John Marshall, 1 John Marshall, University of Cambridge IWLC2010, Geneva, October 19 2010.

Slides:



Advertisements
Similar presentations
John Marshall, 1 John Marshall, University of Cambridge LCWS11, Granada, September
Advertisements

Lauri A. Wendland: Hadronic tau jet reconstruction with particle flow algorithm at CMS, cHarged08, Hadronic tau jet reconstruction with particle.
John Marshall, 1 John Marshall, University of Cambridge ILD Workshop, LAL Orsay, May
John Marshall, 1 John Marshall, University of Cambridge ILD Workshop, DESY, July
Testbeam Requirements for LC Calorimetry S. R. Magill for the Calorimetry Working Group Physics/Detector Goals for LC Calorimetry E-flow implications for.
Ties Behnke, Vasiliy Morgunov 1SLAC simulation workshop, May 2003 Pflow in SNARK: the next steps Ties Behnke, SLAC and DESY; Vassilly Morgunov, DESY and.
Some early attempts at PFA Dhiman Chakraborty. LCWS05 Some early attempts at PFA Dhiman Chakraborty2 Introduction Primarily interested in exploring the.
 Track-First E-flow Algorithm  Analog vs. Digital Energy Resolution for Neutral Hadrons  Towards Track/Cal hit matching  Photon Finding  Plans E-flow.
Individual Particle Reconstruction Norman Graf SLAC April 28, 2005.
Cluster finding in CALICE calorimeters Chris Ainsley University of Cambridge, UK General CALICE meeting: simulation/reconstruction session 28  29 June.
Photon reconstruction and calorimeter software Mikhail Prokudin.
John Marshall, 1 John Marshall, University of Cambridge ILD Workshop, LAL Orsay, May
Progress with the Development of Energy Flow Algorithms at Argonne José Repond for Steve Kuhlmann and Steve Magill Argonne National Laboratory Linear Collider.
Energy Flow and Jet Calibration Mark Hodgkinson Artemis Meeting 27 September 2007 Contains work by R.Duxfield,P.Hodgson, M.Hodgkinson,D.Tovey.
Implementing a dual readout calorimeter in SLIC and testing Geant4 Physics Hans Wenzel Fermilab Friday, 2 nd October 2009 ALCPG 2009.
John Marshall, 1 John Marshall, University of Cambridge LCWS, Beijing, March 2010.
0 Status of Shower Parameterisation code in Athena Andrea Dell’Acqua CERN PH-SFT.
Non-prompt Track Reconstruction with Calorimeter Assisted Tracking Dmitry Onoprienko, Eckhard von Toerne Kansas State University, Bonn University Linear.
Summary of Simulation and Reconstruction Shaomin CHEN (Tsinghua University)  Framework and toolkit  Application in ILC detector design Jupiter/Satellites,
Event Reconstruction in SiD02 with a Dual Readout Calorimeter Detector Geometry EM Calibration Cerenkov/Scintillator Correction Jet Reconstruction Performance.
John Marshall, 1 John Marshall, University of Cambridge LCD-WG2, May
Event-Specific Hadronic Event Reconstruction 1 Graham W. Wilson, University of Kansas.
1 Using Jupiter and Satellites Akiya Miyamoto KEK Jan 2006.
PFA Template Concept Performance Mip Track and Interaction Point ID Cluster Pointing Algorithm Single Particle Tests of PFA Algorithms S. Magill ANL.
John Marshall, 1 John Marshall, University of Cambridge LCD WG6 Meeting, April
Development of a Particle Flow Algorithms (PFA) at Argonne Presented by Lei Xia ANL - HEP.
CaloTopoCluster Based Energy Flow and the Local Hadron Calibration Mark Hodgkinson June 2009 Hadronic Calibration Workshop.
Two Density-based Clustering Algorithms L. Xia (ANL) V. Zutshi (NIU)
1 Calorimetry Simulations Norman A. Graf for the SLAC Group January 10, 2003.
Cluster finding in CALICE calorimeters Chris Ainsley University of Cambridge, UK LCWS 04: Simulation (reconstruction) parallel session 20 April 2004, Paris,
Results from particle beam tests of the ATLAS liquid argon endcap calorimeters Beam test setup Signal reconstruction Response to electrons  Electromagnetic.
