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Separation of Cosmic-Ray Components in Water Cherenkov Detector and use of neural networks

Separation of Cosmic-Ray Components in Water Cherenkov Detector and use of neural networks to measure m /EM. Luis Villaseñor* and H. Salazar FCFM-BUAP. 5th International Workshop on Ring Imaging Cherenkov Detectors Playa del Carmen November 30 – December 5, 2004

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Separation of Cosmic-Ray Components in Water Cherenkov Detector and use of neural networks

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  1. Separation of Cosmic-Ray Components in Water Cherenkov Detector and use of neural networks to measure m/EM Luis Villaseñor* and H. Salazar FCFM-BUAP 5th International Workshop on Ring Imaging Cherenkov Detectors Playa del Carmen November 30 – December 5, 2004 *On Leave of Absence from IFM-UMSNH

  2. Contents • Motivation to study m/EM separation • Experimental setup • Data • Composition of showers with known m/EM • Use of neural networks • Conclusions

  3. Nm/Ne Strongly Correlated With Primary Mass, i.e. ~2 x for Fe wrt p

  4. Use low energy data to get real m and EM traces to eliminate systematics due to detector simulation Look here To understand there

  5. Measure Charge, Amplitude,T10-50,T10-90 with good precision for three different triggers. Arbitrary muons threshold of 30mV

  6. 1.54 m diameter, 1.2 m water, one 8” PMT, tyvek 1/5 in volume of an Auger WCD

  7. LabView based DAS

  8. ~74 pe

  9. Low Charge Peak=0.12 VEM R muon = 876 Hz R EM = 80 Hz R shower (Q>7VEM) = 1 Hz Not an Artifact due to V threshold

  10. Stopping muon at 0.1 VEM Decay electron at 0.18 VEM Crossing muon at 1 VEM Alarcón M. et al., NIM A 420 [1-2], 39-47 (1999).

  11. Decay electron at 0.18 VEM Stopping muon at 0.1 VEM In this case Qpeak=0.12 VEM EM particles of ~ 10 MeV Crossing muon at 1 VEM

  12. With PMT Glass Cherenkov signal

  13. No PMT Glass Cherenkov signal

  14. With PMT Glass Cherenkov signal

  15. No PMT Glass Cherenkov signal

  16. Separation of individual Muons and EM particles is Easy for low energy Calibration events

  17. Stopping muon or electron Q~0.12 VEM (9 pe) T12~3ns Isolated Muon Q~1 VEM (74 pe) T12~12 ns Shower Q>7 VEM (500 pe) T12>15ns

  18. Data trace Q=7.8 VEM 8 muons 15 ns 4 muons, 15ns 33 “electrons” 25 ns 66 “electrons” 25 ns

  19. Parameters for Data and Composed Events

  20. Training and Clasification Results for a Kohonen Neural Network 4 features as input (Charge, Amplitude, T10-50, T1090) 8 Neurons in first layer 4 in second layer 2 or 3 classes as output (8m, 4m + 33e, 66e)

  21. Training and Clasification Results for Two Classes Class

  22. Training and Clasification Results for Two Classes Class

  23. Training and Clasification Results for Three Classes Class

  24. Conclusions Clear separation of muons, electrons, PMT interactions and showers in a single WCD Rise time 10-50% is linear with Q/V Neural Networks classify composed events of muons and electrons better than randomly Shower data is dominated by muons To do: Apply to Auger with 25 ns sampling time.

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