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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/2508.19190 |
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| _version_ | 1866918497519403008 |
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| author | Caron, Sascha Moskvitina, Polina de Austri, Roberto Ruiz Shalugin, Eugene |
| author_facet | Caron, Sascha Moskvitina, Polina de Austri, Roberto Ruiz Shalugin, Eugene |
| contents | We present Higgsformer, a transformer-based architecture that classifies Higgs events at the Large Hadron Collider directly from raw inner tracker hits, bypassing the traditional reconstruction chain of intermediate physics objects. As a benchmark, we focus on distinguishing $t\bar{t}H$ from $t\bar{t}$ events with $H \to b\bar{b}$, a particularly challenging task due to their similar final state topologies. Our pipeline begins with event generation in Pythia8, fast simulation with ACTS/Fatras, and classification directly from raw detector hits.
We show for the first time that a transformer model originally developed for inner tracker hit-to-track assignment can be retrained to classify Higgs signal events directly from raw hits. For comparison, we reconstruct the same events with Delphes and train a Particle Transformer as an object-based classifier. We evaluate both approaches under varying dataset sizes and pileup levels. Despite relying exclusively on inner tracker hits, our large Higgsformer achieves an AUC of $0.855$, matching the performance of the traditional reconstruction pipeline at a $b$-tagging efficiency of $\approx 40\%$ under the same detector constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hits to Higgs: Hit-Level Higgs Classification from Raw LHC Detector Data Using Higgsformer Caron, Sascha Moskvitina, Polina de Austri, Roberto Ruiz Shalugin, Eugene High Energy Physics - Phenomenology We present Higgsformer, a transformer-based architecture that classifies Higgs events at the Large Hadron Collider directly from raw inner tracker hits, bypassing the traditional reconstruction chain of intermediate physics objects. As a benchmark, we focus on distinguishing $t\bar{t}H$ from $t\bar{t}$ events with $H \to b\bar{b}$, a particularly challenging task due to their similar final state topologies. Our pipeline begins with event generation in Pythia8, fast simulation with ACTS/Fatras, and classification directly from raw detector hits. We show for the first time that a transformer model originally developed for inner tracker hit-to-track assignment can be retrained to classify Higgs signal events directly from raw hits. For comparison, we reconstruct the same events with Delphes and train a Particle Transformer as an object-based classifier. We evaluate both approaches under varying dataset sizes and pileup levels. Despite relying exclusively on inner tracker hits, our large Higgsformer achieves an AUC of $0.855$, matching the performance of the traditional reconstruction pipeline at a $b$-tagging efficiency of $\approx 40\%$ under the same detector constraints. |
| title | Hits to Higgs: Hit-Level Higgs Classification from Raw LHC Detector Data Using Higgsformer |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2508.19190 |