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Improving Event Classification at Km3net with Orcanet Improving event classification at KM3NeT with OrcaNet A thesis submitted in fulfillment of the requirements for Master in Experimental Physics at Universiteit Utrecht Author Enrique Huesca Santiago July, 2019 2 Abstract Neutrino telescopes such as KM3NeT are being built to detect these tiny, elusive particles. In the case of KM3NeT ORCA the aim is to determine the currently unknown neutrino mass hierarchy, which has far reaching implications for scientific research. Distinguishing between the different types of neutrino flavour interactions seen in the detector is critical for this goal. In order to achieve this, Deep-Learning algorithms such as the OrcaNet framework for KM3NeT are being developed and tested. This work consists of an exploration of the performance of this tool for the concrete case of event identification in KM3NeT, and its implications for determining the neutrino mass hierarchy. Here, clear evidence is presented that there is potential for event classification and identification beyond the current binary track-shower scheme, including up to 40% separation for electron neutrino charged current events. Para mamá y papá, los mejores científicos que nunca han sido. Student number: 6310141 Thesis Research Project: 60 ECTS Contact: [email protected] Supervisor: Paul de Jong First Examiner: Raimond Snellings Second Examiner: Alessandro Grelli Word Count: 24966 3 Contents 1 Introduction6 1.1 Motivation.................................6 1.2 Neutrino classification.......................... 10 1.3 Cherenkov Radiation........................... 11 2 Neutrino Physics 12 2.1 Neutrino Discovery............................ 12 2.2 Neutrino Oscillations........................... 14 2.2.1 Neutrino Mixing Formalism................... 14 2.2.2 The Solar Neutrino Problem................... 16 2.2.3 3 flavour neutrino oscillations.................. 19 2.3 Neutrino Astronomy........................... 21 3 KM3NeT 23 3.1 KM3NeT in a nutshell.......................... 23 3.2 ARCA................................... 25 3.3 ORCA................................... 26 3.4 Software Infrastructure.......................... 28 3.4.1 Triggering............................. 28 3.4.2 Reconstruction and Particle Identification........... 28 3.5 Mass ordering fit............................. 30 4 Methods 32 4.1 OrcaSong................................. 32 4.2 OrcaNet.................................. 35 4.3 Statistical Classification......................... 38 4.3.1 Threshold............................. 38 4.3.2 Estimators............................. 40 4.3.3 The receiver operating characteristic.............. 42 5 Results 43 5.1 Prediction probability distributions................... 43 5.1.1 Track probability distribution.................. 43 5.1.2 New probability distributions.................. 50 5.2 Visible Energy dependency........................ 56 5.2.1 Threshold effect.......................... 57 5.3 Categories comparison.......................... 58 5.3.1 Hadronic and Tau shower.................... 60 5.4 Separation................................. 62 4 Contents 5.4.1 2 categories: e-CC vs e-NC.................... 62 5.4.2 2 categories: Track-shower.................... 64 5.4.3 3 categories: track, EM shower, tau + NC........... 65 5.4.4 4 categories............................ 65 5.5 ROC curves................................ 67 6 Conclusions 72 6.1 Research Objectives............................ 72 6.2 Practical Implications........................... 76 6.3 Future Research.............................. 77 Bibliography 79 Online References 81 A Introduction to Machine Learning 82 A.1 Neural Network.............................. 84 A.2 Model’s Architecture........................... 85 B Hidden Feature Check 88 C Technical Details 92 C.1 The Pipeline................................ 92 C.2 OrcaSong 2.0............................... 94 Note on the content The following work is structured as follows: Chapter 1 explains the present work within neutrino telescopes research. Chapter 2 is a complete overview of the neutrino history from its discovery to the present. Chapter 3 is dedicated to describe the KM3NeT neutrino telescope. Chapter 4 explains explains the concrete tools and methods used in this thesis. Chapter 5 contains and comments the different types of results. Finally, Chapter 6 is dedicated to the different conclusions and what to do next. Chapters 1,2,3,4 are heavily based on bibliography. Two appendices are included to aid with the Machine Learning model and other useful information. 