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

Measurement Science and Technology

Volumn 18, Issue 8, 2007, Pages 2486-2490

  Sulej, R ^a   Zaremba, K ^a   Kurek, K ^b   Rondio, E ^b  

^a WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

^b SOLTAN INSTITUTE FOR NUCLEAR STUDIES   (Poland)

Author keywords

Classification; High energy physics; Neural network; Pattern recognition; Spin structure

Indexed keywords

ALGORITHMS; COMPUTER NETWORKS; DEUTERIUM; HIGH ENERGY PHYSICS; NEURONS; PATTERN RECOGNITION; TOPOLOGY;

KINEMATICAL CUTS; LAYER PERCEPTRON; SPIN STRUCTURE;

NEURAL NETWORKS;

ALGORITHMS; COMPUTER NETWORKS; DEUTERIUM; HIGH ENERGY PHYSICS; NEURAL NETWORKS; NEURONS; PATTERN RECOGNITION; TOPOLOGY;

EID: 34547403697     PISSN: 09570233     EISSN: 13616501     Source Type: Journal    
DOI: 10.1088/0957-0233/18/8/025     Document Type: Article

References (11)

1. Ageev E S et al (the COMPASS Collaboration) 1996 Common Muon and Proton Apparatus for Structure and Spectroscopy CERN/SPSLC (SPSC/P297) 96-14
2. Ashman J et al (the EMC Collaboration) 1989 An investigation of the spin structure of the proton in deep inelastic scattering of polarized muons on polarized protons Nucl. Phys. B 328 1-35
3. T hadrons in DIS and an evaluation of the gluon polarization Phys. Rev. D 70 012002
4. Denby B 1988 Neural networks and cellular automata in experimental high energy physics Comput. Phys. Commun. 49 429-448
5. Denby B, Shpakov D and Wyss L J 1995 Applications of neural networks in high energy physics Proc. Int. Conf. on Artificial Neural Networks ICANN'95 (Paris, France) vol 1 pp 615-22
6. Ingelman G, Edin A and Rathsman J 1997 Lepto 6.5-A Monte Carlo generator for deep inelastic lepton-nucleon scattering Comput. Phys. Commun. 101 108
7. Fahlman S 1988 An empirical study of learning speed in back-propagation networks CMU-CS-88-162 Report Carnegie Mellon University
8. Fahlman S and Lebiere C 1991 The cascade-correlation learning architecture Advances in Neural Information Processing Systems vol 2 (San Mateo, CA: Morgan Kaufmann) pp 524-32
9. Jankowski N and Kadirkamanthan V 1997 Statistical control of growing and pruning in RBF-like neural networks Proc 3rd Conf. Neural Networks and Their Applications (Kule, Poland) pp 663-70
10. Chauvin Y 1989 A back-propagation algorithm with optimal use of hidden units Advances in Neural Information Processing Systems vol 1 (San Mateo, CA: Morgan Kaufmann Publishers) pp 519-26
11. Hassibi B and Stork D 1993 Second order derivatives for network pruning: optimal brain surgeon Advances in Neural Information Processing Systems vol 5 (San Mateo, CA: Morgan Kaufmann Publishers) pp 164-71