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Evolutionary Algorithm for Particle Trajectory Reconstruction within Inhomogeneous Magnetic Field in the NA61/SHINE Experiment at CERN SPS

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Języki publikacji
EN
Abstrakty
EN
In this paper, a novel probabilistic tracking method is proposed. It combines two competing models: (i) a discriminative one for background classification; and (ii) a generative one as a track model. The model competition, along with a combinatorial data association, shows good signal and background noise separation. Furthermore, a stochastic and derivative-free method is used for parameter optimization by means of the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). Finally, the applicability and performance of the particle trajectories reconstruction are shown. The algorithm is developed for NA61/SHINE data reconstruction purpose and therefore the method was tested on simulation data of the NA61/SHINE experiment.
Rocznik
Tom
Strony
159--177
Opis fizyczny
Bibliogr. 38 poz., rys.
Twórcy
  • Faculty of Physics, Astronomy and Applied Computer Science Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland
  • CERN, CH-1211 Geneva 23, Switzerland
Bibliografia
  • [1] Mankel R., Pattern recognition and event reconstruction in particle physics experiments. Reports on Progress in Physics, 2004, 67, pp. 553–622.
  • [2] Billoir P., Track Fitting With Multiple Scattering: A New Method. Nucl. Instrum. Meth., 1984, A225, pp. 352–366.
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  • [4] Billoir P., Progressive track recognition with a Kalman like fitting procedure. Comput. Phys. Commun., 1989, 57, pp. 390–394.
  • [5] Fruhwirth R., Application of Kalman filtering to track and vertex fitting. Nucl. Instrum. Meth., 1987, A262, pp. 444–450.
  • [6] Kalman R.E., A new approach to linear filtering and prediction problems. Transactions of the ASME–Journal of Basic Engineering, 1960, 82(Series D), pp. 35–45.
  • [7] Myers S., The large hadron collider 2008-2013. International Journal of Modern Physics A, 2013, 28(25), pp. 1330035.
  • [8] Cornelissen T., Elsing M., Fleischmann S., Liebig W., Moyse E., Salzburger A., Concepts, Design and Implementation of the ATLAS New Tracking (NEWT). Technical Report ATL-SOFT-PUB-2007-007. ATL-COM-SOFT-2007-002, CERN, 2007.
  • [9] Collaboration A., Airapetian A., Cindro V., Filipčič A., Kramberger G., Mandić I., Mikuž M., Tadel M., Žontar D., ATLAS detector and physics performance. Technical design report. ATLAS, 1999.
  • [10] Palmonari F., CMS tracker performance. Nucl.Instrum.Meth., 2013, A699, pp. 144–148.
  • [11] Merkel P., CMS tracker performance. Nucl.Instrum.Meth., 2013, A718, pp. 339–341.
  • [12] CMS collaboration and others, Description and performance of track and primary-vertex reconstruction with the cms tracker. Journal of Instrumentation, 2014, 9(10), pp. P10009.
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  • [15] Amoraal J., Collaboration L., et al., Alignment of the LHCb detector with kalman filter fitted tracks. In: Journal of Physics: Conference Series. vol. 219., IOP Publishing, 2010, pp. 032028.
  • [16] Rodrigues E., The LHCb track kalman fit. Note LHCb-2007-014, 2007, 164.
  • [17] Hernando J., Rodrigues E., Tracking event model, LHCb internal note, LHCb-2007-007. CERN-LHCb-2007-007.
  • [18] Schiller M., Standalone track reconstruction for the Outer Tracker of the LHCb experiment using a cellular automaton. PhD thesis, Uni Heidelberg 2007.
  • [19] Passaleva G., A recurrent neural network for track reconstruction in the LHCb muon system. In: Nuclear Science Symposium Conference Record, 2008. NSS’08. IEEE, IEEE, 2008, pp. 867–872.
  • [20] Pulvirenti A., Badala A., Barbera R., Lo Re G., Palmeri A., et al., Neural tracking in the ALICE Inner Tracking System. Nucl. Instrum. Meth., 2004, A533, pp. 543–559.
  • [21] Badala A., Barbera R., Lo Re G., Palmeri A., Pappalardo G., et al., Combined tracking in the ALICE decetctor. Nucl. Instrum. Meth., 2004, A534, pp. 211–216.
  • [22] Badala A., Barbera R., Re G.L., Palmeri A., Pappalardo G., Pulvirenti A., Riggi F., Neural tracking in alice. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2003, 502(2), pp. 503–506.
  • [23] Strandlie A., Frühwirth R., Track and vertex reconstruction: From classical to adaptive methods. Reviews of Modern Physics, 2010, 82(2), pp. 1419.
  • [24] Abgrall N., et al., Na61/shine facility at the cern sps: beams and detector system. Journal of Instrumentation, 2014, 9(06), pp. P06005.
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  • [26] Wyszynski O., Trigger system of the NA61/SHINE experiment at the CERN SPS, 2014.
  • [27] Laszlo A., Denes E., Fodor Z., Kiss T., Kleinfelder S., Soos C., Tefelski D., Tolyhi T., Vesztergombi G., Wyszynski O., Design and performance of the data acquisition system for the na61/shine experiment at cern. arXiv preprint arXiv:1505.01004, 2015.
  • [28] Gorbunov S., Kisel I., Analytic formula for track extrapolation in nonhomogeneous magnetic field. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2006, 559(1), pp. 148–152.
  • [29] Jordan A., On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in neural information processing systems, 2002, 14, pp. 841.
  • [30] Hand D.J., Yu K., Idiot’s bayesnot so stupid after all? International statistical review, 2001, 69(3), pp. 385–398.
  • [31] Domingos P., Pazzani M., Beyond independence: Conditions for the optimality of the simple bayesian classifier. In: Machine Learning, Morgan Kaufmann, 1996, pp. 105–112.
  • [32] Hansen N., Ostermeier A., Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation. IEEE 1996 pp. 312–317.
  • [33] Hansen N., Ostermeier A., Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 2001, 9(2), pp. 159–195.
  • [34] Hansen N., The CMA evolution strategy: a comparing review. In Lozano J., Larranaga P., Inza I., Bengoetxea E., eds.: Towards a new evolutionary computation. Advances on estimation of distribution algorithms. Springer 2006 pp. 75–102.
  • [35] Jastrebski G.A., Arnold D.V., Improving evolution strategies through active covariance matrix adaptation. In: Evolutionary Computation, 2006. CEC 2006. IEEE Congress on. IEEE 2006 pp. 2814–2821.
  • [36] Agostinelli S.e.a., GEANT4: A simulation toolkit. Nuclear Instruments and Methods in Physics Research, 2003, A506, pp. 250–303.
  • [37] Sipos R., Laszlo A., Marcinek A., Paul T., Szuba M., Unger M., Veberic D., Wyszynski O., The offline software framework of the na61/shine experiment. In: Journal of Physics: Conference Series. vol. 396., IOP Publishing, 2012, pp. 022045.
  • [38] Irmscher D., Philosophy and parts of the global tracking chain. NA49 Note numer 131 (1997).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c8095fda-1525-4a24-b425-42550e18102e
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