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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.11538 |
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| _version_ | 1866929599378620416 |
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| author | Lu, Junjian Liu, Siwei Kobylianski, Dmitrii Dreyer, Etienne Gross, Eilam Liang, Shangsong |
| author_facet | Lu, Junjian Liu, Siwei Kobylianski, Dmitrii Dreyer, Etienne Gross, Eilam Liang, Shangsong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction Lu, Junjian Liu, Siwei Kobylianski, Dmitrii Dreyer, Etienne Gross, Eilam Liang, Shangsong High Energy Physics - Phenomenology Machine Learning 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. |
| title | PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction |
| topic | High Energy Physics - Phenomenology Machine Learning |
| url | https://arxiv.org/abs/2402.11538 |