = 0.5 MIP. • Satellite attachment not used. • No cut on theta. With Perfect PFA (no confusion term), σ(E) = 3.1 GeV Simulation & PFA at NIU Dhiman Chakraborty