PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction
CoRR(2024)
摘要
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.
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