# 2009.03.02 Application of the neural network for the cut optimization (zero try)

### Multilayer perceptron (feedforward neural networks)

Multilayer perceptron (MLP) is feedforward neural networks

trained with the standard backpropagation algorithm.

They are supervised networks so they require a desired response to be trained.

They learn how to transform input data into a desired response,

so they are widely used for pattern classification.

With one or two hidden layers, they can approximate virtually any input-output map.

They have been shown to approximate the performance of optimal statistical classifiers in difficult problems.

### ROOT implementation for Multilayer perceptron

TMultiLayerPerceptron class in ROOT

mlpHiggs.C example

### Application for cuts optimization in the gamma-jet analysis

Netwrok structure:

r3x3, (pt_gamma-pt_jet)/pt_gamma, nCharge, bBtow, eTow2x1: 10 hidden layers: one output later

**Figure 1:**

- Upper left: Learning curve (error vs. number of training)

Learing method is: Steepest descent with fixed step size (batch learning)
- Upper right: Differences (how important are initial variableles for signal/background separation)
- Lower left: Network structure (ling thinkness corresponds to relative weight value)
- Lower right: Network output. Red - MC gamma-jets, blue QCD background, black pp2006 data

**Figure 2:** Input parameters vs. network output

Row: 1: MC QCD, 2: gamma-jet, 3 pp2006 data

Vertical axis: r3x3, (pt_gamma-pt_jet)/pt_gamma, nCharge, bBtow, eTow2x1

Horisontal axis: network output

**Figure 3:** Same as Fig. 2 on a linear scale