Triggering 37 2.2.10.1 Triggering Rates, Efficiencies and Problems 40 2.3 Physical Description of the Calorimeter 40 2.3.1 Introduction 2.3.2 Calorimetry Theory 2.3.3 Principles of Proportional Tube Counters 2.3.4 Construction 2.3.5 Dead Regions 2.3.6 Prototype Tests 2.3. 7 Data Read-Out 2.3.8 Data Flow 2.3.9 Calorimeter Monitoring 2.3.10 Calorimeter Performance 2.3.10.1 Cathode-Anode comparison V • 2.3.10.2 Dead channels 2.3.10.3 Monitoring performance 2.3.11 Calorimeter Calibration 2.3.11.1 Electron calibration 2.3.11.2 Bremsstrahlung Events 2.3.11.3 Muon minimum ionization 2.3.11.4 Pi zero measurements 3. The Software Chain 3.1 The Monte Carlo 79 3.1.1 The Lund Model for Event Generation 79 3.1.2 Tracking simulation -1st Stage M.C. 82 3.1.3 Detector simulation -2nd Stage M.C. 83 3.1.4 GEANT Calorimeter Simulation 83 3.2 Data Analysis 85 3.2.1 Data Split 86 3.2.2 Filtering 86 3.2.3 Pattern Recognition 87 3.2.4 Track Fitting 88 3.2.5 Vertex Reconstruction 89 3.2.6 Calorimeter Data Analysis 90 4. Resultsvi 4.1 Event Sample 4.1.1 Accuracy and Bias of Event Reconstruction 107 107 4.1.2 Event Cuts 107 4.1.3 Data Sets 113 4.1.4 Data and Monte Carlo Kinematics Distributions 114 4.2 Tracking Results 114 4.3 Monte Carlo Results 116 4.3.1 Acceptances and Efficiencies 4.3.2 Errors in Cluster and Pi Zero Reconstruction 4.4 Calorimeter Results 4.4.1 Calorimeter Energy 4.4.2 Neutral Cluster Properties 4.4.3 1r 0 Production 4.4.3.1 Multiplicities 4.4.3.2 1r 0 Properties 4.4.3.3 Average p} Distributions 4.4.3.4 Azimuthal Angle of Production 4.4.4 T/ Production 4.4.5 Systematic Errors 4.4.5.1 Multiplicities 4.4.5.2 Pi Zero Properties vii