TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914284637782016 |
|---|---|
| author | Soybelman, Nathalie Di Bello, Francesco A. Kakati, Nilotpal Gross, Eilam |
| author_facet | Soybelman, Nathalie Di Bello, Francesco A. Kakati, Nilotpal Gross, Eilam |
| contents | Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing machine learning approaches address this problem but rely on a single event topology, restricting their applicability to realistic analyses where multiple signal and background processes with different structures are present. To overcome this, we present TIGER, a novel hierarchical graph network that is fundamentally topology-agnostic. By incorporating only the common underlying structure of sequential two-body decays, our model can reconstruct complex events without process-specific assumptions. This flexible architecture supports multi-task learning, enabling simultaneous event reconstruction and classification. TIGER thus provides a powerful and generalizable tool for physics analysis at the LHC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08162 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction Soybelman, Nathalie Di Bello, Francesco A. Kakati, Nilotpal Gross, Eilam High Energy Physics - Experiment Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing machine learning approaches address this problem but rely on a single event topology, restricting their applicability to realistic analyses where multiple signal and background processes with different structures are present. To overcome this, we present TIGER, a novel hierarchical graph network that is fundamentally topology-agnostic. By incorporating only the common underlying structure of sequential two-body decays, our model can reconstruct complex events without process-specific assumptions. This flexible architecture supports multi-task learning, enabling simultaneous event reconstruction and classification. TIGER thus provides a powerful and generalizable tool for physics analysis at the LHC. |
| title | TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2510.08162 |