Using Artificial Neural Networks
to identify
Marta Artamendi Tavera
University of Manchester
Neural Networks
ANN aim simulate biological NN
–
good pattern recognition
NN desirable
features: non

linear response
learning
ANN computing unit:
node
(thresholding neuron)
i
= g
ij
j
Takes values within [

1 1]
All neurons connecting to
Weights of connections
Non

linear transfer function (sigmoid function)
Artificial Neural Networks
Feed Forward architecture
FF use supervised training
change weights of connections
Learning:
Back

propagation algorithm
Minimise summed square error function wrt weights
t is the target output
Gradient descent method:
Initially random weights
updated
in proportion to:
and
NN Program
Target output
: function of mass
Jetnet 3.4: L.Lonhblad, C.Peterson, H.Pi, T.Rognvaldsson
Training
: Aim separate signal(
0
) and background
1 output node: target output 1, 0
NO training sample.
Train with data
m
0
: 0.135 GeV
: EM resolution 0.007 GeV
m: reconstructed invariant mass
NN Performance (FoM)
Test NN ability to separate signal and backgd
Fit output distribution to combination of the two
Signal : mass interval 012

0.15
Define Figure of Merit (
FoM
):
b
i
fraction backgd events in bin i
s
i
fraction signal events in bin i
frac signal events in distribution
frac backgd events in distribution
Figure of Merit
FoM depends upon:
NN configuration
No. times is exposed to
training sample (no. cycles)
Size of the training and
testing sample
NN Configuration
Input: events with two clusters.
E: energy
Lat: lateral shower shape
Z42: Absolute value complex
Zernike moment (4,2)
Z20: Zernike moment(2,0)
S1s9: ratio energy of 9 closer crystals
to central one
S9s25: ratio 25 surrounding crystals
s2TP: second moment in theta

phi
OK: good/bad crystal
NX: number of crystals
NB: number of bumps
ET: energy of nearest charged track
4 layered NN
11 input nodes
18 first hidden layer
11 second hidden layer
1 output node
Training sample 10000
Testing sample 10000
No cycles 5000
Preliminary results
Best
FoM:
0.22
Purity and Eff of NN cut
Compare results
Summary and Conclusions
Neural network can be very sensitive to several parameters
Is possible to successfully train a NN with data
Neural Network gives better results than simple cuts
Use a Neural network to select
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