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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.26411 |
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| _version_ | 1866914392088510464 |
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| author | Caron, Sascha Dobreva, Nadezhda Kimpel, Maarten Odyurt, Uraz Pshenov, Slav Bazan, Roberto Ruiz de Austri Shalugin, Eugene Wolffs, Zef Zhao, Yue |
| author_facet | Caron, Sascha Dobreva, Nadezhda Kimpel, Maarten Odyurt, Uraz Pshenov, Slav Bazan, Roberto Ruiz de Austri Shalugin, Eugene Wolffs, Zef Zhao, Yue |
| contents | High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data necessitates critical revisions across the data processing pipeline, with particle track reconstruction being a prime candidate for improvement. In our previous work, we introduced "TrackFormers", a collection of Transformer-based one-shot encoder-only models that effectively associate hits with expected tracks. In this study, we extend our earlier efforts by conducting detailed investigations into more custom Transformer attention mechanisms, a new design combining geometric projection and lightweight clustering, and a joint model conditioning classification on a regressor's predictions. Furthermore, we discuss new datasets that allow the training on hit level for a range of physics processes. These developments collectively aim to boost both the accuracy and potentially the efficiency of our tracking models, offering a robust solution to meet the demands of next-generation high-energy physics experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26411 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TrackFormers Part 2: Enhanced Transformer-Based Models for High-Energy Physics Track Reconstruction Caron, Sascha Dobreva, Nadezhda Kimpel, Maarten Odyurt, Uraz Pshenov, Slav Bazan, Roberto Ruiz de Austri Shalugin, Eugene Wolffs, Zef Zhao, Yue High Energy Physics - Experiment Machine Learning High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data necessitates critical revisions across the data processing pipeline, with particle track reconstruction being a prime candidate for improvement. In our previous work, we introduced "TrackFormers", a collection of Transformer-based one-shot encoder-only models that effectively associate hits with expected tracks. In this study, we extend our earlier efforts by conducting detailed investigations into more custom Transformer attention mechanisms, a new design combining geometric projection and lightweight clustering, and a joint model conditioning classification on a regressor's predictions. Furthermore, we discuss new datasets that allow the training on hit level for a range of physics processes. These developments collectively aim to boost both the accuracy and potentially the efficiency of our tracking models, offering a robust solution to meet the demands of next-generation high-energy physics experiments. |
| title | TrackFormers Part 2: Enhanced Transformer-Based Models for High-Energy Physics Track Reconstruction |
| topic | High Energy Physics - Experiment Machine Learning |
| url | https://arxiv.org/abs/2509.26411 |