Weekly Update 5Neural Network Application to the Matrix Element Method

Cadin Connor

April 8, 2019 Weekly Update 5 1

Recap

• Attempting network training with simple top-quark productiong g → t t̄

• Scaling corrections and general debugging

• Miscellaneous**

April 8, 2019 Weekly Update 5 2

tpT = 108.81+84.2755.69

8

4

0

4

8

t eta

teta = 0.00+1.921.92

1.5

3.0

4.5

6.0

t phi

tphi = 3.14+2.142.14

250

500

750

1000

tbar

pT

tbarpT = 108.81+84.2755.69

6

3

0

3

6

tbar

eta

tbareta = 0.01+1.911.92

250

500

750

1000

tpT

1.5

3.0

4.5

6.0

tbar

phi

8 4 0 4 8

teta

1.5 3.0 4.5 6.0

tphi

250

500

750

1000

tbarpT

6 3 0 3 6

tbareta

1.5 3.0 4.5 6.0

tbarphi

tbarphi = 3.14+2.122.13

Figure: CaptionApril 8, 2019 Weekly Update 5 3

April 8, 2019 Weekly Update 5 4

April 8, 2019 Weekly Update 5 5

Sanity checks: produced from within network code

April 8, 2019 Weekly Update 5 6

X-section output shows reasonable agreement

Vectorized function

1D array for each event, processed iteratively

Road ahead

• Resolve vectorization issue, train network for g g → t t̄

• Extend tt̄ case to include decays → tt̄ → W+bb̄W−; then tott̄ → W+bb̄W− → l+νlbb̄qq̄

• Increase rigor of training, starting with baseline parameters set bylast year’s results

• Generalize hdf5 converter for ease of future use

April 8, 2019 Weekly Update 5 7

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