构建首个高亮度对撞机探测器级数据集，支持机器学习研究
ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset
- 基于标准模型与新物理过程生成百万级模拟事件
- 包含真实堆叠效应和探测器响应，覆盖10类物理过程
- 开源开放，适配机器学习研究，平台兼容性好
我们介绍ColliderML——首个在高亮度大型强子对撞机条件（√s=14 TeV，平均堆叠数μ=200）下，通过完整模拟与数字化处理生成的开放、实验无关的质子-质子碰撞数据集。该数据集包含一百万事件，涵盖十类标准模型与超越标准模型的物理过程，以及大量单粒子样本，均采用先进的下一领头阶矩阵元计算与喷注演化方法生成，引入真实每事件堆叠叠加，并基于已验证的OpenDataDetector几何结构与标准重建流程。此次发布填补了机器学习研究在探测器级数据上的空白，数据通过友好支持机器学习的Hugging Face平台提供。本文介绍了物理覆盖范围、生成与重建流程、数据格式与访问方式，并给出了初步的对撞机物理基准测试。
原文摘要 · Abstract (English)
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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