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 0 - Electron Discrimination in Liquid Argon Time Projection Chamber

 0 - Electron Discrimination in Liquid Argon Time Projection Chamber. Liquid Argon TPC in T2K. T2K experiment. Liquid Argon TPC 100 tons of liquid argon in the intermediate detector (2km) Measure the parameters of low momentum particles (below Cherenkov threshold)

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 0 - Electron Discrimination in Liquid Argon Time Projection Chamber

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  1. 0 - Electron Discrimination in Liquid Argon Time Projection Chamber

  2. Liquid Argon TPC in T2K T2K experiment • Liquid Argon TPC • 100 tons of liquid argon in the • intermediate detector (2km) • Measure the parameters of • low momentum particles • (below Cherenkov threshold) • Background from NC interactions • and e contamination measurements • 1GeV accelerator neutrino beam • from J-PARC • 22.5 ktons water Cherenkov far • detector (295 km from J-PARC) •  disappearance, e appearance • measurements Tomasz Wąchała, Epiphany, Cracow 2006

  3. e + n e- + p MC Data  + n  + 0 + X  Decay: 0 MC Data Important reactions • e CC reaction: • (e appearance signal) e- electromagnetic shower •  NC reaction: • (background for e appearance signal)  electromagnetic shower Tomasz Wąchała, Epiphany, Cracow 2006

  4. Neural network as a classificator • Simple Multilayer Perceptron network used: • Input layer, 1 hidden layer and output layer • Learning with a supervisor on the MC events Hidden layer Input layer Output layer Tomasz Wąchała, Epiphany, Cracow 2006

  5. Signal (electrons) Neural Net Number of events Electron Background (0s) Network output OUTPUT INPUT Neural network as a classificator Tomasz Wąchała, Epiphany, Cracow 2006

  6. Purity [%] Efficiency [%] Classification quality Nsig/bg - number of the signal/background events above the threshold Number of events 500 Better quality of classification 0 Network output Tomasz Wąchała, Epiphany, Cracow 2006

  7. Wire planes Electron / 0 Events geometry • Monoenergetic (1GeV) • Monte-Carlo events • Without noise Tomasz Wąchała, Epiphany, Cracow 2006

  8. Number of events Using <dE/dx> information Wire3 Wire4 Best quality for N=3 Purity [%] dE/dx [MeV/cm] Number of events Wire5 Wire6 dE/dx [MeV/cm] Efficiency [%] Tomasz Wąchała, Epiphany, Cracow 2006

  9. Purity [%] Efficiency [%] Information from the second wire plane Number of events Induction plane Collection plane Improved quality <signal> [ADC] <dE/dx> [MeV/cm] Tomasz Wąchała, Epiphany, Cracow 2006

  10. Results using 2-2-1 network Number of events <dE/dx> <signal> Network output Tomasz Wąchała, Epiphany, Cracow 2006

  11. Using event topology information Electron/Pi0 Number of events Tomasz Wąchała, Epiphany, Cracow 2006

  12. Using event topology information Number of events • Average width of event • Length of the track • with the largest number • of hits • Total number of hits Number of events Number of events Tomasz Wąchała, Epiphany, Cracow 2006

  13. Adding new parameters Purity [%] Best quality for 7 parameters Efficiency [%] Tomasz Wąchała, Epiphany, Cracow 2006

  14. Number of events Network output Results using 7-2-1 network Event topology <dE/dx> <signal> Tomasz Wąchała, Epiphany, Cracow 2006

  15. Primary vertex information Ionization e- Distance from the vertex to the first ionization signal xion  e-  e+ 0 e-  Number of events Ionization Tomasz Wąchała, Epiphany, Cracow 2006

  16. Primary vertex information Purity [%] 7 parameters + xion 7 parameters (<dE/dx> + event topology) Efficiency [%] Tomasz Wąchała, Epiphany, Cracow 2006

  17. Adding hidden neurons Purity [%] Best quality for N = 3 hidden neurons Efficiency [%] Tomasz Wąchała, Epiphany, Cracow 2006

  18. Results using 8-3-1 network xion Number of events Event topology <dE/dx> <signal> Network output Tomasz Wąchała, Epiphany, Cracow 2006

  19. Summary Purity [%] <dE/dx> + topology + xion <dE/dx> + topology Only <dE/dx> Efficiency [%] Tomasz Wąchała, Epiphany, Cracow 2006

  20. Future plans • Adding noise to the events - how does it affect the results? • Influence of additional particles • Applying this analysis to the ICARUS TPC (requires extra • work on software) • Testing algorithms on the real data in the ICARUS T600 • liquid argon TPC detector Tomasz Wąchała, Epiphany, Cracow 2006

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