论文标题
使用机器学习解决粒子围墙的组合问题
Solving Combinatorial Problems at Particle Colliders Using Machine Learning
论文作者
论文摘要
在标准模型过程及其他地区，可能会出现粒子山脉界面的高多性特征。通过这种签名，运动空间的巨大维度通常会引起困难。对于包含单一类型粒子标志的最终状态，这会导致组合问题隐藏了基础运动信息。我们使用包含洛伦兹层的神经网络探索以提取高维相关性。我们将squark衰变的情况以$ r $ -Parity-Parity-Parity-actymatimatimemememetry作为基准，将性能与经典方法的性能进行了比较。通过这种方法，我们表现出对传统方法的显着改善。
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.
