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EMCal update

EMCal update. Rikard Sandström Universite de Geneve MICE PID 15/6-05. Outline. Introduction Setup Input variables Output PID – efficiency vs purity Future plans. Introduction. Using Neural Networks to separate muons from positrons. See PID phone conference May 25 for details.

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EMCal update

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  1. EMCal update Rikard Sandström Universite de Geneve MICE PID 15/6-05

  2. Outline • Introduction • Setup • Input variables • Output • PID – efficiency vs purity • Future plans

  3. Introduction • Using Neural Networks to separate muons from positrons. • See PID phone conference May 25 for details. • Removed the redundant sum of ADC count per layer, exchanged with ADC_left/ADC_right per layer instead. • The sample: • Muons same as before. • Improved description of e+ background.

  4. Beam & detector geometry • As before: • Calorimeter: KLOE-light geometry was used. • The muons were created in TURTLE, interfaced into G4MICE upstream of first tracker. • Full cooling channel and all downstream detectors are in. • New: • Electrons from real decay of muon beam. • Open the gate at 40 ns, after muons hit upstream track ref plane. • Gate open 100 ns. • Added 17 cm/ns delay due to light speed in fibers.

  5. The positron sample • No longer monochrome. • Generated by decaying “real beam”. • Look at first calorimeter hit. • If Truth = muon, tag as signal. • If not, tag as background. • A muon decaying inside EMCal counts as signal. • The 100 ns gate rejects most muon decay at rest. • Result: 124202 signal events, 571 bg events • Input purity = 99.542%.

  6. Input variables

  7. Output

  8. Optimizing purity & efficiency

  9. Comments • Contamination less than half of the 1% expected. • Relative results as good as before (after changing variables). • Heterogeneity of positrons from decay. • Before: P&E > 99.9%, now: P&E = 99.911% simultaneously (at cut = 0.65). • Still did not use • TOF • Momentum measurements • Transversal size • TDC (if any) • Low statistics! • BG efficiency could be better with more data. • Memory leak prevents me from getting larger sample… • Hacked lifetime, but ambiguities arise.

  10. Future plans • Code to build and train NN can be semi-independent part of G4MICE. • The NN is generic, can reject all sort of background it is trained on. • The trained NN is saved to file, so we can use it in G4MICE for reconstruction & analysis. • A future trigger should open the gate. • Now manually set in global time. -> Beam dependent!

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