1. org.lcsim: Event Reconstruction in Java Norman Graf (SLAC) For the ALCPG Physics & Detector Simulation & Reconstruction Group CHEP 2010 Taipei, Oct. 21, 2010

2. Lepton Collider Detector Environment Detectors designed to exploit the physics discovery potential of lepton collisions at the Terascale. Perform precision measurements of complex final states with well-defined initial state:  Tunable energy  Known quantum numbers (plus e ─ e + polarization)  Very small interaction region  Momentum constraints (modulo beam & bremsstrahlung) 2

3. Detector Requirements Desire to fully reconstruct hadronic final states Need ability to tag heavy (light) quarks Excellent missing energy/mass sensitivity Require:  Exceptional momentum resolution Large volume gaseous (TPC) or low-mass Si (  -strip) tracker  Excellent vertexing capabilities Multi-layer pixel Vertex Detector ( ~GigaPixel )  Capable calorimetry Highly segmented (longitudinal & transverse) imaging calorimeter Dual readout total absorption crystals  Hermeticity 3

4. 4 LCD Simulation Mission Statement Provide full simulation capabilities for Linear Collider physics program:  Physics simulations  Detector designs  Reconstruction and analysis Need flexibility for:  New detector geometries/technologies  Different reconstruction algorithms Limited resources demand efficient solutions, focused effort.  Strong connections between university groups, national labs, international colleagues.

5. 5 Goals Facilitate contribution from physicists in different locations with various amounts of available time. Use standard data formats, collaborate & cooperate. Provide a general-purpose framework for physics software development. Develop a suite of reconstruction and analysis algorithms and sample codes. Simulate benchmark physics processes on different full detector designs. Deliver software which is easy to install, learn, use.  “0 to analysis in 15 min.” Support via web based forums, tutorials, meetings.

6. org.lcsim Components Geometry  Provide access to the subdetector geometry, types, readouts  64bit System & Channel ID  Detector & position  Readout cell sizes, strip pitch, pixel size, etc. Conditions Handling  compact.xml geometry file, sampling fractions, etc.  Constants stored in.zip file, web accessible by detector name  Arbitrary files (xml, ascii, binary) for reco algorithms Event Access Fast Detector Response Simulation Full Event Reconstruction Analysis Tools JAS3 plugin 6

7. 7 LC Detector Full Simulation MC Event (stdhep) Geometry (lcdd) Raw Event (lcio) GEANT4 slic Compact Geometry Description (compact.xml) Reconstruction, Visualization, … Items highlighted in yellow represent common standards Use runtime xml file to completely define detector geometry

8. 8 Single Source of Geometry Compact Description GeomConverter LCDD GODL org.lcsim Analysis & Reconstruction HEPREP Small Java program for converting from compact description to a variety of other formats slic lelaps wired lcio

9. 9 Detector Variants Runtime XML format allows variations in detector geometries to be easily set up and studied:  Stainless Steel vs. Tungsten HCal sampling material  Total absorption crystal calorimeters  RPC vs. GEM vs. Scintillator vs. dual readout  Layering (radii, number, composition)  Readout segmentation (size, projective vs. nonprojective)  Tracking detector technologies & topologies TPC, Pixels, Silicon microstrip “Wedding Cake” Nested Tracker vs. Barrel + Cap  Field strength and maps  Far forward Machine Detector Interface variants

10. 10 Example Geometries (same exe) MDI-BDS Cal Barrel SiD01 TPC Tracker Test Beam Silicon Trackers

11. 11 Silicon Detector Inner Tracker

12. Event Data Model and I/O MC event input via stdhep, output in LCIO Poster PO-MON-097 LCIO - Status and Recent Updates, Tony Johnson et al. 12

13. Fast Detector Response Simulation Covariantly smear tracks with matrices derived from geometry, materials and point resolution using Billoir’s formulation.  Analytically from geometry description. Smear neutrals according to expected calorimeter resolution (EM for , HAD for neutral hadrons)  Derived from full Geant4 simulations Create reconstructed particles from tracks and clusters ( , e,  from MC,  +/-, K 0 L for others) Can also dial in arbitrary effective jet energy resolution.  Derived from full simulation, reconstruction & analysis. 13

