Monte-Carlo study of top quark production at the Large Hadron Collider
- 주제(키워드) Monte Carlo , Top quark , High-performance computing , LHC , Rivet toolkit
- 발행기관 경희대학교 대학원
- 지도교수 고정환
- 발행년도 2023
- 학위수여년월 2023. 2
- 학위명 석사
- 학과 및 전공 대학원 물리학과
- 세부분야 해당없음
- 원문페이지 xi, 119 p.
- 실제URI http://www.dcollection.net/handler/khu/200000658597
- UCI I804:11006-200000658597
- 본문언어 영어
- 저작권 경희대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Massive amounts of Monte-Carlo simulation samples are required for the precision measurement of the properties of elementary particles. For producing a diversity of Monte-Carlo samples with different software and parameters to be optimized to be consistent with the real data, a tremendous amount of resources is needed. In this study, we aim for a framework that integrates Monte-Carlo sample generation and its validation on computing environments at different scales. We choose container technology to enable running Monte-Carlo software in inhomogeneous environments. Benchmarks for mass production of MC samples to find optimized software setup to enable mass production of the MC samples. We generated MC samples of top quark pair or four top quarks produced from the proton-proton collisions at √s = 13TeV with various MC software programs and their tune parameters. The Rivet toolkit is used to verify and perform comparisons. We also propose a new variable inspired by the N-subjettiness to understand the event topology of the top quark pair in the dilepton channel and four tops in the full-hadronic channel.
more목차
1 Introduction 1
2 Theory 4
2.1 Standard Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Top quark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Top quark processes of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3.1 t¯t pair production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3.2 Fourtop production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 Monte Carlo Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3 Monte Carlo Generators 12
3.1 MC generation workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Matrix element . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Jet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.4 Partonshower . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.5 Hadronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.6 List of MC generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.6.1 Madgraph5_aMC@NLO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.6.2 POWHEG BOX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.6.3 PYTHIA8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.6.4 Herwig7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.6.5 Sherpa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4 Computing environment for MC generation 21
4.1 Generated processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Computing environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3 Cost-effectiveness Optimization for mass production . . . . . . . . . . . . . . . . . 22
4.3.1 Single core performance comparison . . . . . . . . . . . . . . . . . . . . . . . 23
4.3.2 Multicore comparison with Madgraph5_aMC@NLO . . . . . . . . . . . . . . 24
4.3.3 Nurion Single node occupancy test . . . . . . . . . . . . . . . . . . . . . . . . 25
4.3.4 Nurion generation strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4 Production time Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.4.1 Time estimation at local server . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.4.2 Time estimation at available systems . . . . . . . . . . . . . . . . . . . . . . . 26
4.4.3 Extrapolation of sample generation . . . . . . . . . . . . . . . . . . . . . . . 27
4.5 List of Monte Carlo samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5 Physics validation 30
5.1 Rivet toolkit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5.2 Object selection for t¯t + jets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.3 Event selection for t¯t + jets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.4 Object selection for fourtop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
5.5 Event selection for fourtop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
5.6 N-subjettiness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.6.1 Simplified N-subjettiness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
6 MC Sample comparison 35
6.1 t¯t + jets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.1.1 Ordinary observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.1.2 Observables from the idea of N-subjettiness . . . . . . . . . . . . . . . . . . . 44
6.2 t¯t + jets NLO vs t¯t + jets LO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
6.2.1 Ordinary observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
6.2.2 Observables from the idea of N-subjettiness . . . . . . . . . . . . . . . . . . . 57
6.3 t¯t + jets vs t¯tb¯b . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6.3.1 Ordinary observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6.3.2 Observables from the idea of N-subjettiness . . . . . . . . . . . . . . . . . . . 70
6.4 PDF variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.4.1 Ordinary observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.4.2 Observables from the idea of N-subjettiness . . . . . . . . . . . . . . . . . . . 83
6.5 Strong coupling constant alpha S variance . . . . . . . . . . . . . . . . . . . . . . . 87
6.5.1 Ordinary observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6.5.2 Observables from the idea of N-subjettiness . . . . . . . . . . . . . . . . . . . 95
6.6 Fourtop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.6.1 Ordinary observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.6.2 Observables from the idea of N-subjettiness . . . . . . . . . . . . . . . . . . . 106
7 Conclusion 111
References 112