Last update:
Apr 1, 2000
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A neural approach to the analysis of CHIMERA experimental data
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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
- Istituto Nazionale di Fisica Nucleare, Catania, Italy
- Istituto di Fisica Cosmica, CNR, Milano, Italy
- Laboratorio del Sud, Catania, Italy
- Dipartimento di Fisica dell'Universita' degli Studi and Istituto Nazionale di Fisica Nucleare, Milano, Italy
- Laboratorio del Sud and Dipartimento di Fisica dell'Universita', Catania, Italy
- Gruppo Collegato di Messina, I.N.F.N.,Catania, Italy
- Dipartimento di Fisica dell'Universita', Catania, Italy
- Istituto Nazionale di Fisica Nucleare and Dipartimento di Fisica dell'Universita', Catania, Italy
- 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:  | Short Paper:  |
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