Development of an optimised event reconstruction for the Deep Underground Neutrino Experiment using machine learning and a multi-algorithm approach

Mousam将在潘多拉框架[1]中开发模式识别算法，以重建将由深地中微子实验(Dune)部署的液体-Ar时间投影室(LArTPC)中的中微子诱导事件[2]。沙丘被设计用来帮助回答基础物理学中最重要的悬而未决的问题：“我们宇宙中物质-反物质不对称的起源是什么？”它还将提供控制中微子振荡的参数的精确测量，并有可能记录来自核心坍塌超新星的中微子爆发，提供丰富的新信息。LArTPC模式识别是现代高能物理中最具挑战性的问题之一，也是对沙丘的关键贡献。LArTPC提供了中微子相互作用中产生的带电粒子的“照片质量”图像。图像可能非常复杂，混合了重叠的轨迹状和淋浴状的拓扑结构。虽然人脑通常可以挑选出关键特征，但开发自动化的算法解决方案是一个巨大的挑战。模式识别是沙丘工作流程中详细检查LArTPC图像的单一步骤，因此图像中的信息被完全提取是至关重要的。潘多拉项目倡导一种分析LArTPC图像的“多算法”方法，在该方法中，每个算法都在事件拓扑中寻找特定特征。许多算法精心构建了一幅事件的图景，并共同提供了稳健的重建。潘多拉目前提供了最先进和最有文件证明的LArTPC重建，它被国际中微子物理界广泛使用。Mousam将致力于开发新的模式识别算法，以在DUNEP上识别事件的特征。首先，Mousam将专注于了解沙丘事件的潘多拉模式识别的当前性能，找出任何弱点，并设计算法来解决特定拓扑的任何问题。Mousam将越来越专注于使用机器学习方法来驱动模式识别算法做出的决策。他将开发使用机器学习的算法，将沙丘事件中的个别命中归类为来自轨迹状或阵雨状粒子。他将致力于确定沙丘事件中中微子相互作用顶点的位置，并进行第一次研究，以确定次级、下游相互作用的顶点。他将确保从机器学习方法中提取的信息得到有效利用，以推动更高性能的模式识别。Mousam最终将开发一项物理分析，使用模式识别输出的详细知识来优化事件选择，并评估沙丘对控制中微子振荡和/或中微子部门CP破坏的参数的敏感性。太棒了。J.C(2018)78：82[2]arxiv：1512.06148[Physiics.Ins-Det]

Mousam will develop pattern-recognition algorithms in the Pandora framework[1], to reconstruct neutrino-induced events in the liquid-argon time-projection chambers (LArTPCs) to be deployed by the Deep Underground Neutrino Experiment (DUNE)[2]. DUNE is designed to help answer arguably the most important outstanding question of fundamental physics: "What is the origin of the matter-antimatter asymmetry in our Universe?". It will also provide precision measurements of the parameters governing neutrino oscillations and it has the potential to record a burst of neutrinos from a core-collapse supernova, providing a wealth of new information.LArTPC pattern recognition is one of the most challenging problems in modern high energy physics and is a critical contribution to DUNE. LArTPCs provide "photograph quality" images of the charged particles produced in neutrino interactions. The images can be extremely complex, with a mix of overlapping track-like and shower-like topologies. Whilst the human brain can usually pick out the key features, it is a significant challenge to develop an automated, algorithmic solution. The pattern recognition is the single step in the DUNE workflow in which LArTPC images are examined in detail, so it is vital that information in the images is fully extracted.The Pandora project champions a "multi-algorithm" approach to analysing LArTPC images, in which individual algorithms each look for specific features in event topologies. Many tens of algorithms carefully build up a picture of events and collectively provide a robust reconstruction. Pandora currently offers the most advanced and best documented LArTPC reconstruction and it is used extensively by the international neutrino physics community. Mousam will be working to develop novel pattern-recognition algorithms to identify features in events at DUNE.In the first instance, Mousam will focus on understanding the current performance of the Pandora pattern recognition for DUNE events, identifying any weaknesses and designing algorithms to address any issues with specific topologies. Mousam will increasingly focus on the use of machine-learning approaches to drive the decisions made by pattern-recognition algorithms. He will develop algorithms that use machine-learning to classify individual hits in DUNE events as originating from track-like or shower-like particles. He will work to identify the positions of neutrino interaction vertices in DUNE events and perform the first studies to identify the vertices of secondary, downstream interactions. He will ensure the information extracted from machine-learning approaches is exploited effectively to drive a more performant pattern recognition.Mousam will ultimately develop a physics analysis, using detailed knowledge of the pattern-recognition outputs to optimise selection of events and assess the sensitivity of DUNE to the parameters governing neutrino oscillations and/or CP violation in the neutrino sector.[1] Eur. Phys. J. C (2018) 78: 82[2] arXiv:1512.06148 [physics.ins-det]