Deep Learning Applications for Neutrino Event Reconstruction in Liquid Argon Time Projection Chamber Detectors and Measurement of Zero-Pion Production

对中微子相互作用物理学的几个方面的理解仍然是由70年代和80年代气泡室时代记录的几百个事件的小样本和少量现代高统计但低分辨率的测量所驱动的。现在即将上线的液态氩（LAr）时间投影室（TPC）探测器提供了与气泡室实验相当的成像能力，但统计数据要高得多，它们将为中微子研究带来一代人的进步。到2020年年底，中微子相互作用特性的一些最精确的测量将来自费米实验室的短基线（SBN）计划，特别是从2020年底开始数据采集操作的SBND实验。该项目的目标是精确测量具有0-π介子和具有0，1，2或更多最终状态质子的排他性CC相互作用通道。这是一组关键的测量，将使我们能够映射核响应函数，从多核子相互作用和非弹性背景中解开真正的（裸）CCQE相互作用，并改善物理模型的系统性，这将在DUNE的早期开发阶段通知CP搜索。中微子相互作用的精确重建，特别是宇宙的去除（一个真实的挑战LArTPC探测器上运行的表面），相互作用顶点的重建，以及识别和重建非常短的质子轨道附近的相互作用顶点将是至关重要的这个项目。该项目的一个重要部分将是开发基于深度学习方法的新中微子事件重建方法，验证这些方法对SBND数据的有效性，并将其部署在DUNE中。

The understanding of several aspects of neutrino interaction physics is still driven by small samples of just a few hundred events recorded in the bubble chamber era in the 70's and 80's, and by a small number of modern, high-statistics but low-resolution measurements. Liquid-Argon (LAr) Time Projection Chamber (TPC) detectors that are now coming online offer imaging capability comparable to that of bubble chamber experiments but much higher statistics and they will bring a generational advance in neutrino studies. By the end of this decade, some of most precise measurements of neutrino interaction characteristics will come from the Fermilab short-baseline (SBN) programme and, in particular, from the SBND experiment starting data-taking operation in late 2020. The goal of this project is to perform precision measurements of exclusive CC interaction channels with 0-pions and with 0, 1, 2 or more protons in the final state. This is a key set of measurements that will enable us to map the nuclear response function, to disentangle genuine (bare) CCQE interactions from multi-nucleon interactions and inelastic backgrounds, and to improve the systematics of the physics models that will inform the CP searches in the early exploitation phase of DUNE. The accurate reconstruction of neutrino interactions and, in particular the removal of cosmics (a real challenge for a LArTPC detector operating on surface), the reconstruction of the interaction vertex, and the identification and reconstruction of very short proton tracks near the interaction vertex will be crucially important for this project. An important part of this project will be the development of new neutrino event reconstruction methods based on Deep Learning approaches, the validation of these methods against SBND data, and their deployment in DUNE.