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Possible contributions of JINR-LIT to CBM

Possible contributions of JINR-LIT to CBM. V.V. Ivanov Laboratory of Information Technologies, Joint Institute for Nuclear Research CBM Collaboration Meeting, GSI , February 11 - 2 3 , 200 4. Direction: Networking, Computing, Computational Physics.

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Possible contributions of JINR-LIT to CBM

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  1. Possible contributions of JINR-LIT to CBM V.V. Ivanov Laboratory of Information Technologies, Joint Institute for Nuclear Research CBM Collaboration Meeting, GSI, February11 - 23, 2004

  2. Direction: Networking, Computing, Computational Physics 1. Information, Computer and Network Support of the JINR's Activity Provision of theoretical and experimental studies conducted by the JINR Member State institutes at JINR and other scientific centers by telecommunication, network and information resources adequate to modern requirements of international scientific collaboration. 2. Computer Physics for Theoretical and Experimental Research Creation and development of methods for mathematical simulation of physics processes and analysis of data for theoretical and experimental research. Algorithmic and software support of computer simulation on the basis of new programming technologies with the use and optimization of computing systems of modern architecture and high-speed networks.

  3. Information, Computer and Network Support of the JINR's Activity 1. Provision of JINR and its Member States with high-speed telecommunication data links. 2. Creation of a high-speed, reliable and protected local area network (LAN) of JINR. 3. Creation and maintenance of the distributed high-performance computing infrastructure and mass storage resources. 4. Provision of information, algorithmic and software support of the JINR research-and-production activity. 5. Elaboration of the JINR GRID-segment and its inclusion in European and world GRID-structures.

  4. The JINR Central Computing and Informational Center (JINR-CCIC) is part of the Russian Information Computing Complex for processing information from the Large Hadron Collider (RICC-LHC). It comprises: • An interactive cluster of common access; • A computing farm for carrying out simulation and data processing for large experiments; • A computing farm for the tasks of the RICC-LHC project; • A computing farm for carrying out parallel calculations on the basis of modern network technologies (MYRINET, SCI, etc.); • Mass storage resources on disk RAID-arrays and tape robots.

  5. * EGEE, LCG

  6. Computer Physics for Theoretical and Experimental Research 1. Development of methods for modeling physical processes and experimental data analysis. 2. Creation of methods and numerical algorithms for modeling magnetic systems and transportation of charged particle beams. 3. Elaboration of software and computer complexes for experimental data processing; their application in JINR experiments. 4. Elaboration of numerical schemes and software for complex physical systems simulation. 5. Development of methods, algorithms and software of computer algebra.

  7. LHC ALICE ATLAS CMS LHCb COMPASS DIRAC Development of mathematical models, methods and codes for modeling and analysis of HEP experiments STAR FAZA EXCHARM GIBS SPHERE E391a HERA-B HEND KLOD

  8. Current status of CBM-LIT group • Fast robust track fitting in non-uniform magnetic field. • Particle identification algorithms for RICH. • Kalman fit for parabolic track model. • 3D approximation of magnetic field in the superconducting dipole magnet. • Basis for first level trigger modeling. • Concluding remarks.

  9. Fast robust track fitting in non-uniform magnetic field Goal: to develop fast & accurate tracking algorithm for the CBM trigger based on the SiliconPixel Detector. Idea: using the parametric form of track (parabola) accomplish both stages - track recognition and fittingsimultaneously. Fig.1. Y-Z view of the simulated SPD event. Both axes are in cm.

  10. Elastic tracking (ET) has been successfully applied for STAR TPC tracking in 1998 HOW to realize this idea? - vary track equation parameters, so that a curve corresponding to this equation (template) passes as close as possible through the track points. Physical interpretation:the better the elastic template fits points, the lower theenergy E of interactionbetween template points (distributed with densityρT(r)) and track points (distributed with densityρ(r’)) where V is theLorentz potential with a temperature-dependent width T is a constant, a is the maximal distance, at which points are still accredited to this template, andb~σresis spatial resolution of a detectorb << a.

  11. For discrete case the interaction energy is determined by the sum: N is the number of track points, and are the i-th measured point and its distance from template, respectively, and π is the set of the track parameters. In homogeneous magnetic field track is described by a helix with the curvature κ , helix angle λ, and phase Φat the point , i.e. E(π,t) depends only one track points. Although many tracks could be fitted simultaneously, G&H does not recommend such a synchronous fit. To avoid E(π,t) falling into local spurious minima,the simulated annealing procedure is applied.

