Köppel Marius, Segner Alexander, Wagener Martin, Pensel Lukas, Karwath Andreas, Schmitt Christian, Kramer Stefan
Johannes Gutenberg University, Mainz, Germany.
ETH, Zurich, Switzerland.
Sci Rep. 2022 Jul 30;12(1):13094. doi: 10.1038/s41598-022-10383-w.
In the extensive search for new physics, the precise measurement of the Higgs boson continues to play an important role. To this end, machine learning techniques have been recently applied to processes like the Higgs production via vector-boson fusion. In this paper, we propose to use algorithms for learning to rank, i.e., to rank events into a sorting order, first signal, then background, instead of algorithms for the classification into two classes, for this task. The fact that training is then performed on pairwise comparisons of signal and background events can effectively increase the amount of training data due to the quadratic number of possible combinations. This makes it robust to unbalanced data set scenarios and can improve the overall performance compared to pointwise models like the state-of-the-art boosted decision tree approach. In this work we compare our pairwise neural network algorithm, which is a combination of a convolutional neural network and the DirectRanker, with convolutional neural networks, multilayer perceptrons or boosted decision trees, which are commonly used algorithms in multiple Higgs production channels. Furthermore, we use so-called transfer learning techniques to improve overall performance on different data types.
在对新物理的广泛探索中，希格斯玻色子的精确测量继续发挥着重要作用。为此，机器学习技术最近已应用于诸如通过矢量玻色子融合产生希格斯玻色子的过程。在本文中，我们建议使用用于学习排序的算法，即将事件按排序顺序排列，先信号后背景，而不是用于此任务的两类分类算法。由于可能组合的数量呈二次方增长，因此在信号和背景事件的成对比较上进行训练这一事实可以有效地增加训练数据量。这使其对不平衡数据集场景具有鲁棒性，并且与诸如最新的增强决策树方法等逐点模型相比，可以提高整体性能。在这项工作中，我们将我们的成对神经网络算法（它是卷积神经网络和DirectRanker的组合）与卷积神经网络、多层感知器或增强决策树进行比较，这些是多个希格斯玻色子产生通道中常用的算法。此外，我们使用所谓的迁移学习技术来提高在不同数据类型上的整体性能。
