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

TL;DR

ColliderML is a comprehensive, open dataset of simulated proton-proton collision events at the LHC, designed to advance machine learning research in high-energy physics with realistic, high-luminosity data.

Contribution

It introduces the first large-scale, open, experiment-agnostic dataset for HL-LHC conditions, including detailed simulation, digitisation, and reconstruction for ML applications.

Findings

Provides one million simulated events covering Standard Model and BSM processes.

Includes extensive single-particle samples with realistic pile-up overlay.

Initial collider physics benchmarks demonstrate dataset's utility.

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 $\mu = 200$). 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.