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ColliderML-Release-1

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ColliderML-Release-1 / README.md
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metadata
license: cc-by-4.0
task_categories:
  - other
tags:
  - physics
  - high-energy-physics
  - particle-physics
  - collider-physics
  - tracking
  - calorimetry
  - machine-learning
  - simulation
  - particle-tracking
  - jet-tagging
pretty_name: ColliderML Dataset Release 1
size_categories:
  - 100K<n<1M
configs:
  - config_name: dihiggs_pu0_calo_hits
    data_files:
      - split: train
        path: data/dihiggs_pu0_calo_hits/*.parquet
  - config_name: dihiggs_pu0_particles
    data_files:
      - split: train
        path: data/dihiggs_pu0_particles/*.parquet
  - config_name: dihiggs_pu0_tracker_hits
    data_files:
      - split: train
        path: data/dihiggs_pu0_tracker_hits/*.parquet
  - config_name: dihiggs_pu0_tracks
    data_files:
      - split: train
        path: data/dihiggs_pu0_tracks/*.parquet
  - config_name: dihiggs_pu200_calo_hits
    data_files:
      - split: train
        path: data/dihiggs_pu200_calo_hits/*.parquet
  - config_name: dihiggs_pu200_particles
    data_files:
      - split: train
        path: data/dihiggs_pu200_particles/*.parquet
  - config_name: dihiggs_pu200_tracker_hits
    data_files:
      - split: train
        path: data/dihiggs_pu200_tracker_hits/*.parquet
  - config_name: dihiggs_pu200_tracks
    data_files:
      - split: train
        path: data/dihiggs_pu200_tracks/*.parquet
  - config_name: ggf_pu0_calo_hits
    data_files:
      - split: train
        path: data/ggf_pu0_calo_hits/*.parquet
  - config_name: ggf_pu0_particles
    data_files:
      - split: train
        path: data/ggf_pu0_particles/*.parquet
  - config_name: ggf_pu0_tracker_hits
    data_files:
      - split: train
        path: data/ggf_pu0_tracker_hits/*.parquet
  - config_name: ggf_pu0_tracks
    data_files:
      - split: train
        path: data/ggf_pu0_tracks/*.parquet
  - config_name: ggf_pu200_calo_hits
    data_files:
      - split: train
        path: data/ggf_pu200_calo_hits/*.parquet
  - config_name: ggf_pu200_particles
    data_files:
      - split: train
        path: data/ggf_pu200_particles/*.parquet
  - config_name: ggf_pu200_tracker_hits
    data_files:
      - split: train
        path: data/ggf_pu200_tracker_hits/*.parquet
  - config_name: ggf_pu200_tracks
    data_files:
      - split: train
        path: data/ggf_pu200_tracks/*.parquet
  - config_name: ttbar_pu0_calo_hits
    data_files:
      - split: train
        path: data/ttbar_pu0_calo_hits/*.parquet
  - config_name: ttbar_pu0_particles
    data_files:
      - split: train
        path: data/ttbar_pu0_particles/*.parquet
  - config_name: ttbar_pu0_tracker_hits
    data_files:
      - split: train
        path: data/ttbar_pu0_tracker_hits/*.parquet
  - config_name: ttbar_pu0_tracks
    data_files:
      - split: train
        path: data/ttbar_pu0_tracks/*.parquet
  - config_name: ttbar_pu200_calo_hits
    data_files:
      - split: train
        path: data/ttbar_pu200_calo_hits/*.parquet
  - config_name: ttbar_pu200_particles
    data_files:
      - split: train
        path: data/ttbar_pu200_particles/*.parquet
  - config_name: ttbar_pu200_tracker_hits
    data_files:
      - split: train
        path: data/ttbar_pu200_tracker_hits/*.parquet
  - config_name: ttbar_pu200_tracks
    data_files:
      - split: train
        path: data/ttbar_pu200_tracks/*.parquet
  - config_name: zee_pu200_calo_hits
    data_files:
      - split: train
        path: data/zee_pu200_calo_hits/*.parquet
  - config_name: zee_pu200_particles
    data_files:
      - split: train
        path: data/zee_pu200_particles/*.parquet
  - config_name: zee_pu200_tracker_hits
    data_files:
      - split: train
        path: data/zee_pu200_tracker_hits/*.parquet
  - config_name: zee_pu200_tracks
    data_files:
      - split: train
        path: data/zee_pu200_tracks/*.parquet
  - config_name: zmumu_pu200_calo_hits
    data_files:
      - split: train
        path: data/zmumu_pu200_calo_hits/*.parquet
  - config_name: zmumu_pu200_particles
    data_files:
      - split: train
        path: data/zmumu_pu200_particles/*.parquet
  - config_name: zmumu_pu200_tracker_hits
    data_files:
      - split: train
        path: data/zmumu_pu200_tracker_hits/*.parquet
  - config_name: zmumu_pu200_tracks
    data_files:
      - split: train
        path: data/zmumu_pu200_tracks/*.parquet

