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---
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
```bash
pip install datasets pyarrow
```
### Load a Configuration
```python
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
```python
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:
```python
{
'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
- **ACTS**: A Common Tracking Software - https://acts.readthedocs.io/
- **Open Data Detector**: https://github.com/acts-project/odd
- **Key4hep**: https://key4hep.github.io/
- **EDM4HEP**: https://edm4hep.web.cern.ch/
## Citation
If you use this dataset in your research, please cite:
```bibtex
@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:
- **Email**: daniel.thomas.murnane@cern.ch
- **GitHub**: https://github.com/OpenDataDetector/ColliderML
## 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
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