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研究生： 鄭博仁
Cheng, Po-Jen
論文名稱： 用機器學習區分部分可視噴流
Classification of Semi-visible Jets with Machine Learning
指導教授： 張敬民
Cheung, King-Man
徐士傑
Hsu, Shih-Chieh
口試委員: 徐百嫻
Hsu, Pai-hsien
郭家銘
Kuo, Chia-Ming
學位類別： 碩士
Master
系所名稱： 理學院 - 物理學系
Department of Physics
論文出版年： 2020
畢業學年度： 109
語文別： 英文
論文頁數： 34
中文關鍵詞： 機器學習 、部分可視噴流 、對撞機 、噴流辨識
外文關鍵詞： semi-visible, Machine, Classificatio, Hidden Valley
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    • 摘要部分可視噴流是一種我們尚未觀察到的暗物質模型預測,它在模型中是由可視的強子與不可視的穩定暗介子組成,因為它有一部分是可以被偵測到的,所以我們就能在大型強子對撞機裡面搜尋這個訊號.但我們很難去區分部分可視噴流與量子色動力學的噴流,所以我們應用機器學習去做這些種類的區分,我們在研究中應用深度神經網路、卷積神經網路與ParticleNet來區分它們,而我們所輸入的參數是噴流的結構變數、噴流的影像還有噴流組成粒子的參數.最後我們比較這些模型的區分能力.

    Semi-visible jets (SVJ) are predictions of dark sectors which are not observed yet, and they are composed of visible hadrons and stable dark hadrons. Since they have a visible part, we can search SVJ in the Large Hadron Collider. We apply Machine Learning (ML) techniques to classify SVJ from Quantum Chromodynamics jets because it’s extremely challenge to distinguish them. In this thesis, we deployed three different deep learning models using combinations of low-level features and high-level features and compared their performances. Models include Deep Neural Network, Convolution Neural Network, and ParticleNet. High-level features are jet-substructure variables, and low-level features are jet images or variables of jet’s constituents. We find that ParticleNet with constituent variables as inputsprovides the best classification power.

    ContentsContentsiiList of TablesivList of Figuresvi1 Introduction12 Description of Physics and Machine Learning22.1 Dark sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22.1.1s-channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32.1.2Semi-Visible Jets . . . . . . . . . . . . . . . . . . . . . . . . . . .42.2 Quantum Chromodynamics Jets . . . . . . . . . . . . . . . . . . . . . .42.3 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53 Event Generation73.1 Semi-visible Jets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .73.2 Quantum Chromodynamics Jets . . . . . . . . . . . . . . . . . . . . . .8i 4 Jet Substructure and Jet image study104.1 Algorithm explanations . . . . . . . . . . . . . . . . . . . . . . . . . . .104.2 Jet image and Jet Substructure plot . . . . . . . . . . . . . . . . . . . .115 Model Architecture155.1 Deep Neural Network and Convolution Neural Network . . . . . . . . .155.2 Two Convolution Neural Network(2CNN) . . . . . . . . . . . . . . . . .195.3 ParticleNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .206 Result236.1 Result Analysis explanation . . . . . . . . . . . . . . . . . . . . . . . . .236.2 Receiver Operating Characteristic plot. . . . . . . . . . . . . . . . . .256.3 Comparison Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .257 Conclusion and Outlook27Appendix27A Appendix28A.1 MadGraph Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28A.2 DelphesPythia8 setting . . . . . . . . . . . . . . . . . . . . . . . . . . . .29ii (page i)

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