A Deep-Learning-Based a` CCQE Selection for Searches Beyond the Standard Model with MicroBooNE Davio Cianci Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2021 © 2021 Davio Cianci All Rights Reserved Abstract A Deep-Learning-Based a` CCQE Selection for Searches Beyond the Standard Model with MicroBooNE Davio Cianci The anomalous Low Energy Excess (LEE) of electron neutrinos and antineutrinos in MiniBooNE has inspired both theories and entire experiments to probe the heart of its mystery. One such experiment is MicroBooNE. This dissertation presents an important facet of its LEE investigation: how a powerful systematic can be levied on this signal through parallel study of a highly correlated channel in muon neutrinos. This constraint serves to strengthen MicroBooNE’s ability to confirm or validate the cause of the LEE and will lay the groundwork for future oscillation experiments in Liquid Argon Time Projection Chamber (LArTPC) detector experiments like SBN and DUNE. In addition, this muon channel can be used to test oscillations directly, demonstrated through the world’s first a` disappearance search with LArTPC data. Table of Contents List of Figures .......................................... vii List of Tables .......................................... xxiv Acknowledgments ........................................xxvii Prologue ............................................. 1 I Introductions 2 Chapter 1: Neutrinos ...................................... 3 1.1 A Strange Position within the Standard Model . 3 1.1.1 The Standard Model of Particle Physics . 3 1.1.2 How Neutrinos Fit In . 5 1.2 Neutrino Oscillation Formalism . 6 1.3 Leading Experimental Constraints . 9 1.4 eV Scale Neutrino Masses . 11 Chapter 2: The MicroBooNE Experiment ........................... 14 2.1 The BNB . 15 2.1.1 The Proton Beam . 15 i 2.1.2 Proton Target and Focusing Horn . 16 2.1.3 Beam Composition and Flux Uncertainty . 18 2.2 The MicroBooNE Detector . 19 2.2.1 The Time Projection Chamber . 20 2.2.2 The Optical System . 23 2.2.3 Triggering . 23 2.2.4 The Readout . 24 2.2.5 2D Deconvolution . 26 2.3 Neutrino Interactions and Their Signatures in MicroBooNE . 28 Chapter 3: On Notation .................................... 33 II Rising Action 34 Chapter 4: Sterile Neutrinos ................................. 35 4.1 What We Talk About When We Talk About the LEE . 35 4.1.1 What has MiniBooNE Measured? . 35 4.1.2 Limits of Cherenkov technology . 36 4.2 MiniBooNE’s eLEE and the Sterile Neutrino Hypothesis . 38 4.2.1 Further Extending the Extended Standard Model . 38 4.2.2 Oscillation Probabilities . 40 4.2.3 Predicting an Oscillated Spectrum . 43 4.2.4 Predicting 3+1 a` Disappearance in MicroBooNE . 45 4.2.5 Drawing Limits and Confidence Intervals . 46 4.2.6 Sensitivities . 50 ii 4.3 Sterile Neutrino Compatibility with Global Data . 50 4.3.1 a4/a¯4 Appearance Experiments . 53 4.3.2 a4/a¯4 Disappearance Experiments . 55 4.3.3 a`/a¯` Disappearance Experiments . 58 4.3.4 Global Short Baseline Data . 60 4.3.5 Tensions and Limitations . 62 4.4 The Future of Global Fit Analyses . 66 4.4.1 The 2016 Global Fit . 66 4.4.2 Coverage Plots . 70 Chapter 5: A Deep Learning-Based LEE Search with MicroBooNE ............. 74 5.1 The MicroBooNE Path . 74 5.2 DL LEE Philosophy and Reconstruction . 77 5.2.1 The DL Reconstruction Strategy . 77 5.2.2 Wire-Cell Cosmic Tagging and Semantic Segmentation . 79 5.2.3 3D Muon Neutrino Vertex Finding and Reconstruction . 81 5.3 Quantifying the eLEE Measurement . 82 5.3.1 GENIE Models . 82 5.3.2 A Simple LEE Model . 83 5.3.3 A Hypothesis Test for the LEE . 85 5.3.4 Motivation for the 1`1? Constraint . 87 III Conflict 91 Chapter 6: The 1`1? Selection ................................ 92 iii 6.1 Pre-Selection . 94 6.1.1 Data and Simulation Samples . 94 6.1.2 Two-Body Scattering, Kinematics, and Boosting . 96 6.1.3 The Pre-Selection Cuts . 100 6.1.4 What’s in a Plot? . 