Full Event Reconstruction in Java CHEP '04 Interlaken, Switzerland Sep. 30, 2004 Norman Graf (SLAC) for the LCD development team (“From 0 to analysis in 15 minutes”)

International Linear Collider (ILC) Detectors designed to exploit the physics discovery potential of e + e - collisions at  s~1TeV. Detectors designed to exploit the physics discovery potential of e + e - collisions at  s~1TeV. Will perform precision measurements of complex final states. Will perform precision measurements of complex final states. Require: Require: Exceptional momentum resolution. Exceptional momentum resolution. Excellent vertexing capabilities. Excellent vertexing capabilities. “Particle Flow” calorimetry. “Particle Flow” calorimetry. Hermeticity. Hermeticity.

Mission Statement Provide full simulation capabilities for ILC physics program: Provide full simulation capabilities for ILC physics program: Physics simulations. Physics simulations. Detector designs. Detector designs. Machine-Detector interface & backgrounds. Machine-Detector interface & backgrounds. Need flexibility for: Need flexibility for: New detector geometries/technologies. New detector geometries/technologies. Innovative reconstruction algorithms. Innovative reconstruction algorithms. Limited resources demand efficient solutions, focused effort. Limited resources demand efficient solutions, focused effort. Desire a common simulation environment used in ILC studies which allows sharing of detectors, algorithms, and code. The system should be flexible, powerful, yet simple to install, maintain and use. Metric of performance: Ease with which physicist having an idea can implement and test its effect!

Most important tool of all!

Why Java? Java is a pure Object Oriented Language. Java is a pure Object Oriented Language. Simpler to learn and use than C++ : Simpler to learn and use than C++ : Language design emphasizes ease-of-use over performance. Language design emphasizes ease-of-use over performance. Garbage collector takes care of freeing unused objects. Garbage collector takes care of freeing unused objects. New language, no historical baggage. New language, no historical baggage. Very powerful standard library. Very powerful standard library. –Large number of open-source libraries including libraries for scientific computing. Platform independent, compile once just runs everywhere (Linux, Windows, Mac OSX, Solaris, …). Platform independent, compile once just runs everywhere (Linux, Windows, Mac OSX, Solaris, …). Physicist gets to concentrate on writing clean OO code to perform analysis tasks. Physicist gets to concentrate on writing clean OO code to perform analysis tasks. Not understanding core dumps and learning difference between a pointer and a reference. Not understanding core dumps and learning difference between a pointer and a reference. Performance of Java code is close to that of C++. Performance of Java code is close to that of C++. In real life maybe 20-30% overhead typical. In real life maybe 20-30% overhead typical.

Why Java? Several very powerful, free, IDE’s now available: Several very powerful, free, IDE’s now available: e.g. Netbeans, Eclipse. e.g. Netbeans, Eclipse. Support editing, code completion, GUI building, debugging, performance profiling, refactoring, CVS, etc… Support editing, code completion, GUI building, debugging, performance profiling, refactoring, CVS, etc… Multiplatform. Multiplatform. Mantra “I prefer EMACS” is becoming hard to justify! Mantra “I prefer EMACS” is becoming hard to justify!

Why a full reconstruction now?  ILC  LEP ILC  LEP Much higher luminosity, energy, precision. Much higher luminosity, energy, precision. More complex events (not on a resonance). More complex events (not on a resonance). ILC  LHC ILC  LHC Detector R&D focused on orthogonal issues. Detector R&D focused on orthogonal issues. e.g. rad hardness not an issue, tracker material is! e.g. rad hardness not an issue, tracker material is! Desired precision and resolutions require a new detector paradigm, intimately connected with the reconstruction: “Particle Flow”. Desired precision and resolutions require a new detector paradigm, intimately connected with the reconstruction: “Particle Flow”. Aim for very tight loop Aim for very tight loop  design  simulate  reconstruct  analyze 

Particle Flow Motivation Measure momenta of charged tracks in the tracker with superb resolution. Measure photons in highly segmented EM calorimeter with reasonable resolution. Remaining neutral hadrons measured in hadron calorimeter.  2 confusion is the hardest term, cannot be correctly simulated with fast 4-vector smearing. Needs detailed calorimeter shower simulation and reconstruction to understand.  2 confusion is the hardest term, cannot be correctly simulated with fast 4-vector smearing. Needs detailed calorimeter shower simulation and reconstruction to understand.

Imaging Calorimeter EMCal: dense, small Moliere radius highly segmented: laterally (~30-40 layers) longitudinally (~5x5 mm 2 ) HadCal: emphasize segmentation & granularity over intrinsic resolution. Digital readout?

