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A neural approach to the analysis of CHIMERA experimental data

CHIMERA Collaboration

S.Aiello1, M. Alderighi2,3, A.Anzalone4, M.Bartolucci5, G.Cardella1, S.Cavallaro4,7, M. D’Agostino6 ,E.DeFilippo1, E.Geraci4, M.Geraci1, F.Giustolisi4,7, P.Guazzoni3,5, M.Iacono Manno4,

G.Lanzalone1,7, G.Lanzanò1, S.LoNigro1,7, G.Manfredi5, A.Pagano1, M.Papa1, S.Pirrone1, G.Politi1,7, F.Porto4,7, S.Russo5, S.Sambataro1,7, G.Sechi2,3, L.Sperduto4,7, C.Sutera1, L.Zetta3,5

1Istituto Nazionale di Fisica Nucleare, sez di Catania, Catania, Italy

2Istituto di Fisica Cosmica, CNR, Milano, Italy

3Istituto di Fisica Nucleare, sez. di Milano, Milano, Italy

4 Istituto di Fisica Nucleare, Laboratorio Nazionale del Sud, Catania, Italy

5Dipartimento di Fisica dell’Universita’, Milano, Italy

6Dipartimento di Fisica dell'Universita’ degli Studi and Istituto di Fisica Nucleare, sez. di Bologna, Bologna,Italy

7Dipartimento di Fisica dell'Universita’, Catania, Italy

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Outline

• Detector characteristics• Automatic data analysis• Proposed approaches• Our neural approach• System overview• Results

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CHIMERA (Charged Heavy Ion Mass and Energy Resolving Array)

1192 Si-CsI(TI) detection cells

9 wheels

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Preamplifier

Photodiode

Silicon

detector

CsI(TI)detector

TO

F

E

Fast Slow

Fast

Slow

Fast

E

- S

i

Detection cell

5

58 Ni + 27 Al Einc = 30 AMev

Scatter plot from CHIMERA

• sparse data

• low S/N

• density variation– high frequency: noise

– characteristic frequency: ridges/valleys

– low frequency: background

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E-Si

Fast-CsI(TI)

“banana” extraction

?

E-Si

Counts

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Fast-CsI(TI)

E-Si

Fast-CsI(TI)

E-Si

E-Si

Counts

1-D frequency distribution

Z-lines

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Proposed approach

• FFT not satisfactory results

• filtering edge detection = ill-posed problem

• contextual image segmentation [Benkirane et al. ‘95]: Canny filtering + a priori information not easily applicable

• interactive technique unpractical for a lot of spectra

yet, density modulation can be easily perceived by sight

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Our solution

Using emergent perception mechanisms of biological visual systems

Grossberg’s neural networks

• mathematically defined

• extract information from the global structure of data (rather local relationship)

• no training

• successfully applied to SAR and satellite images (noisy and incomplete)

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Implementation

• 2 levels of neural networks for cluster determination

• Procedural algorithms for frequency distribution construction

• Matlab (PC Pentium II, 400MHz)• 500 500 pixel processing windows

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Neural system

Window

ADD net

BF netLevel 2:oriented completion (Bipole Filter)

Level 1: AdaptiveDensity Discrimination

Input

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Level 1: ADD net• on-center off-surround shunting network

• density information processing– comparison between on-center and off-surround areas

– low-pass filtering of the spatial frequencies in the input windowsensitivity to ridge-valley modulation

• clusters as incomplete and irregular strips

Input

CENTER

SURROUND

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ADD net

input window

on-center convolution

off-surround convolution

inhibitory input

excitatory input

+

-

i

i

i

j

j

j

xij

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Level 2: BF nets

• additive networks• long-term cooperation along selected directions

– bipole filters

– different filtering masks according to hyperbolic trends of data

• clusters as complete strips

105° 135°

Ex.

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Example 1

valley clusters

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ridge clusters

Example 2

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Conclusions

• Grossberg’s approach is good for automatic determination of “bananas”

• Density processing is– dependent on the image structure only

– independent from the underlying physics

• Intensive computation (500 500 neurons)• Processing whole matrices and improving

algorithm efficiency as future works