14. 14 org.lcsim Event Reconstruction Java based reconstruction and analysis package  Runs standalone or inside Java Analysis Studio (JAS)  Full Event Reconstruction Beam background overlays at detector hit level, including time offsets. Detector readout digitization (CCD pixels, Si  -strips, TPC pad hits) ab initio track finding and fitting for ~arbitrary geometries Multiple calorimeter clustering algorithms Individual Particle reconstruction (cluster-track association)  Analysis Tools (including WIRED event display)  Physics Tools (Jet Finding, Vertex Finding, Flavor Tagging) Write once, run anywhere  Exact same libraries run on all platforms (Windows, Mac, Linux(es), Grid) using the Java Virtual Machine.

15. Drivers and Event Access Event reconstruction is performed using a series of Drivers.  startOfData()  getConditionsManager()  process(EventHeader event) *  detectorChanged(org.lcsim.geometry.Detector detector)  endOfData() Interact with Event by fetching objects or collections from the event & processing them, optionally adding results back to the Event. 15

16. 16 Tracking Detector Readout Hits in Trackers record full MC information. Module tiling and signal digitization is deferred to analysis stage.  Used to rapidly study many possible solutions. Fully-featured package to convert MC hits in silicon to pixel hits. Fully configurable at runtime. MC Hits  Pixel ID & ADC  Clusters  Hits (x ±  x) Can correctly study occupancies, overlaps, ghost hits, etc.

17. PixSim (N. Sinev) 17 Allows very detailed descriptions of charge carrier movement, e.g. list of collecting, absorbing and reflecting regions, properties of silicon (doping, mobility, diffusion length and so on), electric and magnetic fields (including TCAD maps). Use of lookup tables leads to faster simulation. Example energy loss distribution for 1  m Si May create delta ray(s) Drift and Diffusion Strips/Pixels and Pulse Heights On the left, only diffusion is simulated, in the middle charge is moving only by electric forces, The right picture shows how it moved in our simulations

18. Silicon Tracker Tracker Simulation Electronics Simulation  Addition of electronics noise to collected charge  Propagation of the signal to readout – e.g. simulation of CCD clocking, and combining of signals from different charge trains  Determination if signal exceeds threshold  Assigning signals to particular bunch crossing  Digitization of the signal  Simulation of fake hits due to electronics noise Clustering  1D (for microstrips) and 2D (for pixels) clustering code is available.  Provides centroid and cluster-size dependent uncertainties  Serves as input to track finding and fitting 18

19. Track Finding and Fitting Standalone pattern recognition code for 1D (Si  strip) and 2D (Si pixel) hits. – High efficiency, even in presence of backgrounds. – Efficient at low momenta. Conformal-mapping pattern recognition also available, applicable to TPC. MIP stubs in highly segmented calorimeters also provide track candidates, propagate inwards to pick up tracker hits. Fitting code incorporating multiple scattering and energy loss via weight matrix or Kalman Filter available. 19

20. Calorimeter Reconstruction Number of clustering algorithms implemented Using Particle Flow Algorithms (PFA) to identify clusters in the calorimeters.  Associate showers to tracks for charged particles Then use track momentum for particle  Clusters unassociated with tracks are either photons or neutral hadrons Resulting jet energy (and dijet invariant mass) resolutions sufficient to resolve W/Z  hadrons 20

21. Reconstruction: Clustering Several Clustering Algorithms included  Cluster Cheater uses MC information  Nearest Neighbor Clusterer  Cone Clusterer  Minimal Spanning Tree  Directed Tree  … Algorithms can be run in parallel for comparison

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24. Running jobs Run control is via xml steering file Define input/output files, # of events to process, etc. List Drivers to run, along with all set methods java -server -jar lcsim-[VERSION]-bin.jar myjob.xml 24

