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Title: Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

Krzysztof M. Graczyk, Piotr Płoński, Robert Sulej

Function approximation, limited amount of training data, Bayesian Framework.

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for χ2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.

Full-text: arXiv:1006.0342, JHEP 2 June 2010.

Title: Direct measurement of the gluon polarisation in the nucleon via charmed meson production

COMPASS Collaboration

NetMaker used in alanysis of aLL parameters in D0 events.

We present the first measurement of the gluon polarisation in the nucleon based on the photon-gluon fusion process tagged by charmed meson production and decay to charged K and pi. The data were collected in polarised muon scattering off a polarised deuteron target by the COMPASS collaboration at CERN during 2002-2004. The result of this LO analysis is <Delta g/g>_x = -0.47 +- 0.44 (stat) +- 0.15 (syst) at <x> ~= 0.11 and a scale mu^2 ~ 13 (GeV/c)^2.

Full-text: arXiv:0802.3023v1 [hep-ex], 21 Feb 2008.

Title: Application of the neural networks in events classification in the measurement of spin structure of the deuteron

R. Sulej 1), K. Zaremba 1), K. Kurek 2), and E. Rondio 2)
1) Institute of Radioelectronics, Warsaw University of Technology, Warsaw, Poland
2) Institute for Nuclear Studies, Warsaw, Poland

Algorithm description can be found in this paper (except the last network growth improvement - split neurons).

In this paper, we present the application of a neural network for events classification in a high-energy physics experiment. As a network model we use a multi-layer perceptron with a dynamic topology adjustment algorithm. Our solution covers both adding new hidden neuron units and removing unnecessary units. Neural network results are compared to the standard kinematical cuts technique and to the well-known k-nearest neighbor (knn) classifier.

Full-text is published in Meas. Sci. Technol., Vol. 18 (2007), pp. 2486-2490 (abstract). Full text (but not in the MST paper format) is also available here.

Title: Polarization effects in tau production by neutrinos

J. Lagoda, D. Kielczewska, M. Posiadala, R. Sulej, K. Zaremba, T. Kozlowski, K. Kurek, P. Mijakowski, P. Przewlocki, E. Rondio, J. Stepaniak, M. Szeptycka

Application of the algorithm in the neutrino interactions classification.

A direct proof of the existence of nmnt oscillations is important. This proof can be obtained by an observation of the production of taons in charge current reactions nt + N → t + X. The influence of t polarization on the characteristics of the CC events and on the efficiency of their selection is discussed. The neural network method is used to select t leptons produced in nt interactions.

Full-text is published in Acta Physica Polonica B, Vol. 38, No. 6 (2007), pp. 2083-2103 (free access to online paper here).

Title: Measurements of Delta G/G

G. K. Mallot

Results of DG/G analysis presented at SPIN2006, Kyoto (neural network trained with dynamic structure algorithm was used in aLL parametrization).

Our present information on the gluon polarisation DG/G is reviewed. The data from fixed-target lepton-nucleon experiments are in context with the recent data from the RHIC polarised pp collider. The main tools to study DG/G in lepton-nucleon scattering are scaling violations of the g1 structure functions and longitudinal spin asymmetries in hadron production. Results from high-pT hadron pairs, inclusive hadrons as well as open-charm production are discussed. At RHIC the most precise data presently came from inclusive p0 and jet production. All data indicate that the gluon polarisation is small compared to earlier expectations, but still can make a major contribution to the nucleon spin.

Full-text: Proceedings for SPIN2006, Kyoto or arXiv:hep-ex/0612055v1.

Title: Dynamic topology adjustment algorithm for MLP networks

R. Sulej 1), K. Zaremba 1), and K. Kurek 2) 1) Institute of Radioelectronics, Warsaw University of Technology, Warsaw, Poland 2) Institute for Nuclear Studies, Warsaw, Poland

Algorithm has been changed significantly since this paper was published. Anyway, these were our first attempts with dynamic network structure...

In this paper we present new algorithm for network topology adjustment during the training process. As a network model we use multi-layer perceptron (MLP) trained with various back-propagation techniques. Our solution covers both adding new hidden neuron units and removing unnecessary units. We present the test results on a basic tasks to show some characteristics of our algorithm and compare it with other well known model - Cascade-Correlation network. Also we give a brief view of applications of our algorithm in high energy physics classification and approximation tasks.

Full-text is published in ICAISC proceedings: "Artificial Intelligence and Soft Computing", Academic Publishing House EXIT, Polish Neural Network Society, Academy of Humanities and Economics in Łódź, IEEE Computational Intelligence Society - Poland Chapter, 2006.

Conference poster in pdf format (1.84MB)

Title: Spin asymmetries for events with high pT hadrons in DIS and an evaluation of the gluon polarization

The SMC Collaboration

Application of the fixed-structure network.

We present a measurement of the longitudinal spin cross section asymmetry for deep-inelastic muon-nucleon interactions with two high transverse momentum hadrons in the final state. Two methods of event classification are used to increase the contribution of the photon-gluon fusion process to above 30%. The most effective one, based on a neural network approach, provides the asymmetries AplN→lhhX = 0.030 ± 0.057(stat) ± 0.010(syst) and AdlN→lhhX = 0.070 ± 0.076(stat) ± 0.010(syst). From these values we derive an averaged gluon polarization DG/G = –0.20 ± 0.28(stat) ± 0.10(syst) at an average fraction of nucleon momentum carried by gluons <h> = 0.07.

Full-text is published in Physical Review D, 70:012002 (2004).

Title: Selection of Photon Gluon Fusion Events in DIS

K. Kowalik, E. Rondio, R. Sulej, K. Zaremba

One more old paper.

A selection of the Photon Gluon Fusion (PGF) process with light quarks for deep inelastic scattering events is presented. This process is directly sensitive to gluon polarization and our goal is to find out the most effective selection on a sample of events simulated for the SMC experiment. We compare two general multi-class classification methods - Bayes method and neural network with a conventional selection procedure. The neural network algorithm presented here is a modification of method belonging to the family of directional minimization algorithms. This method is convenient and effective for photon gluon fusion selection and determination of gluon polarization. Finally we present the estimation for precision of gluon polarization for neural network method.

Full-text is published in Acta Physica Polonica B, Vol. 32, No. 10 (2001), page 2929.