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

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 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, , and HCal (h +, h 0 ). –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

Fermilab, 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)

Fermilab, 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)

Fermilab, 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, …)

Fermilab, 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).

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 7 LCDG4: some nice features Correct MC particle hierarchy, even when V 0 s 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 and

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 8 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. projective non-projective

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 9 LCDG4: XML geometry description Example: barrel HCal (dimensions in cm) Most of detector changes are very easy to make! Proj or NP selection either one, not both.

Fermilab, 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.

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 11 LCDG4: zoom in on primary interaction 00  0 →  0  0 00 ΛKs0ΛKs0 Decays forced in generator correctly processed

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

Fermilab, 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: Many interesting single particle and full event samples available, new ones can be requested: For detailed access instructions, see nicadd.niu.edu/~jeremy/admin/scp/index.html

Fermilab, 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.

Fermilab, 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.

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 16 DigiSim event loop Modifiers TempCalHits SimCalo.Hits TempCalHitsRawCalo.Hits DigiSimProcessor SimHitsLCCollection RawHitsLCCollection ● 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.

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

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 18 DigiSim example: Scint. HCal 1. Photodetection

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 19 DigiSim example: Scint. HCal 2. Noise and discrimination Mip peak ~ 15 PEs

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 20 Scintillation: 10 4  / MeV Photodetector QE: 15% Eff coll = / Expo + gauss noise ¼ MIP threshold HCal scintillator digitization (prelim.) simulated digitized

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 21 Scintillation: 10 4  / MeV Photodetector QE: 15% Eff coll = / Expo + gauss noise ¼ MIP threshold HCal scintillator digitization (prelim.)

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 22 DigiSim: Usage Instructions Download/install/build java 1.5, Maven, org.lcsim (see 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 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

Fermilab, 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.

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

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

Fermilab, 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

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

Fermilab, 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.

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 29  ± angular width: density weighted

Fermilab, 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.

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

Fermilab, 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{S w -1 S m }

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 33 Separability of clusters (contd.) where S w = S i W i S i S i = covariance matrix for cluster c i (in x, y, z) W i = weight of c i (choose your scheme) S m = covariance matrix w.r.t. global mean

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

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

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 36 DHCal: Particle-flow algorithm (NIU) Nominal SD geometry. Density-weighted clustering. Track momentum for charged, Calorimeter E for neutral particles.  +  p  0 Cal only PFA

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

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 38 PFA Jet Reconstruction summary 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.

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

Fermilab, 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 (D j – D i )/d ij where j is in the neighborhood find max []

Fermilab, 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

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

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

Fermilab, 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.

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 45 Z  qq Events Calculate E rec /E gen for each generated cluster Enter into histogram with weight E gen /E total.

Fermilab, 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,...

Fermilab, 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: E rec /E gen after stage 1 (matching) –eratio2: E rec /E gen after stage 2 (merge satellites)

Fermilab, 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: E rec /E gen after stage 1 (matching) –eratio2: E rec /E gen after stage 2 (merge satellites)

Fermilab, 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...

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 50 Current Status (contd.) With Perfect PFA (no confusion term), σ(E) = 3.1 GeV 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.

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 51 Plans No plans to get involved in any new simulation development work... Will continue to support LCDG4, but no further development Will further develop DigiSim – a long-term product.... until, perhaps, we are ready to parametrize some of the simulation (see below) Will concentrate more on algorithm development (with DigiSim). New ideas on reducing the confusion term and accounting for differences in low-E neutral hadron energy sampling need to be coded, polished. Enormous volumes of MC will have to be generated and analyzed to get a handle on these.

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 52 Plans (contd.) Major re-writing of code underway to make it more object-oriented. Aim to be ready to analyze the TB data as soon as they become available. A great deal of work to do. Coordinated, efficient use of scarce resources is key to success.

Fermilab, Simulation & PFA at NIU Dhiman Chakraborty 53 Backup slides