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![Page 1: 1 A neural approach to the analysis of CHIMERA experimental data CHIMERA Collaboration S.Aiello 1, M. Alderighi 2,3, A.Anzalone 4, M.Bartolucci 5, G.Cardella.](https://reader035.fdocuments.us/reader035/viewer/2022081518/5542eb4f497959361e8be576/html5/thumbnails/1.jpg)
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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
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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