用机器学习解决粒子对撞机上的组合问题
Solving Combinatorial Problems at Particle Colliders Using Machine Learning
摘要：
粒子对撞机上的高多重性特征可能出现在标准模型过程中或更高。对于这样的签名，困难往往来自运动空间的大维度。对于包含单一类型的粒子签名的最终状态，这导致隐藏潜在运动学信息的组合问题。我们探索使用包括洛伦兹层的神经网络来提取高维相关性。我们使用的情况下，夸克衰变$R$宇称破坏超对称性作为基准，比较性能的经典方法。通过这种方法，我们证明了显着的改进，传统的方法。
英文摘要：
High-multiplicity signatures at particle colliders can arise in Standard Model processes and beyond. With such signatures, difficulties often arise from the large dimensionality of the kinematic space. For final states containing a single type of particle signature, this results in a combinatorial problem that hides underlying kinematic information. We explore using a neural network that includes a Lorentz Layer to extract high-dimensional correlations. We use the case of squark decays in $R$-Parity-violating Supersymmetry as a benchmark, comparing the performance to that of classical methods. With this approach, we demonstrate significant improvement over traditional methods.
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