LCWS06 Bangalore 13/3/06Mark Thomson 1 A Topologic Approach to Particle Flow “PandoraPFA” Mark Thomson University of Cambridge This Talk:  Philosophy.
CBM ECAL simulation status Prokudin Mikhail ITEP.
05/04/06Predrag Krstonosic - Cambridge True particle flow and performance of recent particle flow algorithms.
PFAs – A Critical Look Where Does (my) SiD PFA go Wrong? S. R. Magill ANL ALCPG 10/04/07.
13 July 2005 ACFA8 Gamma Finding procedure for Realistic PFA T.Fujikawa(Tohoku Univ.), M-C. Chang(Tohoku Univ.), K.Fujii(KEK), A.Miyamoto(KEK), S.Yamashita(ICEPP),
Photon reconstruction and matching Prokudin Mikhail.
John Marshall, 1 John Marshall, University of Cambridge LCD-WG2, June
1 D.Chakraborty – VLCW'06 – 2006/07/21 PFA reconstruction with directed tree clustering Dhiman Chakraborty for the NICADD/NIU software group Vancouver.
Ties Behnke: Event Reconstruction 1Arlington LC workshop, Jan 9-11, 2003 Event Reconstruction Event Reconstruction in the BRAHMS simulation framework:
1 Software tools in Asia Akiya Miyamoto KEK 18-March-2005 Simulation and Reconstruction Session LCWS2005 Representing acfa-sim-j activity M.C.Chang 1,K.Fujii.
John Marshall, 1 John Marshall, University of Cambridge LCD Meeting, December
J. S. MarshallCost-effective ECAL1 ECAL Simulation Studies – Overview Wednesday 30 th January 2013 J. S. Marshall University of Cambridge.
ATLAS and the Trigger System The ATLAS (A Toroidal LHC ApparatuS) Experiment is one of the four major experiments operating at the Large Hadron Collider.
1 OO Muon Reconstruction in ATLAS Michela Biglietti Univ. of Naples INFN/Naples Atlas offline software MuonSpectrometer reconstruction (Moore) Atlas combined.
John MarshallPandora Development1 J.S. Marshall University of Cambridge.
Individual Particle Reconstruction The PFA Approach to Detector Development for the ILC Steve Magill (ANL) Norman Graf, Ron Cassell (SLAC)
Calice Meeting Argonne Muon identification with the hadron calorimeter Nicola D’Ascenzo.
John Marshall, 1 John Marshall, University of Cambridge LCD Software Meeting, September
7/13/2005The 8th ACFA Daegu, Korea 1 T.Yoshioka (ICEPP), M-C.Chang(Tohoku), K.Fujii (KEK), T.Fujikawa (Tohoku), A.Miyamoto (KEK), S.Yamashita.
Mark Thomson University of Cambridge High Granularity Particle Flow Calorimetry.
Status of SiD/Iowa PFA by Usha Mallik The University of Iowa for Ron Cassell, Mat Charles, TJ Kim, Christoph Pahl 1 Linear Collider Workshop of the Americas,
DESY1 Particle Flow Calorimetry At ILC Experiment DESY May 29 th -June 4 rd, 2007 Tamaki Yoshioka ICEPP, Univ. of Tokyo Contents.
J. S. MarshallPandora PFA1 Pandora Particle Flow Calorimetry Tuesday 29 th January 2013 J. S. Marshall University of Cambridge.
John Marshall, 1 John Marshall, University of Cambridge LCD-WG2, July
 reconstruction and identification in CMS A.Nikitenko, Imperial College. LHC Days in Split 1.
John Marshall, 1 John Marshall, University of Cambridge LCD WG6 Meeting, February
David Lange Lawrence Livermore National Laboratory
ECAL Interaction layer PFA Template Track/CalCluster Association Track extrapolation Mip finding Shower interaction point Shower cluster pointing Shower.
Particle detection and reconstruction at the LHC (IV)
slicPandora: slic + pandoraPFANew
Studies with PandoraPFA
OO Muon Reconstruction in ATLAS
Detector Configuration for Simulation (i)
ILD Optimisation: towards 2012 University of Cambridge
Individual Particle Reconstruction
Linear Collider Simulation Tools
Argonne National Laboratory
Detector Optimization using Particle Flow Algorithm
Linear Collider Simulation Tools
Presentation transcript:

