Atlas CHEP‘2000 Padova, ITALY February 2000 Implementation of an Object Oriented Track Reconstruction Model into Multiple LHC Experiments

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 2 CHEP‘2000 Padova, ITALY February 2000 Outline Overview of LHC, CMS and Atlas Review of the OO track reconstruction model Implementation of the OO model into CMS ORCA (O bject-oriented R econstruction for C MS A nalysis ) Implementation of the OO model into Atlas LVL-2 trigger reference SW and performance Experience and lessons learned Summary and prospects

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 3 CHEP‘2000 Padova, ITALY February 2000 L H C

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 4 CHEP‘2000 Padova, ITALY February 2000 CMS and ATLAS CMS

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 5 CHEP‘2000 Padova, ITALY February 2000 Trackers of CMS and Atlas CMS ATLAS

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 6 CHEP‘2000 Padova, ITALY February 2000 OO Tracker Model (I) Brief history -- Initial analysis and design by Irwin GAINES and Sijin QIAN (3/1995) -- Started C++ coding based on a Fortran Kalman filter tracking package (9/95) -- The model re-design by using STL (2/1996) -- Accomplished the tracking finding and fitting (autumn 1996) -- Ported to Windows NT (the beginning of 1997) ** a contribution published in proceedings of CHEP’97, Berlin, Germany (4/1997) -- A partial re-design to form an abstract Track_builder class (summer 1997) -- Tested in Atlas environment (end of 1997) and started LVL-2 implem. (9/98) ** a presentation by Irwin at CHEP’98, Chicago, U.S.A. (9/1998) -- Produced the pure C++ version (3/1999) ** a presentation by Sijin at AIHENP’99, Crete, Greece (4/1999) -- Released to Atlas LVL-2 reference SW (5/1999) and Ctrig (8/1999) -- Preliminarily integrated into ORCA (8-9/1999)

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 7 CHEP‘2000 Padova, ITALY February 2000 OO Tracker Model (II) Main ingredients of the OO model: –Problem statement ==> class diagram –Function statement ==> object diagrams and message trace diagrams Main features of the model: –Use well known HEP concepts for classes –Extensive use of Standard Template Library (STL), in both model design and C++ coding –Initially successful re-use of FORTRAN legacy code (for Kalman filtering method) in the member functions of various classes; later converted them to C++ in a straightforward manner; now pure C++ –Flexible enough to be re-used in multiple HEP experiments (only implementation of layer class is different)

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 8 CHEP‘2000 Padova, ITALY February 2000 The latest version of class diagram

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 9 CHEP‘2000 Padova, ITALY February 2000 Object Diagrams First scenario Second scenario

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 10 CHEP‘2000 Padova, ITALY February 2000 CMS Implementation Uses first scenario (internal hit triplets as seeds) Conceptual structure of “mytest.cpp” in the ORCA example directory –RecHit objects (in ORCA) are converted to “hit” object of the OO model. –Layer objects of OO model are instantiated with hard-wired code at this stage, can be done from ORCA tracker geometry in future. –All classes in the OO KF tracking model are brought in straightforwardly without any major change. Examples of reconstructed tracks from the output log file

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 11 CHEP‘2000 Padova, ITALY February 2000 Conceptual structure of “mytest.cxx” in ORCA example directory

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 12 CHEP‘2000 Padova, ITALY February 2000

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 13 CHEP‘2000 Padova, ITALY February 2000

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 14 CHEP‘2000 Padova, ITALY February 2000 A Reconstructed Event

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 15 CHEP‘2000 Padova, ITALY February 2000 Atlas Implementation (I) LVL-2 trigger requirement (O(10ms) time slot) ==> execution time is a factor of concern. – use 2nd scenario (outside seeds) – do reconstruction in Region of Interest (RoI) instead of in full  -  range Frameworks – LVL-2 Reference Software system (OO designed, input ASCII data) – Ctrig (input from ASCII files, produce ntuples, etc.) Seed modes: (1) TRT guided (inward pattern recognition) (2) pixel guided (outward pattern recognition) Tracking corrections: (1) Multiple scattering (yes); (2) Energy loss (yes for electrons); (3) Non-uniform B field (in near future).

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 16 CHEP‘2000 Padova, ITALY February 2000 Energy Loss Correction   (1/Pt) distribution (1/GeV) 1 GeV5 GeV 20 GeV electrons electrons electrons

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 17 CHEP‘2000 Padova, ITALY February 2000 Atlas Implementation (II) Performance: – Execution speed (on 300 MHz Pentium II, under Linux) TRT seeded Pixel seeded Single  (no pile-up) ms/event -- B-physics (at low luminosity) ms/event ms/event ( seeds) ( seeds) – Memory usage is in O(10 MB), depends on data volume – Efficiencies (single track events, B-physics events) – Resolutions (momentum, impact parameter, etc.) – B-physics study in the channel B -->  – Many more results are at

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 18 CHEP‘2000 Padova, ITALY February 2000 Efficiencies for  and e muons electrons –

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 19 CHEP‘2000 Padova, ITALY February 2000 Efficiencies for B-physics (1)  vs. Pt  for e from B 0 --> J/   vs.  –

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 20 CHEP‘2000 Padova, ITALY February 2000 Efficiencies for B-physics (2)  vs. Pt  for e from B 0 -->   vs.  –

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 21 CHEP‘2000 Padova, ITALY February 2000 Resolutions  Pt 20 GeV  (tops: barrel; bottoms: endcap) D 0 –

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 22 CHEP‘2000 Padova, ITALY February 2000 B-physics mass reconstruction Reconstruction of B -->  mass hypothesis in ATLAS Level-2 trigger using the Kalman filtering method

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 23 CHEP‘2000 Padova, ITALY February 2000 Lessons Learned C++’s new features (for previous Fortran programmers) now can be handled by some powerful debugging tools, e.g. –“INSURE++” to detect the memory leaks –“DDD” graphic debugger to detect the pointer bugs Integration of an OO model into a general OO environment of whole experiment needs a stable framework with a stable interface. As soon as all functionality of I/O objects needed by the model are available, the integration is straightforward.

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 24 CHEP‘2000 Padova, ITALY February 2000 Conclusions OO makes it easier to do cooperative development among widely separated collaborators (promotes truly modular designs) OO makes re-use easier: the implementation of this OO model into two LHC experiments demonstrates: -- the implementation structure is very similar for different experiments -- the experience in code development can be shared among different experiments immediately

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 25 CHEP‘2000 Padova, ITALY February 2000 Summary and Prospects We are moving towards a realistic OO track reconstruction model for HEP experiments –the memory usage of this OO model is moderate –the track finding efficiency is satisfactory –the execution speed is approaching the LVL-2 trigger requirement It can be used in different experiments with only minor modifications

CMS & Atlas Atlas OO Tracker Model Implementation S. Qian 26 CHEP‘2000 Padova, ITALY February 2000 Ongoing and Future Work CMS: -- Investigate more efficient I/O functions -- Use finalized CMS detector and track classes -- Overhaul the package with CMS coding rules Atlas: -- Implement the non-uniform B-field correction -- Optimize the performance by investigating new seeding methods and by exploring new reconstruction strategies