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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2502.04193 |
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| _version_ | 1866913681639473152 |
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| author | Melennec, Matthieu Ghosh, Shamik Magniette, Frédéric |
| author_facet | Melennec, Matthieu Ghosh, Shamik Magniette, Frédéric |
| contents | In the recent years, high energy physics discoveries have been driven by the increasing of luminosity and/or detector granularity. This evolution gives access to bigger statistics and data samples, but can make it hard to process results with current methods and algorithms. Graph convolution networks, have been shown to be powerful tools to address these challenges. We present our graph convolution framework for particle identification and energy regression in high granularity calorimeters. In particular, we introduce our algorithm for optimised graph construction in resource constrained environments. We also introduce our implementation of graph convolution and pooling layers. We observe satisfying accuracies, and discuss possible application to other high granularity particle detector challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimised Graph Convolution for Calorimetry Event Classification Melennec, Matthieu Ghosh, Shamik Magniette, Frédéric High Energy Physics - Experiment In the recent years, high energy physics discoveries have been driven by the increasing of luminosity and/or detector granularity. This evolution gives access to bigger statistics and data samples, but can make it hard to process results with current methods and algorithms. Graph convolution networks, have been shown to be powerful tools to address these challenges. We present our graph convolution framework for particle identification and energy regression in high granularity calorimeters. In particular, we introduce our algorithm for optimised graph construction in resource constrained environments. We also introduce our implementation of graph convolution and pooling layers. We observe satisfying accuracies, and discuss possible application to other high granularity particle detector challenges. |
| title | Optimised Graph Convolution for Calorimetry Event Classification |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2502.04193 |