104 6.1.5 Truth labeling . 105 6.1.6 Training the BDTs . 109 6.1.7 MPID . 112 6.2 Selection . 113 6.2.1 Applying the BDT Weights . 113 6.2.2 MPID Cut . 115 6.2.3 Final selection . 116 6.3 Systematics . 118 6.3.1 Reweightable Systematics . 121 6.3.2 Detector Systematics . 122 IV Resolution 124 Chapter 7: Quantifying Final Uncertainty .......................... 125 7.1 Relative Systematic Contributions to Uncertainty . 125 7.1.1 Theoretical Limit on a` Disappearance Sensitivity . 127 7.2 Strength of the a` Constraint . 128 Chapter 8: Quantifying MicroBooNE’s Sensitivity to a MiniBooNE eLEE ......... 133 8.1 SBNfit . 133 iv 8.1.1 Drawing Pseudo-Experiments with SBNfit . 134 8.2 Method of Frequentist Hypothesis Testing with SBNfit . 135 8.3 MicroBooNE Sensitivity to a MiniBooNE eLEE . 137 8.4 Further Extrapolation . 138 Chapter 9: Measurement of a` Disappearance with MicroBooNE .............. 140 9.1 Calculating Sensitivity . 140 9.1.1 A Parallel Shape-Only Analysis . 142 9.2 Applying a Frequentist Correction . 143 9.3 Fitting to Data . 147 V Denouement 154 Chapter 10: Reflections On the Long Way Home ....................... 155 Epilogue ............................................. 157 References ............................................ 158 Appendix A: Sterile Neutrino Fits to Global Data ...................... 165 A.1 IceCube 2017 Oscillation Result Reproduction . 165 A.1.1 Constructing a Predicted Spectrum . 167 A.2 DANSS 2018 Oscillation Result Reproduction . 170 A.2.1 Calculate Ratio of Predicted Events in True Positron Energy . 171 A.2.2 Smear Ratios into Reconstructed Positron Energy . 172 A.2.3 Calculate j2 ..................................172 v A.3 NEOS 2017 Oscillation Result . 173 A.3.1 Calculate Predicted Event rates in NEOS and Daya Bay . 174 A.3.2 Calculate the Ratio of the Event Rates in NEOS and Daya Bay . 176 A.4 Comparisons of Fits with Reference Examples . 177 Appendix B: 1`1? Selection Asides ............................. 180 B.1 Boosted Decision Tree Primer . 180 B.2 Selection Code Release . 181 B.3 U) Study . 182 B.4 Inter-Run Compatibility in BDT Selection . 186 B.4.1 Compatibility Tests . 187 B.4.2 Consequences of Incompatibility . 189 B.4.3 Conclusions . 189 B.5 BDT Input Distributions At Preselection . 191 B.6 1`1? Distributions After Selection . 199 Appendix C: a` Disappearance Validation .......................... 215 C.1 Signal Injection Closure Tests . 215 vi List of Figures 1.1 Table of the bosons and three generations of fermions that currently make up the Standard Model with their quantum properties [3]. 4 1.2 Feynman diagrams for charged-current neutrino interactions (left) and neutral cur- rent neutrino interactions (right). In the diagrams, = is a neutron, ? is a proton, # is either nucleon, and ; is any lepton. The shaded circles on each diagram ac- count for nuclear interactions, which may complicate the final-state outputs. Time progresses from left to right in each diagram. 6 1.3 A cartoon demonstrating how the three SM neutrino mass states (colored bars) are divided between three flavor components (individual color components). Neither the flavor compositions nor mass positions are to scale, but up-to-date measure- ments can be found in [4] . 7 1.4 Plot of the excess of electron antineutrinos observed by LSND (dots). The SM prediction is given as the sum of the red and green histograms. The blue hatched histograms illustrate hypothetical predictions beyond the ESM to attempt to explain the excess [18]. 11 1.5 Plot of MiniBooNE’s observed (dots) versus their predicted (stacked histogram) counts of electron neutrinos and antineutrinos. A clear excess is visible in the leftmost four bins in both neutrino modes [19]. 12 1.6 antineutrino observed excesses from both MiniBooNE and LSND overlaid on a common axis. The dotted and dashed lines illustrate hypothetical, oscillatory dis- tributions to provoke curiosity [19, 18]. ..