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研究生:吳晉瑋
研究生(外文):Wu, Chin-Wei
論文名稱:波形鑑別在JUNO探測器中超新星微中子事例上的應用
論文名稱(外文):Pulse Shape Discrimination Application to Supernova Neutrino Events in JUNO
指導教授:林貴林
指導教授(外文):Lin, Guey-Lin
口試委員:高文芳王正祥林貴林
口試委員(外文):Kao, Wen-FangWang, Chung-HsiangLin, Guey-Lin
口試日期:2017-11-02
學位類別:碩士
校院名稱:國立交通大學
系所名稱:物理研究所
學門:自然科學學門
學類:物理學類
論文種類:學術論文
論文出版年:2017
畢業學年度:106
語文別:英文
論文頁數:48
中文關鍵詞:JUNO超新星微中子波形鑑別
外文關鍵詞:JUNOsupernova neutrino burstpulse shape discrimination
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超新星爆炸帶有豐富的微中子信息,使其成為微中子物理中不可或缺的一環,1987A的超新星爆炸使人類首次以探測器觀察到太陽系以外的微中子,為微中子探測寫下新的一頁,而隨著探測器技術的演進,我們對未來的超新星爆炸事件將具有更好的探測與分析能。JUNO地下微中子探測器是中國最新的微中子探測器,預計在2020年開始取數,其良好的能量分辨率,以及優良的發光效率,無疑的成為下一代最受矚目的微中子探測器。

本論文將運用JUNO官方的模擬架構探討超新星在JUNO中產生的事例,並從中加以區分。首先,我們運用Nakanato 等人團隊的超新星數值模擬結果作為產生子的輸入,之後將產生子結果放入探測器模擬,再從給出的蒙地卡羅信息中,給予適當的條件篩選並初步區分各個微中子反應。最後,我將運用波形鑑別的技巧,進一步探討其用以提升效能的可能性。
Supernova burst has abundant neutrino information and plays an important role in neutrino physics. Supernova 1987A was the first neutrino burst we were able to detect by neutrino detector which left a milestone in neutrino experiment. As the technique grows, better supernova event detection capability can be expected.

The Jiangmen Underground Neutrino Observatory (JUNO) is the latest neutrino detector situated at China which is expected to start running in 2020. Its good energy resolution and light yield make it one of the most precise neutrino detectors in the world.

In this dissertation, we perform the supernova neutrino simulation using the framework of JUNO official software. First, we use the supernova numerical result from Nakanato et al. (2013) as our generator input. Then, we go through the detector simulation and perform proper selection of supernova channels from the Monte Carlo result. In the end, we will use pulse shape discrimination (PSD) technique to improve the selection performance.
1 Introduction 1
1.1 Supernova Burst Neutrinos . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Characteristic Phases . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Detection channels . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.3 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 JUNO Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.1 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.1 Models of a Neuron . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.2 Multilayer Perceptrons . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.3 Tookit for Multivariate Data Analysis . . . . . . . . . . . . . . . 9
2 Supernova Neutrinos Simulation in JUNO 12
2.1 Numerical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Simulation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Full Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.2 Modifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Event Selection 18
3.0.1 Basic Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.0.2 Selection Strategy . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.0.3 Selection Performance . . . . . . . . . . . . . . . . . . . . . . . 21
3.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4 Pulse Shape Discrimination 24
4.1 Pulse Shape Characteristics . . . . . . . . . . . . . . . . . . . . . . . . 24
4.2 MLP Method in TMVA . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.2.1 Input Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2.2 Quantifying the Performance . . . . . . . . . . . . . . . . . . . . 27
4.3 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.3.1 Application to -p in Elastic Scattering Channel . . . . . . . . . 29
4.3.2 Application to 12C-CC Channel . . . . . . . . . . . . . . . . . . 31
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5 PSD with Full Simulation 35
5.1 Readout Time Window . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.1.1 Trigger System . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.1.2 Dark Noise Reduction . . . . . . . . . . . . . . . . . . . . . . . 36
5.2 PSD on Full Simulation Data . . . . . . . . . . . . . . . . . . . . . . . . 39
5.2.1 Input Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.2.2 Testing for p vs. e− and
. . . . . . . . . . . . . . . . . . . . . 41
5.2.3 Testing for e+ vs. e− . . . . . . . . . . . . . . . . . . . . . . . . 44
5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
6 Conclusions 47
[1] Zelimir Djurcic et al. JUNO Conceptual Design Report. 2015.
[2] Tao Lin. Parallelized juno simulation software based on sniper. JUNO-doc-2842-v1, page 2, 2017.
[3] Simon O. Haykin. Neural Networks and Learning Machines (3rd Edition). Prentice Hall, 2009.
[4] Qin Liu. Vertex reconstruction and energy reconstruction. JUNO-doc-990-v1, page 13, 2015.
[5] Hongxin Wang. Particle discrimination –multivariate data analysis. JUNO-doc-1359-v1, page 3, 2016.
[6] Carlo Giunti and Chung W. Kim. Fundamentals of Neutrino Physics and Astrophysics. Oxford University Press, 2007.
[7] Fengpeng An et al. Neutrino physics with juno. J. Phys., G43(3):030401, 2016.
[8] Kwang-Chang Lai, Fei-Fan Lee, Feng-Shiuh Lee, Guey-Lin Lin, Tsung-Che Liu, and Yi Yang. Probing Neutrino Mass Hierarchy by Comparing the Charged-Current and Neutral-Current Interaction Rates of Supernova Neutrinos. JCAP, 1607(07):039, 2016.
[9] Andreas Hoecker, Peter Speckmayer, Joerg Stelzer, Jan Therhaag, Eckhard von Toerne, and Helge Voss. TMVA: Toolkit for Multivariate Data Analysis. PoS, ACAT:040, 2007.
[10] Helge Voss. Data analysis with tmva. http://lphe.epfl.ch/seminar/extern/Voss_12_04_10.pdf, Apr. 2010.
[11] Ken’ichiro Nakazato, Kohsuke Sumiyoshi, Hideyuki Suzuki, Tomonori Totani, Hideyuki Umeda, and Shoichi Yamada. Supernova neutrino light curves and spectra for various progenitor stars: From core collapse to proto-neutron star cooling. 2012.
[12] Qian Sen. Production and testing of nnvt mcp-pmt. JUNO-doc-2174-v1, page 5, 2017.
[13] Ya-Ping Cheng, Liang-Jian Wen, Peng Zhang, and Xing-Zhong Cao. e+/e− Discrimination in Liquid Scintillator and Its Usage to Suppress 8He/9Li Backgrounds. Chin.Phys., C41(1):016101, 2017.
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