标题： 深度图像重构用于重离子碰撞中的背景减除 显示英文标题

标题： Deep Image Reconstruction for Background Subtraction in Heavy-Ion Collisions

摘要： 在超相对论重离子碰撞中，喷注重建受到一个著名的大且波动的热背景的困扰。传统的背景减法方法难以去除这种软背景，同时保留喷注的硬子结构。在本文中，我们提出了DeepSub，一种基于机器学习的完整事件背景减法方法。DeepSub利用基于Swin Transformer层的模型来消除喷注图像中的噪声，并将硬喷注与重离子背景分离。DeepSub通过再现关键的喷注可观测量，如喷注$p_\mathrm{T}$和质量，以及子结构可观测量，如周长和能量关联函数，在亚百分比到百分比的水平上显著优于现有的减法技术。因此，DeepSub为在以前无法触及的动量范围进行精确的重离子测量铺平了道路。 显示英文摘要

摘要： Jet reconstruction in an ultra-relativistic heavy-ion collision suffers from a notoriously large, fluctuating thermal background. Traditional background subtraction methods struggle to remove this soft background while preserving the jet's hard substructure. In this Letter, we present DeepSub, the first machine learning-based approach for full-event background subtraction. DeepSub utilizes a model based on Swin Transformer layers to denoise jet images and disentangle hard jets from the heavy-ion background. DeepSub significantly outperforms existing subtraction techniques by reproducing key jet observables such as jet $p_\mathrm{T}$ and mass, and substructure observables such as girth and the energy correlation function, at the sub-percent to percent level. As such, DeepSub paves the way for precision heavy-ion measurements in hitherto inaccessible kinematic regimes.