A neural approach to the analysis of CHIMERA experimental data

1. 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

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

3. CHIMERA (Charged Heavy Ion Mass and Energy Resolving Array) 9 wheels 1192 Si-CsI(TI) detection cells

4. Photodiode Preamplifier Fast Slow CsI(TI) detector Slow D E - Si Silicon detector TOF Fast Fast E Detection cell

5. Scatter plot from CHIMERA 58 Ni + 27 Al Einc = 30 AMev • sparse data • low S/N • density variation • high frequency: noise • characteristic frequency: ridges/valleys • low frequency: background

6. “banana” extraction ? D E-Si Fast-CsI(TI) Counts D E-Si

7. 1-D frequency distribution Z-lines D E-Si Fast-CsI(TI) Counts Fast-CsI(TI) D E-Si D E-Si

8. 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

9. 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)

10. 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

11. Neural system Level 2: oriented completion (Bipole Filter) BF net Level 1: Adaptive Density Discrimination ADD net Window Input

12. Input CENTER SURROUND 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 window sensitivity to ridge-valley modulation • clusters as incomplete and irregular strips

13. on-center convolution i excitatory input + input window j xij i j inhibitory input i - j off-surround convolution ADD net

14. 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 Ex. 105° 135°

15. Example 1 valley clusters

16. Example 2 ridge clusters

17. 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