weekly update 5 - indico › ... › atlas_weekly_update_5edit.pdfrecap attempting network training...
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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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