Antony Carver

231 Pages·2010·5.21 MB·English

Electron identification in and performance
of the ND280 Electromagnetic Calorimeter
by
Antony Carver
Thesis
Submitted to The University of Warwick
for the degree of
Doctor of Philosophy
Physics
March 2010
Contents
Acknowledgments vii
Declarations viii
Abstract ix
Abbreviations x
List of Figures i
List of Tables xiv
Chapter 1 Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2 Neutrino Physics 3
2.1 Neutrino Phenomenology . . . . . . . . . . . . . . . . . . . . . 3
2.1.1 Neutrino Mass . . . . . . . . . . . . . . . . . . . . . . . 3
2.1.2 Oscillation Probability . . . . . . . . . . . . . . . . . . 5
2.1.3 Neutrino Oscillations in Matter . . . . . . . . . . . . . 8
ii
2.1.4 Current 3 flavour neutrino oscillation model . . . . . . 9
2.2 Neutrino Interaction Physics . . . . . . . . . . . . . . . . . . . 12
2.2.1 Charged Current Interactions . . . . . . . . . . . . . . 13
2.2.2 Neutral Current Interactions . . . . . . . . . . . . . . . 16
2.3 A review of Neutrino Oscillations . . . . . . . . . . . . . . . . 17
2.3.1 The Solar Neutrino Problem . . . . . . . . . . . . . . . 18
2.3.2 Neutrino Oscillation Experiments . . . . . . . . . . . . 25
2.4 T2K . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Chapter 3 T2K and the ND280 Detector 33
3.1 Introduction to T2K . . . . . . . . . . . . . . . . . . . . . . . 33
3.2 J-PARC Neutrino Beamline . . . . . . . . . . . . . . . . . . . 36
3.3 INGRID on-axis detector . . . . . . . . . . . . . . . . . . . . . 37
3.4 Super-Kamiokande . . . . . . . . . . . . . . . . . . . . . . . . 38
3.4.1 Super-Kamiokande Reconstruction . . . . . . . . . . . 40
3.5 ND280 off axis near detector . . . . . . . . . . . . . . . . . . . 42
3.5.1 P0D (π0 Detector) . . . . . . . . . . . . . . . . . . . . 44
3.5.2 Fine Grained Detector . . . . . . . . . . . . . . . . . . 46
3.5.3 Time Projection Chamber (TPC) . . . . . . . . . . . . 48
3.5.4 Electromagnetic Calorimeter . . . . . . . . . . . . . . . 49
3.5.5 Side Muon Range Detector . . . . . . . . . . . . . . . . 52
3.5.6 Scintillator Detectors . . . . . . . . . . . . . . . . . . . 54
3.5.7 Multi Pixel Photon Counters . . . . . . . . . . . . . . 55
iii
3.5.8 ND280 Electronics . . . . . . . . . . . . . . . . . . . . 57
3.6 Data Acquisition (DAQ) . . . . . . . . . . . . . . . . . . . . . 61
3.7 ND280 Software Suite . . . . . . . . . . . . . . . . . . . . . . 61
3.7.1 oaEvent, oaRawEvent and oaUnpack . . . . . . . . . . 62
3.7.2 Monte Carlo simulation . . . . . . . . . . . . . . . . . 63
3.7.3 Reconstruction . . . . . . . . . . . . . . . . . . . . . . 65
Chapter 4 Particle Identification in the ECal 70
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.2 Monte Carlo simulation and Particle event types in the ECal . 71
4.2.1 Monte Carlo simulation of ECal . . . . . . . . . . . . . 71
4.2.2 Tracks . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2.3 Electromagnetic Showers . . . . . . . . . . . . . . . . . 75
4.2.4 Hadronic Showers . . . . . . . . . . . . . . . . . . . . . 76
4.3 Identification Techniques . . . . . . . . . . . . . . . . . . . . . 79
4.3.1 Likelihood . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.3.2 Artificial Neural Networks . . . . . . . . . . . . . . . . 82
4.4 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.4.1 Track Reconstruction . . . . . . . . . . . . . . . . . . . 88
4.4.2 Shower Reconstruction . . . . . . . . . . . . . . . . . . 88
4.4.3 Angle Reconstruction . . . . . . . . . . . . . . . . . . . 89
4.5 ECal Particle Identification . . . . . . . . . . . . . . . . . . . 89
4.5.1 Particle Identification Variables . . . . . . . . . . . . . 89
iv
4.6 Particle Identification Technique . . . . . . . . . . . . . . . . . 104
4.6.1 PID algorithm description . . . . . . . . . . . . . . . . 104
4.6.2 Network Training . . . . . . . . . . . . . . . . . . . . . 106
4.6.3 Network Optimisation . . . . . . . . . . . . . . . . . . 106
4.7 Neural Network Performance . . . . . . . . . . . . . . . . . . . 109
4.7.1 Neural Network Validation . . . . . . . . . . . . . . . . 109
4.7.2 Predicted Efficiency . . . . . . . . . . . . . . . . . . . . 111
4.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Chapter 5 T9 Testbeam 118
5.1 ECal Testbeam Introduction . . . . . . . . . . . . . . . . . . . 118
5.2 CERN T9 Beamline and ECal experimental layout . . . . . . 119
5.2.1 T9 beamline . . . . . . . . . . . . . . . . . . . . . . . . 119
5.3 Testbeam trigger and particle identification . . . . . . . . . . . 121
5.3.1 Time of Flight . . . . . . . . . . . . . . . . . . . . . . . 122
ˇ
5.3.2 Cerenkov counters . . . . . . . . . . . . . . . . . . . . 130
5.3.3 Determination of beam composition . . . . . . . . . . . 135
5.3.4 Sample selection . . . . . . . . . . . . . . . . . . . . . 137
