22 Feb. 2005AGATA week in Darmstadt1 Status of the AGATA PSA For the PSA team, P. Désesquelles (IPN Orsay)

22 Feb. 05AGATA week in Darmstadt2 PSA formalization (1) X = 00E100E2000E100E20 T T -1 ? S = … … … S1S1 … One segmentOne « Meta-signal » : hit segment+4(or 8) neighbors Energy deposit in a voxel MGS

22 Feb. 05AGATA week in Darmstadt3 PSA formalization (2) T X = S : X S1S1 T S1S1 … about 50 voxels/segment Each column = MGS signal 10 ns bins

22 Feb. 05AGATA week in Darmstadt4 Tasks  Number of hits  Folding algo. (Milano/Munchen) not adapted.  Smoothing/ derivation (Orsay) not adapted.  Derivation/data base (Milano) >65% (→ PSA meeting).  Acclivity (Darmstadt) in progress.  Neural networks (Orsay) in progress.  Discriminant Analysis (Strasbourg/Orsay) next.

22 Feb. 05AGATA week in Darmstadt5 Tasks  Location and energy  Neural networks (Orsay/Munchen) not adapted.  Multivariate Analysis (Strasbourg) not adapted.  Genetic algo. (Legnaro/ Darmstadt) too slow → coupled with grid search (→ PSA meeting).  Wavelets (Darmstadt) in progress (→ PSA meeting).  Wavelets + grid descent (Orsay+Saclay) in progress (→ PSA meeting).  Matrix Inversion (Orsay+Strasbourg) in progress (→ PSA meeting).

22 Feb. 05AGATA week in Darmstadt6 Thus… Difficulties with A.I. methods. Exp. info. must be used in an optimum way. Math. before algo.

22 Feb. 05AGATA week in Darmstadt7 Difficulties (1) “Sensitivity” = How much S is changed for a given X shift shift  very large sensitivity range  very low sensitivity zones

22 Feb. 05AGATA week in Darmstadt8 Difficulties (2)  signals mainly sensitive to c.m. of energy deposits G 23 ill conditioned transform

22 Feb. 05AGATA week in Darmstadt9 Difficulties (3)  Multi hits  True noise  The signal does not belong to the base  distance between the hits  relative energies  neighbor segments  whole detector  number of hits unknown  sampling rate  time Treat the realistic case :

22 Feb. 05AGATA week in Darmstadt10 Grid to choice advantagesdrawbacks r,r, cst. values of t … cylindrical not homogenous √r,√r, cst. values of t … homogen., cylindr. not the same x/y accuracy x,y,zx,y,z homogenous simple not cylindrical large distances to grid hexagon, z cylindrical compact not compact in z not homogenous hexagonal compact cylindrical maximum compacity less “standard” Adaptated grid optimum conditioning of the problem not homogenous (we work with the last one)

22 Feb. 05AGATA week in Darmstadt11 A grid adapted to the sensitivity  2 between grid points >  2 min  Condition number divided by 4 to 10

22 Feb. 05AGATA week in Darmstadt12 Sampling time One hit in each of two neighboring segments  resolution is not worsen up to 150 ns bins !

22 Feb. 05AGATA week in Darmstadt13 Performances for one segment  Location :  0.3 mm ! (1 hit)  2 mm (simple multi-hit)  Energy :  1% (1 hit)  some % (simple multi-hit)  Time :  ~ ms (1 hit)  0.1 s (simple multi-hit on 2.4 GHz Matlab)

22 Feb. 05AGATA week in Darmstadt14 Conclusions The single-isolated hit PSA is solved → neural networks The front-end can include :  Signals preprocessing  Single-isolated hit PSA  Tagging of events → which algo to use The multi-hit PSA is difficult !  The X → S transform is not well conditioned  Large sensitivity range  Multi hits at the same r,   Juge an algo on realistic case  We should include numerical analysis specialists in our group

22 Feb. 05AGATA week in Darmstadt15 Thank you