John Marshall, 1 John Marshall, University of Cambridge IWLC2010, Geneva, October

John Marshall, 2 Overview In a typical jet: 60% of jet energy is in form of charged hadrons 30% is in photons (mainly from  0   ) 10% is in neutral hadrons (mainly n and K L ) Particle flow calorimetry aims to improve jet energy resolution by: Measuring charged particles in detector tracker Measuring photon energies in ECAL Measuring only neutral hadron energies in HCAL Pandora is the most mature Particle Flow Algorithm, offering the best performance and an approach that is understood and well-documented. However, by 2009 the code had become too difficult to modify or extend. During the past year, a new framework for running decoupled particle flow algorithms has been developed and the original Pandora code has been fully reimplemented within the framework.

John Marshall, 3 New Pandora Structure In the new framework, Pandora is divided into three sections. Communication between these sections is achieved via simple C++ APIs, Application Programming Interfaces, which each provide a “high-level” service: Isolates specific details of software framework and detector, allowing framework to be dependency-free. Specifies parameters for calo hits, tracks, etc. so that Pandora objects can be self-describing. Owns named collections of Pandora objects: calo hits, tracks, clusters and PFOs. Can perform memory management, as content can only be provided or accessed via APIs. Use APIs to access Pandora objects and carry out particle flow reconstruction tasks. Physics-driven code, with nested structure promoting re-use of code to perform specific tasks. Pandora Content API Pandora API Pandora FrameworkPandora Algorithms Client Application

John Marshall, 4 Client Application A Pandora client application, written in any software framework, uses the PandoraAPI to supply details of the detector geometry and the hits/tracks/MCParticles in each event. Pandora then builds its own self-describing objects to use in the reconstruction. Construction of Pandora objects is designed to be simple; client application makes a Parameters class, supplies all the required information and calls Create API. Required information is self-describing and physics-based, e.g. for a track: d0, z0, track state at start, track state at ECal, etc. After object creation is complete, client application calls ProcessEvent API. When the thread is returned, client application can call GetParticleFlowObjects API. Pandora API ILD/Marlin SiD/org.lcsim +LAr TPC reconstruction +LHC experiments Applications all use same Pandora framework & algorithms Pandora Algorithm Manager CaloHit Manager MC Manager PFO Manager Plugin Manager Track Manager Cluster Manager

John Marshall, 5 Pandora Objects Calo Hit Position + normal vectors Calorimeter cell size Absorber material in front of cell Time of first energy deposition Calibrated energy (mip equivalent, EM, Had) Layer + pseudolayer Hit type + detector region Density weight Surrounding energy IsDigital, IsIsolated + IsPossibleMip flags Associated MC particle Associated user object Track 2D impact parameters Momentum at d.c.a Particle mass Charge sign Start track state End track state ECal track state ReachesECal flag List of track state projections to calorimeter surfaces Associated cluster Associated MC particle Associated user object PFO formation flag “Clusterless” PFO formation flag Cluster List of constituent calo hits, ordered by pseudolayer Mip fraction EM energy measure Had energy measure Initial direction Current direction Result of linear fit to all hits in cluster Energy-weighted centroid ShowerStart layer Shower profile properties List of associated tracks Particle Flow Object PDG Code Charge Mass Energy Momentum List of tracks List of clusters The Pandora objects provide a mixture of properties specified by the user and value-added properties. They are all simple and well defined physics quantities for use in the particle flow algorithms.

