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README.md

@@ -1,52 +1,243 @@
 ---
-dataset_info:
-  config_name: ttbar_pu0_particles
-  features:
-  - name: event_id
-    dtype: uint32
-  - name: particle_id
-    sequence: uint64
-  - name: pdg_id
-    sequence: int64
-  - name: mass
-    sequence: float32
-  - name: energy
-    sequence: float32
-  - name: charge
-    sequence: float32
-  - name: vx
-    sequence: float32
-  - name: vy
-    sequence: float32
-  - name: vz
-    sequence: float32
-  - name: time
-    sequence: float32
-  - name: px
-    sequence: float32
-  - name: py
-    sequence: float32
-  - name: pz
-    sequence: float32
-  - name: perigee_d0
-    sequence: float32
-  - name: perigee_z0
-    sequence: float32
-  - name: vertex_primary
-    sequence: uint16
-  - name: parent_id
-    sequence: int64
-  - name: primary
-    sequence: bool
-  splits:
-  - name: train
-    num_bytes: 39046863524
-    num_examples: 100000
-  download_size: 22589984479
-  dataset_size: 39046863524
-configs:
-- config_name: ttbar_pu0_particles
-  data_files:
-  - split: train
-    path: ttbar_pu0_particles/train-*
+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
 ---
+
+# 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