ColliderML: Dataset Release 1

Dataset Description

This dataset contains simulated high-energy physics collision events generated using the Open Data Detector (ODD) geometry within the Key4hep and ACTS (A Common Tracking Software) frameworks, representing a generic collider detector similar to those at the HL-LHC.

Dataset Summary

  • Collision Energy: 14 TeV (proton-proton)
  • Detector: Open Data Detector (ODD)
  • Simulation: DD4hep + Geant4 + ACTS
  • Format: Apache Parquet with list columns for variable-length data
  • License: CC-BY-4.0

Available Configurations

The dataset is organized into multiple configurations, each representing a combination of:

  • Physics process (e.g., ttbar, ggf, dihiggs)
  • Pileup condition (pu0 = no pileup, pu200 = HL-LHC pileup)
  • Object type (particles, tracker_hits, calo_hits, tracks)

Supported Tasks

This dataset is designed for machine learning tasks in high-energy physics, including:

  • Particle tracking: Reconstruct charged particle trajectories from detector hits
  • Track-to-particle matching: Associate reconstructed tracks with truth particles
  • Jet tagging: Identify jets originating from top quarks, b-quarks, or light quarks
  • Energy reconstruction: Predict particle energies from calorimeter deposits
  • Physics analysis: Event classification (signal vs. background discrimination)
  • Representation learning: Study hierarchical information at different detector levels

Quick Start

Installation 

pip install datasets pyarrow

Load a Configuration 

from datasets import load_dataset

# Load truth particles from ttbar (no pileup)
particles = load_dataset(
    "OpenDataDetector/ColliderML-Release-1",
    "ttbar_pu0_particles",
    split="train"
)

print(f"Loaded {len(particles)} events")
print(f"Columns: {particles.column_names}")

Load First 100 Events with Specific Columns 

from datasets import load_dataset
import numpy as np

# Load only specific columns
particles = load_dataset(
    "OpenDataDetector/ColliderML-Release-1",
    "ttbar_pu0_particles",
    split="train[:100]",
    columns=["event_id", "px", "py", "pz", "energy", "pdg_id"]
)

# Process events
for event in particles:
    px = np.array(event['px'])
    py = np.array(event['py'])
    pt = np.sqrt(px**2 + py**2)
    print(f"Event {event['event_id']}: {len(px)} particles, mean pT = {pt.mean():.2f} GeV")

Dataset Structure

Data Instances

Each row represents a single collision event. Variable-length quantities (particles, hits, tracks) are stored as Parquet list columns.

Example event structure:

{
    'event_id': 42,
    'particle_id': [0, 1, 2, 3, ...],
    'pdg_id': [11, -11, 211, ...],
    'px': [1.2, -0.5, 3.4, ...],
    'py': [0.8, 1.1, -0.3, ...],
    'pz': [5.2, -2.1, 10.5, ...],
    'energy': [5.5, 2.3, 11.2, ...],
    # ... additional fields
}

Data Fields by Object Type

1. particles (Truth-level)

Truth information about generated particles before detector simulation.

