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. Look here To understand there 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

8. LabView based
DAS

9. ~74 pe

10. R shower (Q>7VEM) = 1 Hz
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

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

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

13. With PMT
Glass
Cherenkov
signal

14. No PMT
Glass
Cherenkov
signal

16. With PMT
Glass
Cherenkov
signal

17. No PMT
Glass
Cherenkov
signal

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

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

23. 4 muons, 15ns Data trace Q=7.8 VEM 8 muons 15 ns 33 “electrons” 25 ns

24. Parameters for Data and Composed Events
Charge (VEM)
Amplitude (V)
T10-50
(ns)
T10-90

25. 2 or 3 classes as output (8m, 4m + 33e, 66e)
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)

26. Training and Clasification Results for Two Classes
8 m 4m
33 e
Data
65%
39%
68%
35%
61%
32%
Class

27. Training and Clasification Results for Two Classes
8 m 0m
66 e
Data
65%
33%
78%
35%
67%
22%
Class

28. Training and Clasification Results for Three Classes
8 m
0 e
4 m
33 e 0m
66 e
Data
56%
29%
33%
58%
21%
35%
27%
15%
0 m
23%
36%
40%
Class

29. 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.