ILC Detector Simulations 1. Use simplified volumes to investigate larger number of designs, allowing dynamic topologies using single executable. 2. Describe “optimal” detectors in great detail. TPC Tracker, Si Disks, CCD VTX All Si Tracker, CCD VTX

Java Reconstruction/Analysis Overview Runs standalone or inside Java Analysis Studio (JAS3). Runs standalone or inside Java Analysis Studio (JAS3). Fast MC provides target (“best you can do”). Fast MC provides target (“best you can do”). Smeared tracks and calorimeter clusters, fiducial acceptance. Smeared tracks and calorimeter clusters, fiducial acceptance. Beam & Physics background overlays at hit level. Beam & Physics background overlays at hit level. Hit digitization allows detector readout schemes to be varied, provides more realistic hits (merged, ghost,  pos ). Hit digitization allows detector readout schemes to be varied, provides more realistic hits (merged, ghost,  pos ). Full Reconstruction: Full Reconstruction: ab initio track finding and fitting. ab initio track finding and fitting. calorimeter clustering. calorimeter clustering. Individual Particle reconstruction (cluster-track association). Individual Particle reconstruction (cluster-track association). Java Neural Net available for training & use. Java Neural Net available for training & use. Physics Tools (Vertex Finding, Jet Finding, Flavor Tagging). Physics Tools (Vertex Finding, Jet Finding, Flavor Tagging). Analysis Tools (including LCD-specific WIRED event display). Analysis Tools (including LCD-specific WIRED event display).

Event Reconstruction Flow Sequential Record Loop LCEvent G4 Detector Simulation Input SimTrackerHits, SimCalorimeterHits, MCParticles Detector Digitization/ Hit Finding CCD Pixels, Si  strips*, TPC waveform* Tracker hit clustering, uncertainty assignment Track Finding/Fitting Standalone 3D trackfinding (e.g. TPC, CCD) Track merging, fitting Calorimeter Clustering “Nearest” Neighbor, MST*, Fuzzy* Cluster Particle ID (e.g. , , h ±, h 0 ) Reconstructed Particle Track-Cluster association, Jet-Finding, Flavor Tagging ZVTop vertexing, NN Flavor Tagging (b, c, uds)* Reconstruction Output LCIO (Tracks, Clusters, ReconstructedParticles…) AIDA (tuples, histograms) Reconstructors implemented as Drivers with hooks, e.g.: add(Processor p) processEvent(EventData evt) beforeFirstEvent() afterLastEvent() setDetector(Detector det) … Drivers implement Processor interface, so can be nested. Event contains some predefined collection hooks, evt.getCalorimeterHits() Arbitrary objects can be added to/retrieved from Event evt.put(“myStuff”, myStuff) Bckgnd Events +

Detector Hit Digitization SimCalorimeter quantized into cells in Geant4. SimCalorimeter quantized into cells in Geant4. Study readout segmentation by ganging cells. Study readout segmentation by ganging cells. SimTrackerHit has full MC info: x, t, dE/dx… SimTrackerHit has full MC info: x, t, dE/dx… Digitized at reco level: e.g. strip pitch, pixel size, charge sharing, electronic noise, … Digitized at reco level: e.g. strip pitch, pixel size, charge sharing, electronic noise, … Provides more realistic simulation of effects of backgrounds, efficiencies and noise. Provides more realistic simulation of effects of backgrounds, efficiencies and noise. Nearby hits merged, ghosts simulated. Nearby hits merged, ghosts simulated. Hit-specific measurement uncertainties. Hit-specific measurement uncertainties. Can add Random Hits (salt & pepper backgrounds). Can add Random Hits (salt & pepper backgrounds). Efficiencies simulated by dropping hits, channels. Efficiencies simulated by dropping hits, channels.

CCD Digitization VXD hits from simulated events, finds charge deposited in each pixel, adds electronics noise and digitizes signal. VXD hits from simulated events, finds charge deposited in each pixel, adds electronics noise and digitizes signal. Finds CCD clusters, splitting if necessary. Finds CCD clusters, splitting if necessary. Coordinates of found centers are used to replace TrackerHits in the events. Further event processing (track finding, fitting, and so on) proceeds the same way as it was before. Coordinates of found centers are used to replace TrackerHits in the events. Further event processing (track finding, fitting, and so on) proceeds the same way as it was before. Can set CCD parameters (like thickness, depleted layer depth, epitaxial layer thickness and so on), electronics parameters (noise, ADC conversion scale, pixel and cluster thresholds), processing parameters (like cluster center calculation method). Can set CCD parameters (like thickness, depleted layer depth, epitaxial layer thickness and so on), electronics parameters (noise, ADC conversion scale, pixel and cluster thresholds), processing parameters (like cluster center calculation method).

