kalanand mishra kaon neural net 1 retraining kaon neural net kalanand mishra university of...
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Kalanand Mishra Kaon Neural Net 1
Retraining Kaon Neural NetRetraining Kaon Neural Net
Kalanand MishraUniversity of Cincinnati
Kalanand Mishra Kaon Neural Net 2
• This exercise is aimed at improving the performance of KNN selectors.
• Kaon PID control samples are obtained from D* decay: D*+D0 [K-+] s+
Track selection and cuts used to obtain the control sample is described in
detail in BAD 1056 ( author : Sheila Mclachlin ).
• The original Kaon neural net (KNN) training was done by Giampiero Mancinelli & Stephen Sekula in circa 2000, analysis 3, using MC events ( they didn’t use PID control sample). They used 4 neural net input variables: likelihoods from SVT, DCH, DRC (global) and K momentum.
• I intend to use two additional input variables: track based DRC likelihood and polar angle () of kaon track.
• I have started the training with PID control sample (Run 4). I will repeat the same exercise for MC sample and also truth-matched MC events.
• Due to higher statistics and better resolution in the control sample available now, I started with a purer sample ( by applying tighter cuts).
• Many thanks to Kevin Flood and Giampiero Mancinelli for helping me getting started and explaining the steps involved.
MotivationMotivation
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KK--ππ++ invariant mass in control sample invariant mass in control sample
No P* cut Purity within 1 = 96 %
P* > 1.5 GeV/cPurity within 1 = 97 %
Conclusion : P* cut improves signal purity. We will go ahead with this cut.Other cuts: K-π+ vertex prob > 0.01 and require DIRC acceptance.
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| m | m D*D* - m - mDD0 0 | distribution in control sample | distribution in control sample
Conclusion : P* cut doesn’t affect ∆m resolution.
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Momentum and cosMomentum and cos didistributions stributions
Kaon P
Pion P
Kaon coscos
Pion coscos
Very similar distributions for K and π
Almost identical dist. for K and π
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PPlablab vs cos vs cos di distributionstribution
Kaon Pion
Conclusion : Almost identical distributions for Kaon and Pion except on the verticalleft edge where soft pions make slightly fuzzy boundary.
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Purity as a function of Kaon momentumPurity as a function of Kaon momentum
Purity = 93 % Purity = 97 % Purity = 98 %
Purity = 98 % Purity = 98 % Purity = 98 %
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NN input variablesNN input variables
Pscaledscaled
scaledscaled
scaled scaled not a input var
SVT lh
Inputs vars are: P, , svt-lh, dch-lh, glb-lh, trk-lh.
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NN input variables NN input variables
DCH lh DRC-glb lh
DRC-trk lh
Inputs vars are: P, , svt-lh, dch-lh, glb-lh, trk-lh.
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NN output at optimal point NN output at optimal point A sample of 120,000 events with inputs : svt-lh, dch-lh, glb-lh, trk-lh, P and
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Signal performanceSignal performanceA sample of 120,000 events with inputs : svt-lh, dch-lh, glb-lh, trk-lh, P and
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Background performanceBackground performanceA sample of 120,000 events with inputs : svt-lh, dch-lh, glb-lh, trk-lh, P and
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Performance vs number of hidden nodesPerformance vs number of hidden nodesA sample of 120,000 events with inputs : svt-lh, dch-lh, glb-lh, trk-lh, P and
Saturates ataround 18
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I have set up the machinery and started training K neural net.One way to proceed is to include P and as input variables after flattening the sample in P - plane ( to get rid of the in-built kinematic bias spread across this plane).The other way is to do training in bins of P and cos. This approach seems more robust but comes at the cost of more overheads and requires more time and effort. Also, this approach may or may not have performance advantage over the first approach.By analyzing the performance of neural net over a sample using both of these approaches, we will decide which way to go.The performance of the neural net will be analyzed in terms of kaon efficiency vs. pion rejection [ and also kaon eff vs. pion rej as a function of both momentum and ].Stay tuned !
SummarySummary