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 /EM

Luis Villaseñor* and H. SalazarFCFM-BUAP

5th International Workshop on Ring Imaging Cherenkov DetectorsPlaya del CarmenNovember 30 – December 5, 2004

*On Leave of Absence from IFM-UMSNH

Contents

Motivation to study /EM separationExperimental setup DataComposition of showers with known Use of neural networksConclusions

N/NeStronglyCorrelatedWith PrimaryMass, i.e.~2 x for Fe wrt p

Use low energy data to get real and EM traces to eliminate systematics due to detector simulationLook

here

To understandthere

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

1.54 m diameter, 1.2 m water, one 8” PMT, tyvek

1/5 in volume of an Auger WCD

LabView basedDAS

~74 pe

R muon = 876 HzR EM = 80 HzR shower (Q>7VEM) = 1 Hz

Low Charge Peak=0.12 VEM

Not anArtifact due to V threshold

Stopping muonat 0.1 VEM

Decay electronat 0.18 VEM

Crossing muonat 1 VEM

Alarcón M. et al., NIM A 420 [1-2], 39-47 (1999).

Stopping muonat 0.1 VEM

Decay electronat 0.18 VEM

Crossing muonat 1 VEM

In this case Qpeak=0.12 VEM

EM particlesof ~ 10 MeV

With PMTGlassCherenkovsignal

No PMTGlassCherenkovsignal

With PMTGlassCherenkovsignal

No PMTGlassCherenkovsignal

Separationof individualMuons andEM particles isEasy for low energyCalibration events

Stopping muonor electronQ~0.12 VEM(9 pe)T12~3ns

Isolated MuonQ~1 VEM(74 pe)T12~12 ns

Shower Q>7 VEM(500 pe)T12>15ns

Data traceQ=7.8 VEM

8 muons15 ns

4 muons, 15ns33 “electrons”25 ns

66 “electrons”25 ns

Parameters for Data and Composed Events

Data 8 e

4 33 e

0 66 e

Charge (VEM)

7.9+-0.5

8.0+-0.55

7.9+-0.51

8.34+-0.4

Amplitude (V)

1.16+-0.08

1.20+-0.20

1.25+-0.20

1.34+-0.19

T10-50

(ns)

16.7+-0.9

17.5+-3.0

18.25+-3.6

18.45+-2.9

T10-90

(ns)

50.8+-2.0

50.0+-4.3

52.4+-6.6

54.2+-6.9

Training and Clasification Results for a Kohonen Neural Network

4 features as input

(Charge, Amplitude, T10-50, T1090)

8 Neurons in first layer4 in second layer

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

Training and Clasification Results for Two Classes

8 433 e

Data

8 65% 39% 68%

433 e

35% 61% 32%

Training and Clasification Results for Two Classes

8 066 e

Data

8 65% 33% 78%

066 e

35% 67% 22%

Training and Clasification Results for Three Classes 8 e

e

066 e

Data

8 56% 29% 33% 58%

e

21% 35% 27% 15%

0 66 e

23% 36% 40% 27%

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 muonsTo do: Apply to Auger with 25 ns sampling time.