John Marshall, 6 Pandora Framework The Pandora Managers store named lists of objects. These can be accessed by Pandora algorithms, which perform the reconstruction. Algorithms interact with the Managers in a controlled way, via PandoraContentAPI, so the Managers can perform the memory management. At any instant, each Manager designates a particular list as the “current list”, which can be requested by an algorithm. By manipulating the current lists, parent algorithms can control the scope and behaviour of nested daughter algorithms. Algorithms can use the PandoraContentAPI to modify lists and/or save new lists; all the book-keeping is performed by the Managers. Algorithms can use the APIs without worrying about how the managers work – separation of physics and C++ memory management! Pandora Content API TrackManager TrackList CaloHitManager MUON CaloHitList ClusterManager MUON ClusterListECAL/HCAL ClusterList Example Algorithms Algorithm divides hits into separate lists e.g. ECAL/HCAL & MUON. By changing current hit list, can re-use same clustering algorithm to form separate cluster lists. Clusters in the two lists can then be compared, merged and saved in a new named list. ECAL/HCAL CaloHitListFinal Merged ClusterList

John Marshall, 7 Pandora Algorithms In the new Pandora framework, the algorithms perform the actual particle flow reconstruction. The algorithms should contain almost exclusively physics-driven code, calling high-level APIs to perform the following operations: Create new clusters and particle flow objects Modify clusters, by adding hits, merging or deleting Access the current lists of Pandora objects Save new lists of clusters, calo hits or tracks Run a daughter algorithm, etc... Cluster-Track Association Asks for current track and cluster lists. Loop over lists to identify cluster with most compatible position and direction for each track. Ask to form track-cluster association. Pandora algorithms should contain just the kernel of a well-defined operation. Algorithms can be “blind” to their external environment, allowing for efficient re-use of code when the current lists are changed. Helper functions are provided to perform tasks useful to multiple algorithms, such as performing a linear fit to layers of a cluster. Cluster Merging Asks for current cluster list. Loop over list to identify associated clusters e.g. nearby clusters that point towards each other. Ask to merge candidate clusters. Example algorithms

John Marshall, 8 To reproduce original Pandora clustering algorithm in new framework, the requirements are: i. A parent algorithm to control operations, ii. A cluster formation algorithm, iii. Topological association algorithms (optional). Example Algorithm: Clustering 1.Parent algorithm asks to run a clustering algorithm; cluster manager then creates a new temporary cluster list, associated with the parent algorithm, sets this as “current”, and allows new clusters to be formed. 4.Parent algorithm can save the temporary clusters as a new named cluster list. Can set new list as current list for future algorithms, if desired. Any remaining temporary clusters will be tidied automatically. 2.Daughter clustering algorithm gets current hit and track lists, populates temporary cluster list and returns control to parent algorithm. 3.Parent algorithm calls topological association algorithms, which cannot form new clusters, but can modify or merge existing clusters.

John Marshall, 9 Change clustering parameters and/or clustering algorithm until cluster splits and get sensible track-cluster match 10 GeV Track 30 GeV 12 GeV 18 GeV 1. Identify inconsistent pairing of track and cluster(s) and ask to recluster these. Relevant clusters moved to new temporary cluster list. Current hit/track lists changed. 2. Ask to run a clustering algorithm. Creates another uniquely named temporary cluster list, filled by daughter clustering algorithm. 3. Calculate figure of merit for consistency of track and new cluster(s). 4. Repeat stages 2. and 3. as required. Can re-use original clustering algorithm, with different parameters, or try entirely new algorithms. 5. Choose most appropriate cluster(s). All lists will be reorganised and tidied accordingly. An important part of the original Pandora reconstruction is “reclustering”; attempting to redistribute hits between clusters in order to improve consistency between cluster energies and associated track momenta. Example Algorithm: Reclustering Parent reclustering algorithm operations:

John Marshall, 10 Algorithm Configuration ClusteringType1 parameters... ClusteringType2 parameters... ClusteringTypeN parameters... The Pandora framework is designed to make algorithm tuning painless. Algorithms can be swapped in/out without recompiling and there are no hard-coded parameters. Algorithms are configured via xml; a natural way to configure nested structures: Ideal for quickly experimenting with running new algorithms. Easy to mix “real” and “cheating” algorithms. The process of reclustering is reduced to the following simple configuration: Every algorithm has a ReadSettings method, called at startup. In this method, algorithm parameters are assigned default values and the xml file is parsed to see if values have been overridden. Algorithms within the top-level xml tags are automatically called (in order) for each event. These algorithms can, in turn, call their own daughter algorithms.

