通过迁移学习利用Jet taggers的通用性
Leveraging universality of jet taggers through transfer learning
摘要：
通过机器学习技术标记增强对象的一个重大挑战是与训练复杂模型相关的高昂计算成本。然而，QCD的普适性表明，在训练中学到的大量信息对于不同的物理信号和实验设置是共同的。在本文中，我们将探索使用迁移学习技术来开发快速且数据高效的Jet taggers，以利用这种通用性。我们考虑了图神经网络LundNet和ParticleNet，并引入了两个处方，将现有的tagger转换为一个新的信号，要么微调模型的所有权重，要么冻结其中的一小部分。在$W$ -玻色子和顶夸克标签的情况下，我们发现，可以获得可靠的标签使用一个数量级较少的数据与相应的速度加快的训练过程。此外，在保持训练数据集大小固定的同时，我们观察到训练速度提高了三倍。这提供了一个有前途的途径，以促进在对撞机物理实验中使用这些工具。
英文摘要：
A significant challenge in the tagging of boosted objects via machine-learning technology is the prohibitive computational cost associated with training sophisticated models. Nevertheless, the universality of QCD suggests that a large amount of the information learnt in the training is common to different physical signals and experimental setups. In this article, we explore the use of transfer learning techniques to develop fast and data-efficient jet taggers that leverage such universality. We consider the graph neural networks LundNet and ParticleNet, and introduce two prescriptions to transfer an existing tagger into a new signal based either on fine-tuning all the weights of a model or alternatively on freezing a fraction of them. In the case of $W$-boson and top-quark tagging, we find that one can obtain reliable taggers using an order of magnitude less data with a corresponding speed-up of the training process. Moreover, while keeping the size of the training data set fixed, we observe a speed-up of the training by up to a factor of three. This offers a promising avenue to facilitate the use of such tools in collider physics experiments.
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