5.4 Analysis of testbeam data . . . . . . . . . . . . . . . . . . . . 138
5.4.1 Data Calibration . . . . . . . . . . . . . . . . . . . . . 139
5.4.2 Cosmic Muon Calibration . . . . . . . . . . . . . . . . 141
5.4.3 Comparison of Cosmic Data to Monte Carlo . . . . . . 152
5.4.4 Comparison of electron data with simulation . . . . . . 156
v
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Chapter 6 Electron Neutrino Analysis 170
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
6.2 Neutrino Interactions in ND280 . . . . . . . . . . . . . . . . . 170
6.2.1 Neutrino interactions in the FGD . . . . . . . . . . . . 174
6.3 Electron Neutrino Analysis - Event Selection . . . . . . . . . . 176
6.3.1 Lepton Selection . . . . . . . . . . . . . . . . . . . . . 177
6.3.2 Particle Identification . . . . . . . . . . . . . . . . . . . 178
6.4 Analysis Performance . . . . . . . . . . . . . . . . . . . . . . . 186
6.4.1 Systematic Errors . . . . . . . . . . . . . . . . . . . . . 192
6.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
Chapter 7 Conclusions 196
vi
Acknowledgments
Thank you to Steve Boyd and Gary Barker for all their help over the past 31
2
years. Your wisdom and inspiration has been invaluable during my time on
T2K. I would also like to thank everyone in T2K for the help and advice I
have received over the years. In particular from the ECal software group and
the electron neutrino analysis group.
I would also like to thank those I have worked with at Warwick; Martin, Phil,
Leigh and Andy.
Thank you to my Mum, Dad and Grandma for all your support over the years.
Finally, thank you to my wonderful fianc´ee, Alice, for all your understanding
over the past few months.
vii
Declarations
This work has been carried out as part of the T2K neutrino oscillation ex-
periment. The first chapter is a review of the theory and current status of
neutrino oscillation experiments and the second chapter describes the T2K
experiment. The following three chapters describe the author’s contribution
to the experiment. The third chapter describes techniques used to separate
classes of event and then the algorithm designed and implemented by the au-
thor. The development of the trigger and identification algorithms used in the
testbeam analysis were also original work, as was the data to simulation com-
parison and energy resolution measurement carried out. The electron neutrino
analysis presented in the final chapter was also implemented by the author as
part of the T2K electron neutrino analysis group.
viii
Abstract
T2K is an off axis neutrino beam experiment with a baseline of 295 km to
the far detector, Super-Kamiokande. The near detector, ND280, measures
the flux and energy spectra of electron and muon neutrinos in the direction
of Super-Kamiokande. An electromagnetic calorimeter constructed from lead
and scintillator surrounds the inner detector. Three time projection chambers
and two fine grained scintillator detectors sit inside the calorimeter. This
thesis describes the development of a particle identification algorithm for the
calorimeterandstudieshowitcanenhanceasimpleelectronneutrinoanalysis.
Aparticleidentificationalgorithmwaswrittenfortheelectromagneticcalorime-
ter to separate minimally ionising particles, electromagnetic and hadronic
showers. A Monte Carlo study suggested that the algorithm produced an
electron sample with a relative muon contamination of 10−2 whilst maintain-
ing an electron efficiency of 80%. Data collected at CERN was then used
to make comparisons between the Monte Carlo simulation used to train the
particle identification, and experimental data. A reasonable agreement was
found between the electron data and the Monte Carlo simulation, given that
the available calibration framework was still preliminary. Cosmic data agreed
well with simulation. The energy resolution of the DsECal for electromagnetic
showers was estimated at 9%/√E. An electron neutrino analysis was devel-
oped that could be performed on T2K data from the first day of data taking.
This analysis anticipated finding 33 10(sys) 6(stat) CCQE electron neu-
± ±
trino events and 92 28(sys) 10(stat) CCnQE electron neutrino events in
± ±
the FGD after 12 months of nominal running.
ix
Abbreviations
DsECal - Downstream Electromagnetic Calorimeter
FGD - Fine Grained Detector
PID - Particle Identification
SMRD - Side Muon Range Detector
P0D - Pi-0 Detector
CERN - Organisation Europ´enne pour la Recherche Nucl´eaire
TOF - Time Of Flight
T2K - Tokai to Kamioka
ND280 - Near Detector 280m
C/N C - Charged/Neutral Current
MLP - Multi Layered Perceptron
CC(n)QE - Charged Current (non) Quasi-Elastic
TPC - Time Projection Chamber
MIP - Minimally Ionising Particle
AMR - Axis Max Ratio
MPPC - Multi Pixel Photon Counter
x