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0 - Electron Discrimination in Liquid Argon Time Projection Chamber.

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Presentation on theme: "0 - Electron Discrimination in Liquid Argon Time Projection Chamber."— Presentation transcript:

1 0 - Electron Discrimination in Liquid Argon Time Projection Chamber

2 Liquid Argon TPC in T2K disappearance, e appearance measurements 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 T2K experiment 1GeV accelerator neutrino beam from J-PARC 22.5 ktons water Cherenkov far detector (295 km from J-PARC) Tomasz Wąchała, Epiphany, Cracow 2006

3 MC Data Important reactions e + ne - + p + n X e CC reaction: ( e appearance signal) NC reaction: (background for e appearance signal) electromagnetic shower Decay: 0 electromagnetic shower e-e- 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 Input layer Hidden layer Output layer Tomasz Wąchała, Epiphany, Cracow 2006

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

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

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

8 Using information Number of events dE/dx [MeV/cm] Wire3Wire4 Wire5 Wire6 Purity [%] Efficiency [%] Best quality for N=3 Tomasz Wąchała, Epiphany, Cracow 2006

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

10 Results using network Number of events Network output Tomasz Wąchała, Epiphany, Cracow 2006

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

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

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

14 Results using network Number of events Network output Tomasz Wąchała, Epiphany, Cracow 2006 Event topology

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

16 Primary vertex information 7 parameters + x ion Purity [%] Efficiency [%] 7 parameters ( + event topology) Tomasz Wąchała, Epiphany, Cracow 2006

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

18 Results using network Network output Number of events Tomasz Wąchała, Epiphany, Cracow 2006 Event topology x ion

19 Purity [%] Efficiency [%] Summary Only + topology + x ion 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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