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The RAVE Vertex Reconstruction Toolkit: Status and New Applications The RAVE Vertex Reconstruction Toolkit: status and new applications Wolfgang Waltenberger, Fabian Moser and Winfried Mitaroff with contributions by Bernhard Pflugfelder and Herbert V. Riedel Austrian Academy of Sciences, Institute of High Energy Physics Nikolsdorfer Gasse 18, A-1050 Vienna, Austria, Europe Outline: • The RAVE toolkit and VERTIGO framework in short; • Improved track reconstruction by RAVE’s smoother; • Progress on the implementation of kinematics fitting; • Summary: present status and outlook to the future. W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007 Reminder of the main goals 1. Creation of an extensible, detector-independent toolkit (RAVE) for vertex reconstruction, to be embedded into various environments: • RAVE = “Reconstruction (of vertices) in abstract versatile environments”; • The toolkit includes both vertex finding (a pattern recognition task a.k.a. track bundling) and vertex fitting (estimation of the vertex parameters and errors); • Synergy used: starting point was the old CMS software (ORCA) which has recently been refactored and ported to the new CMS framework (CMSSW); • Principal algorithmic assets are robustified reconstruction methods with estimators based on adaptive filters, thus downweighting the influence of outlier tracks; • RAVE is foreseen to stay source-code compatible with CMSSW, but thanks to its generic API may easily be embedded into other software environments. 2. Creation of a simple stand-alone framework (VERTIGO) for fast implemen- tation, debugging and analyzing of the core algorithms: • VERTIGO = “Vertex reconstruction tools and interfaces to generic objets”; • Framework tools available: Visialization, Histogramming, a Vertex Gun for generating artificial test events, an LCIO input event generator, and a Data Harvester & Seeder for flexible I/O. • VERTIGO is able to emulate various detector setups by a flexible “skin” concept. W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007 Example of using the RAVE toolkit Calling RAVE from a C++ environment: % !"# $ & $ . / Note: the factory class name has recently been renamed rave::VertexFactory. W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007 Functionality of the VERTIGO framework TestLCIO CMS Belle Events Files Events Events? Vertex LCIO Data Gun EvtGen Seeder VertigoEventGenerators Tracks Skin RAVE CMS/LCIO/... VERTIGO Vertices VertigoObservers DATA Visual− Histo− Data− FLOW isation gramming Harvester Hdf/Root files W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007 What is a full geometric vertex fit ? Input to a geometric vertex fit: • Fitted tracks: pi, cov(pi, pi), i = 1 ...n (x,x) – pi = 5-vectors of track parameters, p p × – cov( i, i) = symmetric 5 5 matrices. (q1,x) (q1,q1) • Beam int. profile (optional): v, cov(v, v) – v = 3-vector of centre of the beam i.p., (q2,x) (q2,q1) (q2,q2) – cov(v, v) = symm. or diag. 3 × 3 matrix. (q3,x) (q3,q1) (q3,q2) (q3,q3) Results of a geometric vertex fit: • Vertex position: x, cov(x, x) – x = 3-vector of space coordinates, (qk,x) (qk,q1) (qk,q2) (qk,q3) (qk,qk) – cov(x, x) = symmetric 3 × 3 matrix. • Tracks at vertex: qi, cov(qi, qi), i = 1 ...n – qi = 3-vectors of re-fitted track parameters, (qn,x) (qn,q1) (qn,q2) (qn,q3) (qn,qk) (qn,qn) – cov(qi, qi) = symmetric 3 × 3 matrices (obtained by the Kalman “smoother”). • But this is not the full information from a vertex fitter; there are more covariances to deal with: – cov(qi, x), i = 1 ...n n asymmetric 3 × 3 matrices, – cov(qi, qj), i,j = 1 ...n with i =6 j n · (n − 1) asymmetric 3 × 3 matrices. In practice, the cov(qi, qj), i =6 j are only needed in case of a subsequent vertex re-fit with kinematic constraints. The n asymmetric cov(qi, x), however, should not be forgotten for persistency (e.g. LCIO). W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007 Tracks re-fitted by the smoother: zimp Tracks, resolution, z stats Refitted tracks, resolution, z stats imp imp Entries 124904 Entries 124791 10000 Mean -0.007457 Mean 0.09153 RMS 27.01 20000 RMS 11.83 χ2 / ndf 1855 / 82 χ2 / ndf 3091 / 82 Constant (Core) 7441 ± 42.4 18000 Constant (Core) 1.727e+04 ± 87 8000 Mean (Core) -0.001728 ± 0.042109 Mean (Core) 0.0577 ± 0.0192 16000 Sigma (Core) 9.056 ± 0.057 Sigma (Core) 4.617 ± 0.019 Constant (Tail) 1076 ± 14.0 14000 Constant (Tail) 2000 ± 3.8 6000 Mean (Tail) -0.08068 ± 0.21108 Mean (Tail) 0.1558 ± 0.0893 Sigma (Tail) 43.12 ± 0.31 12000 Sigma (Tail) 15.84 ± 0.06 10000 4000 8000 6000 2000 4000 2000 0 0 -100 -50 0 50 100 -100 -50 0 50 100 res, z [µm] res, z [µm] imp imp • Track parameters in “perigee representation” (δT , ∆z, ϕ, . ) w.r.t. an arbitrarily defined pivot point; (fitted) (true) • histogram of zimp ≡ ∆z − ∆z of reconstructed tracks (left), and re-fitted tracks (right); • no use was made of the beam interaction profile. W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007 Kinematics fitting by RAVE Algorithm used: • The algorithm is based on a least squares fit with Lagrangian multipliers for the kinematic constraints; • Flexibility is achieved by separating the kinematics fitting proper from the “decay chain tree” steering; • For details, see K. Prokofiev, Proc. CHEP ’04, Interlaken (CH): CERN-2005-002 and http://indico.cern.ch/getFile.py/access?contribId=75&sessionId=4&resId=1&materialId=paper&confId=0 Implementation: • Work has started on 1 October: the core code has been extracted from the CMS framework CMSSW; • The wrapper code for interfacing with the RAVE toolkit is finished, and all has successfully compiled; • Calling it from a C++ environment proceeds via the new factory class rave::KinematicTreeFactory (both building the tree, and performing the fit); • Requires assignment of particle masses to tracks, and definition of the kinematic constraints. Tests in limbo: • Functional tests of the new code will be performed using the VERTIGO stand-alone framework; • Before starting, new VERTIGO tools must be implemented in order to support the kinematics: – a vertex gun generating kinematically correct events; – observers able to handle the kinematics information. W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007 Decay tree and kinematics fit The decay tree: KinematicTree The kinematic fit: LMS minimization Invalid production vertex 2 0 χ minimization with the set of additional constraints H y re f 0 , B s linearized (first order Taylor expansion) around some given point yexp 0 H yexp B s Decay Vertex yy H y D yd0 y exp exp D: matrix of derivatives, one line per constraint equation J/ Ψ Φ (n_equations x n_parameters) (Previous state) d: vector of values of constraints + − + − µ µΚ Κ Function to minimize with respect to y ref , : collection of trees can be created, Constraints 2 ref 1 ref T T y y V y y y 2 D y d min Invalid decay vertex (Present State) 0 + − + − Decay tree example for Bs → J/ψΦ → µ µ K K . W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007 0 → Example: Bs J/ψΦ (simulation CMS) Residual of the µ+µ−K+K- 4-track invariant mass with and without constraints: 70 Mean 0.1761E-01 Mean 0.3901E-02 RMS 0.3635E-01 RMS 0.2017E-01 32.42 / 25 120 45.96 / 21 Constant 60.30 3.239 Constant 101.4 5.911 60 Mean 0.1503E-01 0.1339E-02 Mean 0.3972E-02 0.6626E-03 Sigma 0.3194E-01 0.1108E-02 Sigma 0.1361E-01 0.5425E-03 Mean = 15 MeV 100 Mean = 4 MeV 50 = 32 MeV = 13 MeV 80 40 60 30 Events/6 MeV Events/10 MeV 40 20 10 20 0 0 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 Bs mass residual (Kalman) GeV Bs mass residual (Global fit) GeV M rec M PDG M rec M PDG Without constraintsBs Bs With constraints Bs Bs CHEP'04, Interlaken, Switzerland, 30 September 2004 K.Prokofiev, Th.Speer Universität Zürich W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007 Summary: status and outlook Present status: • Code written in C++; RAVE depends on CLHEP and Boost, VERTIGO’s visualization also on Coin3d; • Tested with Linux (Debian and SLC4), Mac OSX and CygWin/DLL, on Intel and PPC hardware; • RAVE may be called directly from C++, and with a wrapper (SWIG) also from Java and Python; • Adaptor classes (“glue code”) exist for embedding RAVE into the reconstruction frameworks Marlin (C++) and org.lcsim (Java); tested with LDC and SiD simulation data; • Simple VERTIGO skins exist for the LDC and SiD detectors; tested with LCIO formatted input data. Work in progress: • Interfacing RAVE with the ZvTop/ZvRes “topological vertex search” algorithm is almost finished; • Augmenting RAVE with “heavy flavour tagging” algorithms is in progress (see talk at LCWS ’07); • Augmenting RAVE with a powerful kinematics fitting algorithm has started (see the previous slides). Availability: • A beta release is available; our institute is committed to maintenance, documentation and distribution; • http://projects.hepforge.org/rave/ (HepForge repository for RAVE source code, docus, etc); • http://stop.itp.tuwien.ac.at/websvn/ (repository for Marlin & org.lcsim glue code, and VERTIGO). W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007 The Vertexing Circle’s credo ( ) * + + ,, $ $ $-. () $ $-/ + -0$ -. W. Waltenberger, F.Moser, W. Mitaroff ALCPG ’07, Fermilab: 22–26 Oct. 2007.
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