ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915681506689024 |
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| author | Elitez, Doğa Gessinger, Paul Murnane, Daniel Raaholt, Marcus Selchou Salzburger, Andreas Skov, Stine Kofoed Stefl, Andreas Zaborowska, Anna |
| author_facet | Elitez, Doğa Gessinger, Paul Murnane, Daniel Raaholt, Marcus Selchou Salzburger, Andreas Skov, Stine Kofoed Stefl, Andreas Zaborowska, Anna |
| contents | We introduce ColliderML - a large, open, experiment-agnostic dataset of fully simulated and digitised proton-proton collisions in High-Luminosity Large Hadron Collider conditions ($\sqrt{s}=14$ TeV, mean pile-up $μ= 200$). ColliderML provides one million events across ten Standard Model and Beyond Standard Model processes, plus extensive single-particle samples, all produced with modern next-to-leading order matrix element calculation and showering, realistic per-event pile-up overlay, a validated OpenDataDetector geometry, and standard reconstructions. The release fills a major gap for machine learning (ML) research on detector-level data, provided on the ML-friendly Hugging Face platform. We present physics coverage and the generation, simulation, digitisation and reconstruction pipeline, describe format and access, and initial collider physics benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15230 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset Elitez, Doğa Gessinger, Paul Murnane, Daniel Raaholt, Marcus Selchou Salzburger, Andreas Skov, Stine Kofoed Stefl, Andreas Zaborowska, Anna High Energy Physics - Experiment Machine Learning Data Analysis, Statistics and Probability Instrumentation and Detectors We introduce ColliderML - a large, open, experiment-agnostic dataset of fully simulated and digitised proton-proton collisions in High-Luminosity Large Hadron Collider conditions ($\sqrt{s}=14$ TeV, mean pile-up $μ= 200$). ColliderML provides one million events across ten Standard Model and Beyond Standard Model processes, plus extensive single-particle samples, all produced with modern next-to-leading order matrix element calculation and showering, realistic per-event pile-up overlay, a validated OpenDataDetector geometry, and standard reconstructions. The release fills a major gap for machine learning (ML) research on detector-level data, provided on the ML-friendly Hugging Face platform. We present physics coverage and the generation, simulation, digitisation and reconstruction pipeline, describe format and access, and initial collider physics benchmarks. |
| title | ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset |
| topic | High Energy Physics - Experiment Machine Learning Data Analysis, Statistics and Probability Instrumentation and Detectors |
| url | https://arxiv.org/abs/2512.15230 |