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Cluster Counting in Drift Chambers for Particle Identification Cluster Counting in Drift Chambers for Particle Identification and Tracking by Jean-Fran¸coisCaron B.Sc. Hon. Physics, University of Calgary, 2006 M.Sc., The University of British Columbia, Vancouver, 2009 a thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the faculty of graduate and postdoctoral studies (Physics) The University of British Columbia (Vancouver) December 2015 c Jean-Fran¸coisCaron, 2015 Abstract Drift chambers are a type of gaseous ionization detector used in high-energy physics experiments. They can identify charged particles and measure their momentum. When a high-energy charged particle crosses the drift chamber, it ionizes the gas. The liberated electrons drift towards positive-high-voltage wires where an ionization avalanche amplifies the signal. Traditional drift chambers use only the arrival time of the cluster of charge from the closest ionization for tracking, and use only the integral of the whole signal for particle identification. We constructed prototype drift chambers with the ability to resolve the charge cluster signals from individual ionization events. Different algorithms were studied and optimized to best detect the clusters. The improvements to particle identification were studied using a single-cell prototype detector, while the improvements to particle tracking were studied using a multiple- layer prototype. The prototypes were built in the context of initial work for the now-cancelled SuperB project, but the results apply to any drift chambers used in flavour-factory experiments. The results show that the choice of algorithm is not as critical as properly optimizing the algorithm parameters for the dataset. We find that a smooth- ing time of a few nanoseconds is optimal. This corresponds to bandwidth of a few hundred megahertz, indicating that gigahertz-bandwidth electronics are not required to make use of this technique. Particle identification performance is quantified by the fraction of real pi- ons correctly identified as pions with at most 10% of real pions mis-identified as muons. In our single-cell prototype, the performance increases from 50% ii to 60% of pions correctly identified when cluster counting is combined with a traditional truncated-mean charge measurement, compared to the charge measurement alone. Tracking performance is quantified by the single-cell resolution: the un- certainty in measuring the distance of charged particle tracks from a given sense wire. In our multiple-layer prototype, the single-cell tracking resolu- tion using traditional methods is measured to be ∼ 150 µm. With cluster counting implemented, the resolution is unchanged, indicating that the ad- ditional cluster information is not useful. iii Preface The first half of my research project was a study of the single-cell pro- totype particle tracking detectors which we call the TRIUMF prototypes. They are described in Section 4.2 and were built at TRIUMF by Philip Lu, Rocky So, and Wayne Faszer; the amplifiers used were designed and con- structed by Jean-Pierre Martin at the Universit´ede Montr´eal.A beam test was performed with the help of fellow SuperB collaboration members and TRIUMF staff: Christopher Hearty, Philip Lu, Rocky So, Racha Cheaib, Jean-Pierre Martin, Wayne Faszer, Alexandre Beaulieu, Samuel de Jong, Michael Roney, Riccardo de Sangro, Giulietto Felici, Giuseppe Finocchiaro, Marcello Piccolo, Wyatt Gronnemose, and Steven Robertson. With the data from the beam test, I performed a particle-identification study and the results were published in the paper titled \Improved particle identification using cluster counting in a full-length drift chamber proto- type", published by Elsevier in the journal Nuclear Instruments and Meth- ods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 735, on January 21 2014, Pages 169- 183 [1]. The article was written by me, except for the section describing the amplifiers which was written by Jean-Pierre Martin. The coding and analysis in the paper was entirely done by me, with consultation from the above-mentioned people in the SuperB collaboration. The content of that article is included in Chapter 4, with permission from the publisher and co- authors. Jerry Va'vra lent us the MCPs for our TOF system, and Hirohisa Tanaka