Stereo Angle Wedges Hexagons Tiling Forward Disks

Track Finding/Fitting Full pattern recognition in 3D detectors. Full pattern recognition in 3D detectors. Extrapolations of found tracks to 2D det’s. Extrapolations of found tracks to 2D det’s. Tuned for Large and Silicon Detectors. Tuned for Large and Silicon Detectors. Working on generalizing this.* Working on generalizing this.* Track Fitters: Track Fitters: SLD Weight Matrix Fitter. SLD Weight Matrix Fitter. Single Detector or Combined fit (e.g. VTX+TPC). Single Detector or Combined fit (e.g. VTX+TPC). Kalman Fitter* Kalman Fitter* MIP Tracking in Cal (  ’s, track starting). MIP Tracking in Cal (  ’s, track starting).

Calorimeter Clustering Cluster Interface defined, several clustering algorithms currently implemented. Cluster Interface defined, several clustering algorithms currently implemented. MC “Cheater”, “Nearest” Neighbor, Fixed-cone, … MC “Cheater”, “Nearest” Neighbor, Fixed-cone, … Cluster QA package developed. Cluster QA package developed. Cluster Refinements: Cluster Refinements: Combining HAD + EM clusters. Combining HAD + EM clusters. Endcap + Barrel overlap region. Endcap + Barrel overlap region. Identify cluster as originating from charged or neutral hadron, EM. Identify cluster as originating from charged or neutral hadron, EM.

Reconstructed Particle/ Jet Finding Particle Flow algorithms being developed with minimal coupling to specific detector designs. Particle Flow algorithms being developed with minimal coupling to specific detector designs. Photon and muon reconstruction fairly mature. Photon and muon reconstruction fairly mature. Emphasis on track-following for charged hadrons. Emphasis on track-following for charged hadrons. MIP reconstruction quite promising. MIP reconstruction quite promising. Canonical data samples identified and will be used to characterize detector response. Canonical data samples identified and will be used to characterize detector response. Systematic investigation of Systematic investigation of  jet-jet mass as a function of B n R m a p l q (B-field, Cal radius, Cal cell area, Cal longitudinal segmentation), material and readout technology employing a Particle Flow paradigm being undertaken.

Java, Python, Pnuts editor Dataset, Program, AIDA Explorer Scripting Console JAS3 IDE Tutorial: Tutorial: Event Browser Histogram & ntuple analysis, fitting

WIRED Event Display Tree controls visibility. (HepRep2 support in WIRED3 is preliminary) Picking supports viewing physics attributes

Next Generation (org.lcsim) Goals Retain core functionality from hep.lcd package. Retain core functionality from hep.lcd package. Full suite of reconstruction and analysis tools available to all LCIO users. Full suite of reconstruction and analysis tools available to all LCIO users. Update to use LCIO for I/O and as basis for simulation, raw data and reconstruction event formats. Update to use LCIO for I/O and as basis for simulation, raw data and reconstruction event formats. Update/simplify framework using experience from hep.lcd. Update/simplify framework using experience from hep.lcd. Internationalization: Internationalization: Try to make package independent of detector & geometry assumptions so can work with any detector. Try to make package independent of detector & geometry assumptions so can work with any detector. Read properties of detectors at runtime. Read properties of detectors at runtime. Update to Java 1.5. Update to Java 1.5. Many improvements since hep.lcd framework was created. Many improvements since hep.lcd framework was created. Revitalize work on reconstruction algorithms. Revitalize work on reconstruction algorithms.

Conclusions ILC Detector design is an area of active development with many design choices, requiring close coupling of design, simulation, and reconstruction. ILC Detector design is an area of active development with many design choices, requiring close coupling of design, simulation, and reconstruction. Ease of use and speed of development are essential for physicists conducting ILC studies part-time & in a heterogeneous environment. Ease of use and speed of development are essential for physicists conducting ILC studies part-time & in a heterogeneous environment. A fairly complete suite of simulation tools written in Java exists and is being successfully used for these studies. A fairly complete suite of simulation tools written in Java exists and is being successfully used for these studies.

Links linearcollider.org linearcollider.org linearcollider.org ILC Simulations: ILC Simulations: hep.lcd: hep.lcd: Tutorials: Tutorials: JAS3: JAS3: WIRED: WIRED: HepRep: HepRep: LCIO: LCIO: Discussion Forums: Discussion Forums: (linear collider specific) (linear collider specific) (general JAS, Wired etc) (general JAS, Wired etc)

Related Talks LCIO - A persistency framework and data model for the linear collider, Frank Gaede WIRED 4 - A generic Event Display plugin for JAS 3, Mark Dönszelmann AIDA, JAIDA and AIDAJNI: Data Analysis using interfaces, Victor Serbo Interactive Data Analysis on the Grid using Globus 3 and JAS3, Tony Johnson Writing Extension Modules (Plug-ins) for JAS3, Mark Dönszelmann