5 1 Introduction 1.1 Motivation Neutrinos are neutrally charged fermions (spin 1/2) suggested by Pauli and introduced by Fermi to correctly balance the beta decay and other nuclear interactions. Fermi’s description, now fully developed as the weak interaction, has been extended for the three families of the Standard Model of elementary particles between the leptons (l = e; µ, τ) and their associated neutrinos (νe; νµ; ντ ). Figure 1.1: The Feynman diagram of weak neutrino interactions with a nucleon. a) shows a charged current (CC) interaction, mediated by a weak boson W ±. b) shows a neutral current (NC) interaction, scattering via a Z0 boson. Source [38]. Neutrinos have the lowest cross sections known of all elementary particles, which makes their direct detection and a study of their properties almost impossible. What makes them interesting is that our limited knowledge about neutrinos includes plenty of puzzling phenomena that cannot be explained within the Standard Model of particle physics. At the same time, their ghost-like nature is also the reason why neutrinos originating from extreme astrophysical phenomena, such as the core of a supernova or deep within a black hole, are able to travel unimpeded in the universe, bringing that information to Earth. Because of the low cross sections, neutrinos were assumed to be massless and were placed as a footnote of the physics revolution of the 20th century. Today, thanks to the revolutionary breakthroughs by the experiments of the Kamiokande collaboration and many others, neutrino physics has become an area of active and important research. The elusive particles now seem to hold some clues about the nature of 6 1.1. Motivation the biggest and smallest length and energy scales at the same time, lying in the intersection between the disciplines of particle physics and astrophysics alike1. For this reason, today we have the bizarre concept of neutrino telescopes, experiments that are actively tackling particle physics questions alongside astrophysical research, such is the case of the ongoing experiments ANTARES/KM3NeT and IceCube. A neutrino telescope is essentially a very large array of photodetectors placed deep under water (KM3NeT) or ice (IceCube) that targets atmospheric and cosmic neutrinos (see 1.2). The surrounding medium (water or ice) is used for both shielding against other interactions and Cherenkov light radiation (see 1.3). This simple idea does not capture the fact that neutrino telescopes need to be (and indeed are) the biggest experiments ever designed for two different reasons: First, the interactions in the water/ice with energies of a GeV and beyond create a shower of particles (with their associated Cherenkov light) that spans hundreds of meters. Recording the complete light profile is necessary to fully understand this events. Second, neutrinos with extragalactic origin have an extremely low flux (seen in 1.4). Other neutrino experiments, like the accelerator experiments or nuclear reactor experiments (like the one who discovered the neutrino, see section 2.1), have a very clear source of neutrinos with a controlled direction and position. On the other hand, neutrino telescopes observe the neutrinos that come from the most remote and singular sources in the universe. IceCube, for example, has seen only 82 cosmic events between 60 TeV and 10 PeV in six years of operation [39]. This simply means that in order to have a non-negligible interaction rate (and study enough events to draw statistically-relevant conclusions) the instrumentalized volume needs to be as big as possible. This alone makes the minimum size for a viable telescope, just enough to detect as little as 10 events in the PeV scale per year, around 1 cubic kilometer, containing water or ice in the order of gigatons. Capturing cosmic neutrinos is promising goal for potential knowledge that we can get in return, but is indeed a very difficult task which requires tremendous human and material effort. Due to sheer material and economical reasons, it is impossible to fully cover a cubic kilometer with photodetectors. This is why neutrino detectors have a sparse configuration. The sparsity means that most of the light produced by an event will be lost, either absorbed or scattered by the medium. Thus, the signature of the event, the event we can reconstruct from the light distribution of the shower of particles, is only a poor image of the actual interaction. Neutrino telescopes have a very low resolution, especially in the short range (meters). This means that, out of the many different particles produced in the showers, we can only record a handful of signatures based on the original neutrino interaction, shown in Fig 1.2 below. Even then, the large overlap between the light profiles of the three of the showers (electromagnetic, hadronic and tau) makes them effectively indistinguishable. The single signature that is clearly identifiable with our current resolution is the muon neutrino charged-current interaction (µ − CC), because
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