  12. Our innovations to speed up ET calculations: • new iterative scheme of the least square method based on the robust approach; • new potential providing the weight function closer to the optimal one; • usage of parabolic templates corresponding to the detector geometry; • usage of the index addressing to optimize the search operations. We use 4-th order polynomial as the weight function (Tukey’s bi-weights) where residuals ε=yi-a zi2 – b zi – c; i=1,2,..,n; cTwas initially set to 10 and then decreasing on each iteration according to the sumulated annealing scheme; the rmsσis recalculated using the formula

  13. Elastic tracking. How it works C++ code is developed to implement the ET algorithm. Steps of the SPD simulation: 1. Simulation of the event (X,Z) with 20 tracks. 2. Digitalization, assuming chamber efficiency – 98% and noise - 2 counts/plane. ET steps: 1. Parabolic seed construction by hits in the first three planes. 2.Track recognition by elastic tracking. No blue tracks means that all tracks are recognized correctly. Green seeds are visible, since they’ve been drawn through hits of different tracks and then

  14. Preliminary accuracy results of vertex parameters estimation Vertex accuracy in Y-Z plane: σy =23 mkm; σz=82 mkm Y = aZ2 + bZ + c

  15. Distributions of parameter errors σa = 5 e-8 σb = 5.4 e-6 σc = 9.6 e-5 Y = aZ2 + bZ + c

  16. Distribution of atrue - akal Distribution of btrue - bkal Distribution of ctrue - ckal Kalman fit for parabolic track model Code for Kalman filtering of parabolic track model is elaborated. As estimation for starting values we take the parameters of a parabola which traces the three first measurements: y = ax2 + bx + c. R. Fruhwirth, Application of Kalman filtering to track and vertex fitting, NIMA262, (1987) 444-450. I. Abt, D. Emelianov, I Gorbounov andI. Kisel, Cellular automaton and Kalman filter based track search in the HERA-B pattern tracker, NIM A490 (2002) 546-558.

  17. RICH ring recognition and PID First CBM RICH simulations for a gas mixture of 40% He & 60% CH4 give pretty nice rings (http://www.gsi.de/GSI-Future/cdr/). These rings can be rather easy fitted either by the elastic ring method (I.Kisel, 1999) or by the robust ring approach (G.Ososkov, 1996). However, the CBM reality promises much worser conditions: see, for example, real CERES data with many ring overlaps and noise counts. Therefore, new more effective circle fitting and PID methods have to be developed: they must be robust against noise robust and use the amplitudes of the registered photons.

  18. Circle fitting based on hits Hits are gravity centers for clusters of granulated detector measurements. After clustering and noise removing, one has a set of hits(xi,yi), i=1,2,...n. They are fitted by a circle (R,a,b) applying the minimization of a functionalL(R,a,b) = Σwi ei2 with and where copt=0.2. Due to nonlinearity of L(R,a,b), a comparative study has been accomplished in the collaboration with N.Chernov from the Alabama University (see: www.math.uab.edu/cl/cl1).. The winner of about 20 algorithms gives the fastest iterative scheme based on the gradient approximation of L(R,a,b) and the Levenberg-Marquard scheme. This code needs less than 400 flops per data point.

  19. Particle identification by the Cherenkov radius r Using the amplitude-dependent 2D-weight function, we test the hypothesis that the measured radius corresponds to one of two particles, π- or K-mesons. The critical region r > r0 is chosen on the basis of the likelihood ratio criterion. In the CCCR method one counts the number of nonzero cells in the confidence ring around circles corresponding to both alternative particles.We proposed to count the sum of the amplitudes of all cells in the confidence region (the SACR method). Results for these methods based on simulated data for π- and K-mesons: CCCR - left plot, SACR – right plot. The probability of the misidentication for SACR (1%) is triple as smaller compared to the CCCR method.

  20. 3D APPROXIMATION OF MAGNETIC FIELD IN THE SUPERCONDUCTING DIPOLE MAGNET The magnetic field was calculated using the TOSCA code. 3D view of the superconducting dipole magnet

  21. To construct the magnetic field map the working region was subdivided on two hexahedralvolumes: Q1 and Q2.

  22. Let be the magnetic fields in these nodes. be the nodes ofelements and the corresponding node functions of the first order. Let Then the approximation of the magnetic field B(x) in point x is determined by: Symmetry of the magnetic field: By(x,y,z) = By(-x,y,z) = By(-x,-y,z) = By(x,-y,z); Bx(x,y,z) = -Bx(-x,y,z) = Bx(-x,-y,z) = -Bx(x,-y,z); Bz(x,y,z) = Bz(-x,y,z) = -Bz(-x,-y,z) = -Bz(x,-y,z). These symmetries permit to decrease the needed memoryin 4 times. Total number of nodes is approximately 900000. Memory - 10 MB .

  23. a) b) Distribution of By magnetic field component: a) in median plane, b) along the beam axis

  24. Ptolemy II Simulation of the Trigger

  25. Software modules for Ptolemy II based trigger prototype simulations • Java modules – simulate trigger prototype network components: • C program – generates the XML description of the scalable 3D thorus architecture : A scheduling network permits one to avoid network congestion.

  26. Main goal: global event reconstruction • 3D magnetic field (SDM) for GEANT. • Fast and accurate track recognition: Kalman filter, Hough transform, Cellular Automaton. • Particle identification efficiencies using RICH. • Algorithms for primary and secondary vertices reconstruction. • Investigate different detector components aiming to achieve maximum reconstruction efficiency, robustness and speed of the event reconstruction algorithms. • Applying global tracking define the most optimal detector positions and granularity.

  27. Concluding remarks: • LIT structure: personal - 350, 3 divisions (1 scientific, 1 sc.-technical, 1 technical). • LIT CBM group includes today 15 persons: 3 Profs., 5 Doctors, 3 PhD students, 1 system programmer and 3 students. • Collaboration: KIP, Moscow and Dubna Universities, MIREA, JINR-UC. • Regular weekly meetings and quarterly workshops.

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