In high-energy physics, particles produced in collision events decay in a
format of a hierarchical tree structure, where only the final decay products
can be observed using detectors. However, the large combinatorial space of
possible tree structures makes it challenging to recover the actual decay
process given a set of final particles. To better analyse the hierarchical tree
structure, we propose a graph-based deep learning model to infer the tree
structure to reconstruct collision events. In particular, we use a compact
matrix representation termed as lowest common ancestor generations (LCAG)
matrix, to encode the particle decay tree structure. Then, we introduce a
perturbative augmentation technique applied to node features, aiming to mimic
experimental uncertainties and increase data diversity. We further propose a
supervised graph contrastive learning algorithm to utilize the information of
inter-particle relations from multiple decay processes. Extensive experiments
show that our proposed supervised graph contrastive learning with perturbative
augmentation (PASCL) method outperforms state-of-the-art baseline models on an
existing physics-based dataset, significantly improving the reconstruction
accuracy. This method provides a more effective training strategy for models
with the same parameters and makes way for more accurate and efficient
high-energy particle physics data analysis.

通过引入基于图的深度学习模型，使用最低共同祖先矩阵（LCAG）对粒子衰变树结构进行编码，并结合扰动增强技术以及监督图对比学习算法来提高高能物理数据分析的衰变事件重建准确性。

PASCL：基于扰动增强的监督对比学习用于粒子衰变重建

PASCL: Supervised Contrastive Learning with Perturbative Augmentation  for Particle Decay Reconstruction

Graphs are present in many real-world applications, such as financial fraud
detection, commercial recommendation, and social network analysis. But given
the high cost of graph annotation or labeling, we face a severe graph
label-scarcity problem, i.e., a graph might have a few labeled nodes. One
example of such a problem is the so-called \textit{few-shot node
classification}. A predominant approach to this problem resorts to
\textit{episodic meta-learning}. In this work, we challenge the status quo by
asking a fundamental question whether meta-learning is a must for few-shot node
classification tasks. We propose a new and simple framework under the standard
few-shot node classification setting as an alternative to meta-learning to
learn an effective graph encoder. The framework consists of supervised graph
contrastive learning with novel mechanisms for data augmentation, subgraph
encoding, and multi-scale contrast on graphs. Extensive experiments on three
benchmark datasets (CoraFull, Reddit, Ogbn) show that the new framework
significantly outperforms state-of-the-art meta-learning based methods.

本篇研究研究了在说名高成本的标注或标签问题下，如何使用监督图形对比学习，数据增强，子图编码和多尺度对比等机制，实现图形节点分类任务的有效编码器。实验表明，相比基于元学习的方法，这个新的框架可以显著提高少样本节点分类问题的性能。