대형 강입자 가속기 기반 탐색에서 기계 학습 응용

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 물리·천문학부(물리학전공), 2021. 2. 김형도.In this thesis, we study the application of machine learning for searches at the Large Hadron Collider without a sharp resonance peak. First, we use machine learning to find the best observables for the broad resonance earch. A vector resonance from the composite Higgs models in $t\bar{t}$ final state is considered as a benchmark. Various approaches are adopted to interpret the abstracted information by the machine, and we conclude that the resonance energy is still important for the broad resonance search, while the angular distributions and the transverse momenta of the decayed products have also great importance. Second, we use machine learning to extract information about the resonance from other than the final state. We show the correlation between the kinematics of jets from initial state radiation and the resonance particle. To demonstrate the experimental feasibility we perform the searching for invisible decay of Higgs by using machine learning. As a result, we show that the bound from gluon-fusion production mechanism can be improved even stronger than the other production mechanisms due to the correlation.이 논문은 대형 강입자 가속기에서의 탐색 중, 중간 입자의 날카로운 공명 정점이 없는 경우에 대한 기계 학습의 응용을 다룬다. 응용의 한가지 예로써, 기계 학습을 이용하여 탐색하고자 하는 중간 입자의 공명의 폭이 넓을 때 가장 적합한 관측량이 무엇인가에 대하여 조사한다. 보다 자세한 연구를 위하여, 합성 힉스 모형의 전반에서 예측 되는 무거운 합성 벡터 중간자가 위 쿼크와 위 쿼크의 반입자로 공명 붕괴하는 과정을 생각한다. 다양한 방법론을 통하여 기계 학습을 통해 추상화된 정보를 해석하여, 결과적으로 공명 폭이 넓은 경우에도 재구축 된 중간 입자의 질량이 유용하고 붕괴한 입자들의 각도 분포 및 직교 운동량 또한 해당 탐색에서 중요성을 갖고 있음을 확인한다. 두 번째 예로써, 기계 학습을 이용하여 공명을 유발하는 중간 입자의 붕괴 후 최종 상태가 아닌 곳에서 방출 된 입자를 통해 중간 입자에 대한 정보를 추출하는 방법을 연구한다. 이를 이해하기 위하여 초기 상태 복사로 나온 제트와 중간 입자 사이의 상호 관련성이 있음을 보인다. 제시된 분석 방법의 실제 실험에 대한 응용 가능성을 실증하기 위하여, 힉스 입자의 관측 불가능 입자들로의 붕괴에 대한 연구를 기계학습을 이용하여 재현한다. 결과적으로 글루온 융합으로 힉스입자가 생성되는 과정에서 얻을 수 있는 힉스입자의 관측 불가능 입자로 붕괴하는 확률에 대한 구속 조건이 크게 개선되며, 다른 생성 과정보다 강한 구속 조건을 줄 수 있음을 보인다.Abstract i List of Figures v List of Tables x 1 Introduction 1 2 Reviews on the Standard Model and Neural Network 7 2.1 The Standard Model 7 2.2 Neural Network 25 3 Broad resonance in $t\bar{t}$ Final State 30 3.1 Introduction 31 3.2 Benchmark Model 33 3.3 Searching for a Broad $t\bar{t}$ Resonance 34 3.3.1 Breit-Wigner Parametrisation 34 3.3.2 Preparation of Training Data 35 3.3.3 Training the DNN 41 3.3.4 Setting Bounds for the Signal 46 3.4 Figuring out What the Machine Had Learned 50 3.4.1 Testing High-level Observables 50 3.4.2 Ranking Input Observables by Importance 55 3.4.3 Planing Away $M_{t\bar{t}}$ 60 3.5 Conclusion 63 4 Invisible Higgs Decay 64 4.1 Introduction 64 4.2 Estimation on Leading Jet Kinematics 66 4.2.1 Higgs Produced via Gluon-Fusion 68 4.2.2 Massive Gauge Boson Production 72 4.2.3 Multiple Production 73 4.3 Phenomenology of Invisible Decay of Higgs 76 4.4 Data Preparation and Multi-variate Analysis 78 4.5 Analysis Method 83 4.6 Result and Conclusion 85 5 Conclusion 91 Bibliography 95 A Profile Likelihood Ratio Test 116 B Collider Phenomenology 121 B.1 Parton Density Function 121 B.2 Partonic Cross Section 123 B.3 Hadronic Cross Section 125 C Loop Functions 129 D Jet Tagging Algorithm for Simulated Events 133 초록 137Docto

A search for the Higgs boson decay to two electrons with the Compact Muon Solenoid experiment

A search is presented for the Higgs boson decay to a pair of electrons in proton-proton collisions at a centre-of-mass energy of 13 TeV. The data set was collected with the Compact Muon Solenoid experiment at the Large Hadron Collider between 2016 and 2018, corresponding to an integrated luminosity of 138 inverse femtobarns. The analysis develops event categories targeting Higgs boson production via gluon fusion and vector boson fusion, defined by selection on dedicated machine learning-based classifiers. An upper limit on the Higgs boson branching fraction to an electron pair is determined as 3.0 x 10^(-4) at the 95% confidence level, which is the most sensitive limit to date.Open Acces

