{"ID":2862870,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26411","arxiv_id":"2509.26411","title":"TrackFormers Part 2: Enhanced Transformer-Based Models for High-Energy Physics Track Reconstruction","abstract":"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.","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2509.26411","url_pdf":"https://arxiv.org/pdf/2509.26411v2","authors":"[\"Sascha Caron\",\"Nadezhda Dobreva\",\"Maarten Kimpel\",\"Uraz Odyurt\",\"Slav Pshenov\",\"Roberto Ruiz de Austri Bazan\",\"Eugene Shalugin\",\"Zef Wolffs\",\"Yue Zhao\"]","published":"2025-09-30T15:38:48Z","proceeding":"hep-ex","tasks":"[\"hep-ex\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}