ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset

• URL: http://arxiv.org/abs/2512.15230v1
• Date: Wed, 17 Dec 2025 09:30:44 GMT
• Title: ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset
• Authors: Doğa Elitez, Paul Gessinger, Daniel Murnane, Marcus Selchou Raaholt, Andreas Salzburger, Stine Kofoed Skov, Andreas Stefl, Anna Zaborowska,
• Abstract summary: ColliderML is a dataset of fully simulated and digitised proton-proton collisions in High-Luminosity Large Hadron Collider conditions.<n>It provides one million events across ten Standard Model and Beyond Standard Model processes, plus extensive single-particle samples.<n>The release fills a major gap for machine learning (ML) research on detector-level data, provided on the ML-friendly Hugging Face platform.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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