mlsanityData to 14 Oct 2026

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

arXiv 2512.15230 ↗First posted 17 Dec 2025hep-ex cs.LG physics.data-ancited by 3

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Abstract (arXiv)

We introduce ColliderML - a large, open, experiment-agnostic dataset of fully simulated and digitised proton-proton collisions in High-Luminosity Large Hadron Collider conditions (√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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 20 of its 68 references that are in the map. First posted on arXiv 17 Dec 2025.

Builds on

20 of 68 references.

References · 20

  1. 2015Approximating Likelihood Ratios with Calibrated Discriminative Classifierscited by 90
  2. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  3. 2021Particle Cloud Generation with Message Passing Generative Adversarial Networkscited by 46
  4. 2024Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Modelscited by 46
  5. 2024CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulationcited by 35
  6. 2021CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 34
  7. 2019OmniFold: A Method to Simultaneously Unfold All Observablescited by 31
  8. 2021Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Trackingcited by 30
  9. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 29
  10. 2018Constraining Effective Field Theories with Machine Learningcited by 26
  11. 2020Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image datacited by 24
  12. 2023Explainable Equivariant Neural Networks for Particle Physics: PELICANcited by 24
8 more
  1. 2023EPiC-GAN: Equivariant Point Cloud Generation for Particle Jetscited by 23
  2. 2021Charged particle tracking via edge-classifying interaction networkscited by 19
  3. 2021The Tracking Machine Learning challenge : Throughput phasecited by 12
  4. 2024A Language Model for Particle Trackingcited by 10
  5. 2023Hierarchical Graph Neural Networks for Particle Track Reconstructioncited by 7
  6. 2022Semi-Equivariant GNN Architectures for Jet Taggingcited by 5
  7. 2023Equivariant Graph Neural Networks for Charged Particle Trackingcited by 5
  8. 2023Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Taskscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Taskscited by 2
  2. 2026DCTracks: An Open Dataset for Machine Learning-Based Drift Chamber Track Reconstructioncited by 0
  3. 2026Fast and Precise Learned Charged-Particle Trajectory Regression at the Large Hadron Collidercited by 0