论文标题
在强子山区的新型事件描述中的最佳运输
Optimal transport for a novel event description at hadron colliders
论文作者
论文摘要
我们提出了一种新型的策略，以在LHC等强子山脉（例如LHC）上解散质子碰撞，该策略在目前的最新水平状态上大大改善了。我们的算法采用了受到最佳传输问题为图神经网络的成本函数启发的度量，我们的算法能够比较两个具有不同噪声水平的粒子集合，并学会了构造源自主要相互作用的粒子，该粒子源自主要相互作用的同时堆积堆积。因此，我们避开了通过标记粒子来获得地面真理的关键任务，并避免通过自我监督的过程来定位的标签，避免了艰苦的人类注释。我们展示了我们的方法 - 与竞争算法不同，实施的方法是如何改善精确测量和搜索中使用的关键对象的分辨率，并提高了较大的敏感性增长，以寻找高露珠性LHC处的外来希格斯玻色子衰减。
We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric inspired by optimal transport problems as the cost function of a graph neural network, our algorithm is able to compare two particle collections with different noise levels and learns to flag particles originating from the main interaction amidst products from up to 200 simultaneous pileup collisions. We thereby sidestep the critical task of obtaining a ground truth by labeling particles and avoid arduous human annotation in favor of labels derived in situ through a self-supervised process. We demonstrate how our approach - which, unlike competing algorithms, is trivial to implement - improves the resolution in key objects used in precision measurements and searches alike and present large sensitivity gains in searching for exotic Higgs boson decays at the High-Luminosity LHC.
