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A neural approach to the analysis of CHIMERA experimental data

S. Aiello1, M. Alderighi2, A. Anzalone3, M. Bartolucci4, G. Cardella1, S. Cavallaro5, E. DeFilippo1, S. Femino'6, E. Geraci3, F. Giustolisi5, P. Guazzoni4, M. Iacono Manno3, G. Lanzalone7, G. Lanzano'1, S. LoNigro7, G. Manfredi4, A. Pagano1, M. Papa1, S. Pirrone1, G. Politi8, F. Porto5, S. Russo9, S. Sambataro7, G.R. Sechi2, L. Sperduto5, C. Sutera1, L. Zetta4
  1. Istituto Nazionale di Fisica Nucleare, Catania, Italy
  2. Istituto di Fisica Cosmica, CNR, Milano, Italy
  3. Laboratorio del Sud, Catania, Italy
  4. Dipartimento di Fisica dell'Universita' degli Studi and Istituto Nazionale di Fisica Nucleare, Milano, Italy
  5. Laboratorio del Sud and Dipartimento di Fisica dell'Universita', Catania, Italy
  6. Gruppo Collegato di Messina, I.N.F.N.,Catania, Italy
  7. Dipartimento di Fisica dell'Universita', Catania, Italy
  8. Istituto Nazionale di Fisica Nucleare and Dipartimento di Fisica dell'Universita', Catania, Italy
  9. Dipartimento di Fisica dell'Universita', Milano, Italy

Speaker: Monica Alderighi

  CHIMERA (Charged Heavy Ions Mass and Energy Resolving Array) is a second generation 4$\pi$ detector for high resolution light particles and fragments measurements in the field of intermediate energy nuclear physics (20$\leq$ MeV/A$\leq$100) at LNS (Laboratorio Nazionale del Sud). The paper describes a novel approach for the automatic identification of the Z-lines in the 2D representation ($\Delta$E, L) of CHIMERA experimental data. It is based on the pre-attentive mechanisms modeled from human vision as proposed by S. Grossberg, and uses a two-level system of unsupervised neural networks. The system produces as a result binary images, in which areas of contiguous elements having value 1 (strips) represent the clusters of points, in the experimental images, that can be associated to the same Z value. The Z-lines are then determined by means of an algorithm calculating the central line of the strips thus obtained. The first level of the neural system is composed of an on-center, off-surround shunting network, which implements an adaptive discrimination of the densities of the image points. At the second level, a family of neural networks transforms the result produced by the first level into continuous strips (completion process) by means of oriented long-distance cooperations. In the paper some examples of scatter plots and resulting frequency distributions are shown.

Presentation:  PowerPoint Short Paper:  Adobe Acrobat pdf 

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