25. 25 org.lcsim in use: the ILC LOI Exercise The Letter of Intent (LOI) process required a number of physics analyses to be conducted with full-detector simulation, ab initio event reconstruction, and analysis. The benchmark processes were deliberately chosen to highlight the intrinsic detector performance, to facilitate comparisons between the concept designs. Although still far from “real”, the physics benchmarking requirements presented us with a large-scale, end-to-end exercise which stressed most aspects of the software systems.  Event Generation  Detector Simulation  Event Reconstruction  Physics Analysis

26. 26 The Grid Made extensive use of both the LCG and OSG grids.  LCG: DESY, RAL Tier 1, in2p3, …  OSG running opportunistically on the CMS grid In general, no problems with the concept software  Shipped JVM if needed.  Bundled all classes into single jar file.  SiD software (slic & org.lcsim) just worked. Number of issues with Grid job submission, monitoring and file transfers. Grid is still high-maintenance & very LHC-centric.

27. 27 Event totals 17.8 million events at 500GeV cms. ~ 1 minute per event simulation ~ 1 minute per event reconstruction =======================================> 2 136 000 000 seconds 42.4 million events at 250GeV cms. ~.5 minute per event simulation ~.2 minute per event reconstruction =======================================> 1 780 800 000 seconds

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29. Interoperability via LCIO LCIO provides interoperability with tools developed in other regions (e.g. jet flavor tagging (LCFI), particle flow (Pandora)), other languages (FORTRAN, java, C++, python) and other analysis frameworks (e.g. Marlin). Can analyze output LCIO files using root  Using root LCIO Dictionary  Using rlcio files, LCIO event data model written as root files Can analyze output AIDA files using RAIDA.  non-official root binding to AIDA 29

30. Current User Base ILC physics and detector community  primarily US and UK members of Silicon Detector Concept CLIC physics and detector community  CERN-based SiD’ studies Muon Collider physics and detector studies  FNAL-based JLAB heavy-photon search proposals  HPS: fixed target, forward detector  DarkLight: gas-jet, asymmetric detector. FNAL dual-readout crystal calorimetry group Used at SLAC for ATLAS pixel upgrade simulations. 30

31. 31 Resources for getting started http://lcsim.org/ Web Portal http://lcsim.org/  Tutorials Software installation Using tools Simple Analysis Examples Developers Guide  Datasets  Documentation Confluence Wiki  More tutorials  More documentation  Frequently asked Questions  Users are encouraged to comment on, add to, or correct existing documentation https://jira.slac.stanford.edu/signup

32. 32 Resources for getting started Discussion Forums  http://forum.linearcollider.org/ http://forum.linearcollider.org/ SLIC, org.lcsim LCIO Tools from other regions.

33. 33 Summary ALCPG Sim/Reco team supports an ambitious physics and detector simulation, reconstruction & analysis effort. Goal is flexibility and interoperability, not technology or concept limited. Provides a complete and flexible detector simulation package capable of simulating arbitrarily complex detectors with runtime detector description. Reconstruction & analysis framework exists, core functionality available, individual particle reconstruction template developed, various analysis algorithms implemented. Successfully used in large-scale data challenge exercise (LOI) Being used by wider community:  CLIC@CERN  MuC@FNAL  Heavy photon proposals @JLab  …

34. 34 Additional Information lcsim.org - http://www.lcsim.orghttp://www.lcsim.org ILC Forum - http://forum.linearcollider.orghttp://forum.linearcollider.org Wiki - http://confluence.slac.stanford.edu/display/ilc/Homehttp://confluence.slac.stanford.edu/display/ilc/Home org.lcsim - http://www.lcsim.org/software/lcsimhttp://www.lcsim.org/software/lcsim Software Index - http://www.lcsim.org/softwarehttp://www.lcsim.org/software Detectors - http://www.lcsim.org/detectorshttp://www.lcsim.org/detectors LCIO - http://lcio.desy.dehttp://lcio.desy.de SLIC - http://www.lcsim.org/software/slichttp://www.lcsim.org/software/slic LCDD - http://www.lcsim.org/software/lcddhttp://www.lcsim.org/software/lcdd JAS3 - http://jas.freehep.org/jas3http://jas.freehep.org/jas3 AIDA - http://aida.freehep.orghttp://aida.freehep.org WIRED - http://wired.freehep.orghttp://wired.freehep.org