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

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Full event reconstruction in java

e

e

Norman Graf (SLAC)

for the LCD development team

CHEP '04

Interlaken, Switzerland Sep. 30, 2004

Full Event Reconstruction in Java

(“From 0 to analysis in 15 minutes”)


International linear collider ilc

International Linear Collider (ILC)

  • Detectors designed to exploit the physics discovery potential of e+e- collisions at s~1TeV.

  • Will perform precision measurements of complex final states.

  • Require:

    • Exceptional momentum resolution.

    • Excellent vertexing capabilities.

    • “Particle Flow” calorimetry.

    • Hermeticity.


Mission statement

Mission Statement

  • Provide full simulation capabilities for ILC physics program:

    • Physics simulations.

    • Detector designs.

    • Machine-Detector interface & backgrounds.

  • Need flexibility for:

    • New detector geometries/technologies.

    • Innovative reconstruction algorithms.

  • 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

Most important tool of all!


Why java

Why Java?

  • Java is a pure Object Oriented Language.

    • Simpler to learn and use than C++ :

      • Language design emphasizes ease-of-use over performance.

      • Garbage collector takes care of freeing unused objects.

      • New language, no historical baggage.

      • 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, …).

    • 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.

    • Performance of Java code is close to that of C++.

      • In real life maybe 20-30% overhead typical.


Why java1

Why Java?

  • Several very powerful, free, IDE’s now available:

    • e.g. Netbeans, Eclipse.

    • Support editing, code completion, GUI building, debugging, performance profiling, refactoring, CVS, etc…

    • Multiplatform.

  • Mantra “I prefer EMACS” is becoming hard to justify!


Why a full reconstruction now

Why a full reconstruction now?

  • ILC  LEP

    • Much higher luminosity, energy, precision.

    • More complex events (not on a resonance).

  • ILC  LHC

    • Detector R&D focused on orthogonal issues.

      • 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”.

  • Aim for very tight loop

     design  simulate  reconstruct  analyze 


Particle flow motivation

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.

2confusion is the hardest term, cannot be correctly simulated with fast 4-vector smearing. Needs detailed calorimeter shower simulation and reconstruction to understand.


Imaging calorimeter

Imaging Calorimeter

EMCal:

dense, small Moliere radius

highly segmented:

laterally (~30-40 layers)

longitudinally (~5x5 mm2)

HadCal:

emphasize segmentation & granularity over intrinsic resolution.

Digital readout?


Ilc detector simulations

ILC Detector Simulations

  • Use simplified volumes to investigate larger number of designs, allowing dynamic topologies using single executable.

  • Describe “optimal” detectors in great detail.

TPC Tracker, Si Disks, CCD VTX

All Si Tracker, CCD VTX


Java reconstruction analysis overview

Java Reconstruction/Analysis Overview

  • Runs standalone or inside Java Analysis Studio (JAS3).

  • Fast MC provides target (“best you can do”).

    • Smeared tracks and calorimeter clusters, fiducial acceptance.

  • Beam & Physics background overlays at hit level.

  • Hit digitization allows detector readout schemes to be varied, provides more realistic hits (merged, ghost, pos ).

  • Full Reconstruction:

    • ab initio track finding and fitting.

    • calorimeter clustering.

    • Individual Particle reconstruction (cluster-track association).

  • Java Neural Net available for training & use.

  • Physics Tools (Vertex Finding, Jet Finding, Flavor Tagging).

  • Analysis Tools (including LCD-specific WIRED event display).


Event reconstruction flow

Event Reconstruction Flow

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.

G4 Detector Simulation Input

SimTrackerHits, SimCalorimeterHits, MCParticles

Bckgnd

Events

+

Sequential Record Loop

LCEvent

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±, h0)

Reconstructed Particle

Track-Cluster association,

Event contains some predefined collection hooks,

evt.getCalorimeterHits()

Arbitrary objects can be added to/retrieved from Event

evt.put(“myStuff”, myStuff)

Jet-Finding, Flavor Tagging

ZVTop vertexing, NN Flavor Tagging (b, c, uds)*

Reconstruction Output

LCIO (Tracks, Clusters, ReconstructedParticles…)

AIDA (tuples, histograms)


Detector hit digitization

Detector Hit Digitization

  • SimCalorimeter quantized into cells in Geant4.

