Image decomposition plays a crucial role in various computer vision tasks, enabling the analysis and manipulation of visual content at a fundamental level. Overlapping images, which occur when multiple objects or scenes partially occlude each other, pose unique challenges for decomposition algorithms. The task intensifies when working with sparse images, where the scarcity of meaningful information complicates the precise extraction of components. This paper presents a solution that leverages the power of deep learning to accurately extract individual objects within multi-dimensional overlapping-sparse images, with a direct application in high-energy physics with decomposition of overlaid elementary particles obtained from imaging detectors. In particular, the proposed approach tackles a highly complex yet unsolved problem: identifying and measuring independent particles at the vertex of neutrino interactions, where one expects to observe detector images with multiple indiscernible overlapping charged particles. By decomposing the image of the detector activity at the vertex through deep learning, it is possible to infer the kinematic parameters of the identified low-momentum particles - which otherwise would remain neglected - and enhance the reconstructed energy resolution of the neutrino event. We also present an additional step - that can be tuned directly on detector data - combining the above method with a fully-differentiable generative model to improve the image decomposition further and, consequently, the resolution of the measured parameters, achieving unprecedented results. This improvement is crucial for precisely measuring the parameters that govern neutrino flavour oscillations and searching for asymmetries between matter and antimatter.

翻译：图像分解在众多计算机视觉任务中扮演着关键角色，能够在基础层面实现视觉内容的分析与处理。当多个物体或场景相互部分遮挡时产生的重叠图像，为分解算法带来了独特挑战。而在处理稀疏图像时，有效信息的稀缺使得精确提取组成部分更加困难。本文提出了一种解决方案，利用深度学习的能力在多维重叠稀疏图像中准确提取单个物体，并直接应用于高能物理领域——通过对成像探测器获取的重叠基本粒子进行分解。具体而言，所提出的方法解决了一个高度复杂且尚未解决的问题：识别并测量中微子相互作用顶点处的独立粒子，在这里通常可观测到包含多个难以分辨的重叠带电粒子的探测器图像。通过深度学习对顶点处探测器活动图像进行分解，能够推断出已识别的低动量粒子的运动学参数——否则这些粒子将被忽略——从而提升中微子事件的重建能量分辨率。我们还引入了一个可基于探测器数据直接调整的额外步骤，将上述方法与全可微生成模型相结合，进一步优化图像分解效果，进而提升测量参数的分辨率，实现前所未有的成果。这一改进对于精确测量控制中微子味振荡的参数以及探寻物质与反物质之间的不对称性至关重要。