波形鑑別在JUNO探測器中超新星微中子事例上的應用

Abstract

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.