John Marshall, 11 Customising Pandora To make it easy for people to get involved and try out new ideas, Pandora users can register and use their own (existing) content, including: Particle identification functions, Hadronic and electromagnetic energy correction functions, Custom particle flow algorithms, allowing for a completely different reconstruction. Pandora API is used to pass addresses of helper functions and algorithm “factories” to managers. Gives Pandora the ability to call the helper functions and to create and run instances of the user algorithms. The algorithms and helper functions compiled as part of the Pandora library should have no dependencies and be (subject to steering parameters) detector independent. This need not apply to custom content. This provides a new opportunity to bring together many independent analyses within Pandora... Pandora API Plugin Manager Algorithm Manager Particle id functions Client Application Custom algorithms Energy correction functions Pandora

John Marshall, 12 Including External Content The ability to create custom algorithms, compiled as part of the client application, means it is trivial to write simple wrapper algorithms for external content. These algorithms can bring results from external packages right into the Pandora reconstruction. For example, can very simply (~50 lines of code) use output from GARLIC to replace photon identification.  + Final Pandora reconstructionGARLIC photon clustersStandard Pandora reconstruction for remaining hits Example procedure for adding photon clusters identified by an external package: i.Run GARLIC as normal, saving output photon clusters in lcio collection ii.Early in Pandora reconstruction, use wrapper algorithm to recreate photon clusters within Pandora. iii.Tag clusters as photons and save in a named list, removing hits from subsequent reconstruction. iv.Add the photon clusters to back into reconstruction just before PFO construction.

John Marshall, 13 Original Pandora Clustering Algorithm Topological Association Algorithms Track-cluster Association Algorithms Reclustering Algorithms Cluster first layer position Projected track position Cone-based forward projective method Continued Looping tracks Cone associations Back-scattered tracks Track segment pointing to shower Proximity Having created the new framework, reimplementation of the original Pandora code could begin. This required implementation of the following algorithms:

John Marshall, 14 Original Pandora Neutral hadron Charged hadron Photon Track-cluster Association Algorithms Reclustering Algorithms Photon Recovery Algorithms Fragment Removal Algorithms PFO Construction Algorithms 9 GeV 6 GeV Layers in close contact 9 GeV 6 GeV 3 GeV Fraction of energy in cone 12 GeV32 GeV 18 GeV 30 GeV Track 38 GeV Continued

John Marshall, 15 Performance at LCWS10 After reimplementation, the first tests were performed for ILD, using MC samples of approximately 10,000 Z  uds generated with the Z decaying at rest with E z = 91.2GeV. At this energy, all newly implemented code and framework was exercised, without (a strong) need for statistical reclustering and/or leakage corrections (at the time, was still work in progress for new Pandora). Excellent agreement observed, by construction. PFO energy sum, old vs. new E j = 45GeV

John Marshall, 16 Current Performance Performance for high jet energies (E j > 500 GeV) is a very active area of development. A number of important changes have been made recently e.g. “forced” clustering if no suitable reclustering candidates are found. CLIC_ILD, E j 45GeV100GeV250GeV500GeV1TeV1.5TeV PandoraPFANew, rms 90 (E j ) / E j 3.55 ± ± ± ± ± ± 0.05 ILD, E j 45GeV100GeV180GeV250GeV PandoraPFANew, rms 90 (E j ) / E j 3.62 ± ± ± ± 0.04 Reconstruction of CLIC_ILD Z  uds events, E z = 1, 2, 3TeV, |cos  | < 0.7

John Marshall, 17 Summary A robust framework for running decoupled particle flow algorithms has been implemented and validated. Detector or software-specific issues are resolved in a Pandora client application, allowing for re-use of the same Pandora algorithms for e.g. ILD and SiD. Pandora algorithms perform the particle flow reconstruction, using high-level API functions to communicate with the Pandora framework, which will perform memory management operations. The new framework allows customisation with user-specific algorithms, particle identification functions and energy correction functions. Simple wrapper algorithms allow content from external software packages to be quickly included in the Pandora reconstruction. The original version of Pandora has been reimplemented and fully validated within the new framework; its jet energy reconstruction performance continues to impress. Since this reimplementation, many improvements have been made to the high energy jet reconstruction and particle identification performance. If anyone is interested in contributing ideas or content to Pandora, please get in touch.