Field Type Description
event_id uint32 Unique event identifier
particle_id list<uint64> Unique particle ID within event
pdg_id list<int64> PDG particle code (11=electron, 13=muon, 211=pion, etc.)
mass list<float32> Particle rest mass (GeV/c²)
energy list<float32> Particle total energy (GeV)
charge list<float32> Electric charge (units of e)
px, py, pz list<float32> Momentum components (GeV/c)
vx, vy, vz list<float32> Vertex position (mm)
time list<float32> Production time (ns)
perigee_d0 list<float32> Perigee transverse impact parameter (mm)
perigee_z0 list<float32> Perigee longitudinal impact parameter (mm)
num_tracker_hits list<uint16> Number of hits in tracker
num_calo_hits list<uint16> Number of hits in calorimeter
primary list<bool> Whether particle is primary
vertex_primary list<uint16> Primary vertex index (1=hard scatter)
parent_id list<int64> ID of parent particle (-1 if none)

2. tracker_hits (Detector-level)

Digitized spatial measurements from the tracking detector (silicon sensors).

Field Type Description
event_id uint32 Unique event identifier
x, y, z list<float32> Measured hit position (mm)
true_x, true_y, true_z list<float32> True hit position before digitization (mm)
time list<float32> Hit time (ns)
particle_id list<uint64> Truth particle that created this hit
volume_id list<uint8> Detector volume identifier
layer_id list<uint16> Detector layer number
surface_id list<uint32> Sensor surface identifier
detector list<uint8> Detector subsystem code

3. calo_hits (Calorimeter-level)

Energy deposits in the calorimeter system (electromagnetic + hadronic).

Field Type Description
event_id uint32 Unique event identifier
detector list<uint8> Calorimeter subsystem code
total_energy list<float32> Total energy deposited in cell (GeV)
x, y, z list<float32> Cell center position (mm)
contrib_particle_ids list<list<uint64>> IDs of particles contributing to this cell
contrib_energies list<list<float32>> Energy contribution from each particle (GeV)
contrib_times list<list<float32>> Time of each contribution (ns)

4. tracks (Reconstruction-level)

Reconstructed particle tracks from ACTS pattern recognition and track fitting.

Field Type Description
event_id uint32 Unique event identifier
track_id list<uint16> Unique track identifier within event
majority_particle_id list<uint64> Truth particle with most hits on this track
d0 list<float32> Transverse impact parameter (mm)
z0 list<float32> Longitudinal impact parameter (mm)
phi list<float32> Azimuthal angle (radians)
theta list<float32> Polar angle (radians)
qop list<float32> Charge divided by momentum (e/GeV)
hit_ids list<list<uint32>> List of tracker hit IDs on this track

Derived quantities for tracks:

  • Transverse momentum: pt = abs(1/qop) * sin(theta)
  • Pseudorapidity: eta = -ln(tan(theta/2))
  • Total momentum: p = abs(1/qop)

Dataset Creation

Simulation Chain

  1. Event Generation: MadGraph5 + Pythia8 for hard scatter and parton shower
  2. Detector Simulation: Geant4 via DD4hep with the Open Data Detector geometry
  3. Digitization: Realistic detector response simulation
  4. Reconstruction: ACTS track finding and fitting algorithms
  5. Format Conversion: EDM4HEP → Parquet using the ColliderML pipeline

Software Stack

Citation

If you use this dataset in your research, please cite:

@dataset{colliderml_release1_2025,
  title={{ColliderML Dataset Release 1}},
  author={{ColliderML Collaboration}},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/OpenDataDetector/ColliderML-Release-1}},
  note={Simulation performed using ACTS and the Open Data Detector}
}

Support

For questions, issues, or feature requests:

Acknowledgments

This work was supported by:

  • NERSC computing resources (National Energy Research Scientific Computing Center)
  • U.S. Department of Energy, Office of Science
  • Danish Data Science Academy (DDSA)

Release Version: 1.0
Last Updated: November 2025