W Boson Polarization Studies for Vector Boson Scattering at LHC: from Classical Approaches to Quantum Computing

The Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) has, in the recent years, delivered unprecedented high-energy proton-proton collisions that have been collected and studied by two multi-purpose experiments, ATLAS and CMS. In this thesis, we focus on one physics process in particular, the Vector Boson Scattering (VBS), which is one of the keys to probe the ElectroWeak sector of the Standard Model in the TeV regime and to shed light on the mechanism of ElectroWeak symmetry breaking. VBS measurement is extremely challenging, because of its low signal yields, complex final states and large backgrounds. Its understanding requires a coordinated effort of theorists and experimentalists, to explore all possible information about inclusive observables, kinematics and background isolation. The present work wants to contribute to Vector Boson Scattering studies by exploring the possibility to disentangle among W boson polarizations when analyzing a pure VBS sample. This work is organized as follows. In Chapter1, we overview the main concepts related to the Standard Model of particle physics. We introduce the VBS process from a theoretical perspective in Chapter2, underlying its role with respect to the known mechanism of ElectroWeak Symmetry Breaking. We emphasize the importance of regularizing the VBS amplitude by canceling divergences arising from longitudinally polarized vector bosons at high energy. In the same Chapter, we discuss strategies to explore how to identify the contribution of longitudinally polarized W bosons in the VBS process. We investigate the possibility to reconstruct the event kinematics and to thereby develop a technique that would efficiently discriminate between the longitudinal contribution and the rest of the participating processes in the VBS. In Chapter 3, we perform a Montecarlo generator comparison at different orders in perturbation theory, to explore the state-of-art of VBS Montecarlo programs and to provide suggestions and limits to the experimental community. In the last part of the same Chapter we provide an estimation of PDF uncertainty contribution to VBS observables. Chapter 4 introduces the phenomenological study of this work. We perform an extensive study on polarization fraction extraction and on reconstruction of the W boson reference frame. We first make use of traditional kinematic approaches, moving then to a Deep Learning strategy. Finally, in Chapter 5, we test a new technological paradigm, the Quantum Computer, to evaluate its potential in our case study and overall in the HEP sector. This work has been carried on in the framework of a PhD Executive project, in partnership between the University of Pavia and IBM Italia, and has therefore received supports from both the institutions. This work has been funded by the European Community via the COST Action VBSCan, created with the purpose of connecting all the main players involved in Vector Boson Scattering studies at hadron colliders, gathering a solid and multidisciplinary community and aiming at providing the worldwide phenomenological reference on this fundamental process

Search for pair production of Higgs Bosons decaying to four bottom quarks with data collected by the ATLAS detector

Using the full Run-2 data recorded by the ATLAS detector, a search for the elusive Higgs pair production decaying into four bottom quarks, HH → bbbb, is presented in this thesis. The full Run-2 dataset corresponds to 126 fb−1 of integrated luminosity. The theoretical motivations for this search, which are summarized in this thesis, are clear as the search can probe the structure of the Higgs potential and Beyond the Standard Model physics. To reconstruct the HH → bbbb events a combination of multi-b-jet triggers are used. Events are then selected if they contain at least four small radius jets that have passed the b-tagging selection. These jets are paired to reconstruct the Higgs candidates. As Monte Carlo simulations cannot reliably reproduce the bbb¯b final state, a data-driven approach is taken to produce the background estimate. This makes use of a neural network to predict the background in the signal region. The data-driven approach is validated by the use of several orthogonal control samples. The search is used to set exclusion limits at a 95 % confidence level for heavy resonances and non-resonant gluon-gluon fusion HH production. Two benchmark signals consisting of a spin-0 narrow width scalar and a spin-2 graviton were used for the resonant search. The upper limit on the cross-section of the non-resonant Standard Model HH production via gluon-gluon fusion was observed to be 5.1 times the Standard Model prediction. The trilinear Higgs self-coupling was constrained to the range of [-6.0, 15.0] times the Standard Model prediction. The improvements made to the bbb¯b channel have made the search competitive with the other final states. These optimizations will be useful to maximize the potential of the HL-LHC program

Inference Aware Neural Optimization for Top Pair Cross-Section Measurements with CMS Open Data

In recent years novel inference techniques have been developed based on the construction of summary statistics with neural networks by minimizing inference-motivated losses via automatic differentiation. The inference-aware summary statistics aim to be optimal with respect to the statistical inference goal of high energy physics analysis by accounting for the effects of nuisance parameters during the model training. One such technique is INFERNO (P. de Castro and T. Dorigo, Comp.\ Phys.\ Comm.\ 244 (2019) 170) which was shown on toy problems to outperform classical summary statistics for the problem of confidence interval estimation in the presence of nuisance parameters. In this thesis the algorithm is extended to common high energy physics problems based on a differentiable interpolation technique. In order to test and benchmark the algorithm in a real-world application, a complete, systematics-dominated analysis of the CMS experiment, "Measurement of the top-quark pair production cross section in the tau+jets channel in pp collisions at sqrt(s) = 7 TeV" (CMS Collaboration, The European Physical Journal C, 2013) is reproduced with CMS Open Data. The application of the INFERNO-powered neural network architecture to this analysis demonstrates the potential to reduce the impact of systematic uncertainties in real LHC analysis