Status of the AGATA PSA

1
Status of the AGATA PSA

• For the PSA team, P. Désesquelles (IPN Orsay)

desesque_at_ipno.in2p3.fr
2
PSA formalization (1)
One segment
One  Meta-signal  hit segment4(or 8)
neighbors
0 0 E1 0 0 E2 0
S1
Energy deposit in a voxel
MGS


T
S

• X

T-1 ?

3
PSA formalization (2)
1 0 0
about 50 voxels/segment

X
Each column MGS signal
10 ns bins
S1
T X S
S1
T
4
Tasks

• Number of hits
• Folding algo. (Milano/Munchen) not adapted.
• Smoothing/derivation (Orsay) not adapted.
• Derivation/data base (Milano) gt65 (? PSA
  meeting).
• Acclivity (Darmstadt) in progress.
• Neural networks (Orsay) in progress.
• Discriminant Analysis (Strasbourg/Orsay) next.

5
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 (OrsaySaclay) in
  progress (? PSA meeting).
• Matrix Inversion (OrsayStrasbourg) in progress
  (? PSA meeting).

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Thus

• Difficulties with A.I. methods.
• Exp. info. must be used in an optimum way.
• Math. before algo.

7
Difficulties (1)
Sensitivity How much S is changed for a given
X shift
shift
shift
? very large sensitivity range ? very low
sensitivity zones
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Difficulties (2)
ill conditioned transform
? signals mainly sensitive to c.m. of energy
deposits
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Difficulties (3)
Treat the realistic case

• 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

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Grid to choice
advantages drawbacks
r,q cst. values of t10-90 cylindrical not homogenous
vr,q cst. values of t10-90 homogen., cylindr. not the same x/y accuracy
x,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)
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A grid adapted to the sensitivity
c2 between grid points gt c2 min
? Condition number divided by 4 to 10
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Sampling time
One hit in each of two neighboring segments
Very preliminary
? resolution is not worsen up to 150 ns bins !
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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)

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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,q
• Juge an algo on realistic case
• We should include numerical analysis specialists
  in our group

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Thank you