论文标题
在Dilepton中解决组合歧义$ t \ bar t $事件拓扑与神经网络
Resolving Combinatorial Ambiguities in Dilepton $t \bar t$ Event Topologies with Neural Networks
论文作者
论文摘要
我们研究了深度学习在LHC上使用两个看不见的颗粒解决SUSY样事件中组合问题的潜力。作为一个具体的例子，我们专注于dialptonic $ t \ bar t $事件，其中组合问题成为二进制分类的问题：将正确的Lepton与每个$ B $ Quark配对。我们研究了许多机器学习算法的性能，包括基于注意力的网络，这些网络已在$ t \ bar t $生产的全部频道中用于类似问题；洛伦兹（Lorentz）增强网络，这是由物理原理激励的。然后，我们考虑了基础质谱尚不清楚的一般情况，因此没有运动端点信息。与基于运动学变量的现有方法相比，我们证明选择正确配对的效率可以通过使用深度学习技术可大大提高。
We study the potential of deep learning to resolve the combinatorial problem in SUSY-like events with two invisible particles at the LHC. As a concrete example, we focus on dileptonic $t \bar t$ events, where the combinatorial problem becomes an issue of binary classification: pairing the correct lepton with each $b$ quark coming from the decays of the tops. We investigate the performance of a number of machine learning algorithms, including attention-based networks, which have been used for a similar problem in the fully-hadronic channel of $t\bar t$ production; and the Lorentz Boost Network, which is motivated by physics principles. We then consider the general case when the underlying mass spectrum is unknown, and hence no kinematic endpoint information is available. Compared against existing methods based on kinematic variables, we demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing deep learning techniques.
