零排列喷射-部分子分配使用自注意力网络
Zero-Permutation Jet-Parton Assignment using a Self-Attention Network
AI总结：
本文提出自注意力网络SaJa，用于零排列地解决喷注-部分子分配问题，在全强子$t\ar{t}$事件中优于基于似然的方法。
摘要：
在高能粒子物理事件中，找到与中间态衰变相关的喷注（例如，由顶夸克强子衰变产生的三个喷注）是有利的。通常，构建一个关联优度度量，如与关联喷注质量相关的$χ^2$，并通过优化该度量来找到最佳喷注组合。由于该过程随喷注数量出现组合爆炸，通过仅使用$n$个最高$p_T$喷注来限制排列数量。自注意力块是用于神经机器翻译问题的神经网络单元，它可以在单次迭代中突出任意数量输入之间的关系，而无需排列。在本文中，我们引入了用于喷注分配的自我注意力（SaJa）网络。SaJa可以接受任意数量的喷注作为输入，并在单步中输出所有喷注的喷注-部分子分配概率。我们将SaJa应用于寻找全强子$t\ar{t}$事件的喷注-部分子分配以评估性能。我们表明，SaJa比基于似然的方法实现了更好的性能。
英文摘要：
In high-energy particle physics events, it can be advantageous to find the jets associated with the decays of intermediate states, for example, the three jets produced by the hadronic decay of the top quark. Typically, a goodness-of-association measure, such as a $χ^2$ related to the mass of the associated jets, is constructed, and the best jet combination is found by optimizing this measure. As this process suffers from a combinatorial explosion with the number of jets, the number of permutations is limited by using only the $n$ highest $p_T$ jets. The self-attention block is a neural network unit used for the neural machine translation problem, which can highlight relationships between any number of inputs in a single iteration without permutations. In this paper, we introduce the Self-Attention for Jet Assignment (SaJa) network. SaJa can take any number of jets for input and outputs probabilities of jet-parton assignment for all jets in a single step. We apply SaJa to find jet-parton assignments of fully-hadronic $t\bar{t}$ events to evaluate the performance. We show that SaJa achieves better performance than a likelihood-based approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