lent us the oscilloscope for our data acquisition. In addition to the authors of the resulting article, Wyatt Gronnemose and Steven Robertson iv assisted during the beam test. The second half of my research project involved the multi-cell prototype called \proto 2" or the Italian prototype, and is described in Chapter 7. The Italian prototype was constructed at the Laboratorio Nazionale di Frascati by myself and Riccardo de Sangro, Giulietto Felici, Giuseppe Finocchiaro, and Marcello Piccolo. The beam test was performed at TRIUMF by the same people from the TRIUMF prototype beam test. The coding and anal- ysis for tracking was done entirely by me in consultation with Christopher Hearty. Several contributions were made to open-source programs. The most notable is the addition of a new interpolation method to the GNU Scientific Library [2]. Many bug reports and some patches were submitted to the developers of ROOT [3] and Garfield [4]. The largest patch involved cor- rections to gender-specific pronouns used in documentation; it is described in Appendix A.4. The analyses in this work also made extensive use of these open-source programs: Python [5], IPython [6], PyROOT [7] and NumPy [8]. This work was supported by the Natural Sciences and Engineering Re- search Council of Canada and TRIUMF. My supervisory committee members were Christopher Hearty, Janis McKenna, Joanna Karczmarek, and Kris Sigurdson. Sherry Leung assisted with edit- ing and proofreading. Figures 1.3, 1.4, 7.4, 7.9, and 7.18 were made by Alon Hershenhorn. v Table of Contents Abstract . ii Preface . iv Table of Contents . vi List of Tables . x List of Figures . xi Acknowledgements . xv 1 Introduction . 1 1.1 Particle Physics . 1 1.2 Flavour Physics . 2 1.3 Drift Chambers . 4 1.3.1 Tracking . 9 1.3.2 Particle Identification . 11 2 Design Considerations of Drift Chambers . 17 2.1 Overall Design . 17 2.2 Gas Composition . 20 2.3 Wire Materials . 22 2.4 Outer Structure . 25 2.5 Ageing . 27 vi 3 SuperB ............................... 31 3.1 Introduction . 31 3.2 SuperB Drift Chamber . 33 4 Particle Identification Study . 40 4.1 Introduction . 41 4.1.1 Drift Chambers . 41 4.1.2 Cluster Counting . 42 4.2 Apparatus . 45 4.2.1 Prototype Drift Chambers . 45 4.2.2 Amplifiers . 48 4.2.3 Wire Voltages . 49 4.2.4 Cabling . 50 4.2.5 Test Beam . 51 4.2.6 Time of Flight . 52 4.2.7 Trigger . 54 4.2.8 Data Acquisition System . 55 4.3 Simulations . 56 4.4 Beam Test Data . 57 4.5 Analysis . 58 4.5.1 Single-Cell Information . 59 4.5.2 Track Composition . 64 4.5.3 Combined Likelihood Ratio . 67 4.5.4 Figures of Merit . 70 4.6 Results . 71 4.6.1 Charge Integration . 71 4.6.2 Cluster Counting . 72 4.6.3 Cluster Timing for PID . 76 4.6.4 Dependence of PID on Gas Gain . 77 4.6.5 Momentum . 78 4.6.6 Dependence of PID on Window (Z-position) . 78 4.6.7 Cables . 80 4.6.8 Amplifiers . 82 vii 4.6.9 Summary of Results . 82 4.7 Conclusions . 84 4.8 Cluster-Counting Algorithms . 85 4.8.1 Smoothing Procedures . 85 4.8.2 Signal above Average . 86 4.8.3 Smooth and Delay . 87 4.8.4 Second Derivative . 87 4.8.5 Timeout Booster . 88 5 Multi-Cell Prototype . 90 5.1 Introduction . 90 5.2 Wires . 91 5.3 Structure . 94 5.4 Electronics . 96 6 Theoretical Tracking Improvements . 102 6.1 Model . 103 6.2 Distances Along the Track . 105 6.3 Distances From the Wire . 107 6.4 Bayesian Analysis . 111 6.4.1 The First Cluster . 112 6.5 The Second Cluster . 114 6.6 More Clusters . 115 6.7 Summary . 117 7 Tracking . 119 7.1 Overview . 119 7.2 Measuring the Arrival Times . 120 7.3 Garfield Time-to-Distance Relations . 124 7.3.1 Interpolation . 127 7.4 Track Fitting . 129 7.4.1 Track Initial Parameters . 132 7.4.2 Minimization Troubles . 133 7.4.3 Track Parameter Uncertainties . 135 viii 7.5 Iterative Refinement and Resolution Measurement . 140 7.6 Timing Clusters . 145 7.6.1 Motivation . 145 7.6.2 Overview of Technique . 146 7.6.3 Algorithms and Parameters . 149 7.7 Combined Likelihood . 153 7.8 Results . 157 8 Conclusion . 160 8.1 Summary of Results . 160 8.1.1 Generalities . 160 8.1.2 Particle Identification . 161 8.1.3 Tracking . 162 8.2 Future Improvements . 163 8.2.1 Generalities . 163 8.2.2 Particle Identification . 164 8.2.3 Tracking . 165 8.2.4 Other Ideas . 167 Bibliography . 169 A Supporting Materials . 180 A.1 The Novosibirsk Function . ..
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