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的几个方面的理解仍然是由70‘S和80’S泡沫室时代仅记录的几百个事件的小样本以及少量现代的、高统计但低分辨率的测量推动的。即将上线的液体-Ar(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新一代的进步。到本十年结束时，对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短基线方案，特别是来自于2020年底开始数据采集操作的短基线实验。该项目的目标是对具有0-介子和0、1、2或更多处于终态的质子的独有CC相互作用通道进行精确测量。这是一组关键的测量，将使我们能够绘制核响应函数图，将真正的(裸)CCQE相互作用从多核子相互作用和非弹性背景中分离出来，并改进物理模型的系统性，这些模型将为沙丘早期开发阶段的CP搜索提供信息。中微子相互作用的准确重建，特别是宇宙的去除(对在表面工作的LArTPC探测器来说是一个真正的挑战)，相互作用顶点的重建，以及相互作用顶点附近非常短的质子轨迹的识别和重建，将对这一项目至关重要。该项目的一个重要部分将是开发基于深度学习方法的新的中微子事件重建方法，利用SBND数据对这些方法进行验证，并在沙丘部署这些方法。

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.