Separation of Cosmic-Ray Components in Water Cherenkov Detector and use of neural networks
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Separation of Cosmic-Ray Components in Water Cherenkov Detector and use of neural networks to measure /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
description
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 - PowerPoint PPT Presentation
Transcript of 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.