    • Study readout segmentation by ganging cells.

  • SimTrackerHit has full MC info: x, t, dE/dx…

    • 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.

    • Nearby hits merged, ghosts simulated.

    • Hit-specific measurement uncertainties.

  • Can add Random Hits (salt & pepper backgrounds).

  • Efficiencies simulated by dropping hits, channels.


Ccd digitization

CCD Digitization

  • VXD hits from simulated events, finds charge deposited in each pixel, adds electronics noise and digitizes signal.

  • 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.

  • 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).


Tiling forward disks

Stereo Angle

Wedges

Hexagons

Tiling Forward Disks


Track finding fitting

Track Finding/Fitting

  • Full pattern recognition in 3D detectors.

  • Extrapolations of found tracks to 2D det’s.

  • Tuned for Large and Silicon Detectors.

    • Working on generalizing this.*

  • Track Fitters:

    • SLD Weight Matrix Fitter.

    • Single Detector or Combined fit (e.g. VTX+TPC).

    • Kalman Fitter*

  • MIP Tracking in Cal (’s, track starting).


Calorimeter clustering

Calorimeter Clustering

  • Cluster Interface defined, several clustering algorithms currently implemented.

    • MC “Cheater”, “Nearest” Neighbor, Fixed-cone, …

  • Cluster QA package developed.

  • Cluster Refinements:

    • Combining HAD + EM clusters.

    • Endcap + Barrel overlap region.

    • Identify cluster as originating from charged or neutral hadron, EM.


Reconstructed particle jet finding

Reconstructed Particle/ Jet Finding

  • Particle Flow algorithms being developed with minimal coupling to specific detector designs.

  • Photon and muon reconstruction fairly mature.

  • Emphasis on track-following for charged hadrons.

    • MIP reconstruction quite promising.

  • Canonical data samples identified and will be used to characterize detector response.

  • Systematic investigation of jet-jet mass as a function of BnRmaplq (B-field, Cal radius, Cal cell area, Cal longitudinal segmentation), material and readout technology employing a Particle Flow paradigm being undertaken.


Jas3 ide

Event Browser

Histogram & ntuple analysis, fitting

JAS3 IDE

  • Tutorial: http://jas.freehep.org/jas3/Tutorial/index.html

Java, Python, Pnuts editor

Dataset, Program, AIDA Explorer

Scripting Console


Wired event display

Tree controls visibility. (HepRep2 support in WIRED3 is preliminary)

Picking supports viewing physics attributes

WIRED Event Display


Next generation org lcsim goals

Next Generation (org.lcsim) Goals

  • Retain core functionality from hep.lcd package.

    • 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/simplify framework using experience from hep.lcd.

  • Internationalization:

    • Try to make package independent of detector & geometry assumptions so can work with any detector.

    • Read properties of detectors at runtime.

  • Update to Java 1.5.

    • Many improvements since hep.lcd framework was created.

  • Revitalize work on reconstruction algorithms.


Conclusions

Conclusions

  • 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.

  • A fairly complete suite of simulation tools written in Java exists and is being successfully used for these studies.


Links

Links

  • linearcollider.org

  • ILC Simulations: http://lcsim.org

  • hep.lcd: http://www-sldnt.slac.stanford.edu/jas/Documentation/lcd/

    • Tutorials:

      • http://jas.freehep.org/jas3/Tutorial/index.html

      • http://www-sldnt.slac.stanford.edu/snowmass/Welcome.html

  • JAS3: http://jas.freehep.org/jas3

  • WIRED: http://wired.freehep.org/

  • HepRep: http://heprep.freehep.org/

  • LCIO: http://lcio.desy.de

  • Discussion Forums:

    • http://forum.linearcollider.org (linear collider specific)

    • http://forum.freehep.org/ (general JAS, Wired etc)


Related talks

Related Talks

248 - LCIO - A persistency framework and data model for the linear collider, Frank Gaede

395 - WIRED 4 - A generic Event Display plugin for JAS 3, Mark Dönszelmann

397 - AIDA, JAIDA and AIDAJNI: Data Analysis using interfaces, Victor Serbo

401 - Interactive Data Analysis on the Grid using Globus 3 and JAS3, Tony Johnson

402 - Writing Extension Modules (Plug-ins) for JAS3, Mark Dönszelmann


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