Update hard_scatter/ttbar/v1 dataset card

README.md

@@ -16,13 +16,11 @@ size_categories:
 configs:
 - config_name: particles
   data_files:
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events0-9.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events1000-1999.parquet"
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events2000-2999.parquet"
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events9000-9999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events10000-10999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events11000-11999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events15000-15999.parquet"
+  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events2000-2999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events24000-24999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events25000-25999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events28000-28999.parquet"
@@ -43,17 +41,16 @@ configs:
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events78000-78999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events83000-83999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events88000-88999.parquet"
+  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events9000-9999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events92000-92999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/truth/particles/hard_scatter.ttbar.v1.truth.particles.events97000-97999.parquet"
 - config_name: tracker_hits
   data_files:
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events0-9.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events1000-1999.parquet"
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events2000-2999.parquet"
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events9000-9999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events10000-10999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events11000-11999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events15000-15999.parquet"
+  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events2000-2999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events24000-24999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events25000-25999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events28000-28999.parquet"
@@ -74,17 +71,16 @@ configs:
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events78000-78999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events83000-83999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events88000-88999.parquet"
+  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events9000-9999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events92000-92999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracker_hits/hard_scatter.ttbar.v1.reco.tracker_hits.events97000-97999.parquet"
 - config_name: calo_hits
   data_files:
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events0-9.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events1000-1999.parquet"
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events2000-2999.parquet"
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events9000-9999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events10000-10999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events11000-11999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events15000-15999.parquet"
+  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events2000-2999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events24000-24999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events25000-25999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events28000-28999.parquet"
@@ -105,17 +101,16 @@ configs:
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events78000-78999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events83000-83999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events88000-88999.parquet"
+  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events9000-9999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events92000-92999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/calo_hits/hard_scatter.ttbar.v1.reco.calo_hits.events97000-97999.parquet"
 - config_name: tracks
   data_files:
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events0-9.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events1000-1999.parquet"
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events2000-2999.parquet"
-  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events9000-9999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events10000-10999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events11000-11999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events15000-15999.parquet"
+  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events2000-2999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events24000-24999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events25000-25999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events28000-28999.parquet"
@@ -136,26 +131,26 @@ configs:
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events78000-78999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events83000-83999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events88000-88999.parquet"
+  - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events9000-9999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events92000-92999.parquet"
   - "https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/reco/tracks/hard_scatter.ttbar.v1.reco.tracks.events97000-97999.parquet"
 ---
 
-# ColliderML: Top-Quark Pair Production Dataset (ttbar, No Pileup)
+# ColliderML: ColliderML Top-Quark Pair Production (No Pileup)
 
 ## Dataset Description
 
-This dataset contains simulated high-energy physics collision events for top-quark pair (ttbar) production with **no pileup** (single interaction per event). The data is generated using the **Open Data Detector (ODD)** geometry within the **ACTS (A Common Tracking Software)** framework, representing a generic collider detector similar to those at the LHC.
+This dataset contains simulated high-energy physics collision events generated using the **Open Data Detector (ODD)** geometry within the **ACTS (A Common Tracking Software)** framework.
 
 ### Dataset Summary
 
 - **Campaign**: `hard_scatter`
-- **Process**: Top-quark pair production (ttbar)
+- **Process**: Top-quark pair (ttbar) production
 - **Version**: `v1`
-- **Number of Events**: ~29,000 events (29 files × 1000 events per file)
-- **Pileup**: 0 (no additional interactions)
+- **Pileup**: 0
 - **Detector**: Open Data Detector (ODD)
 - **Format**: Apache Parquet with list columns for variable-length data
-- **License**: CC-BY-4.0
+- **License**: cc-by-4.0
 
 ### Supported Tasks
 
@@ -163,446 +158,99 @@ This dataset is designed for machine learning tasks in high-energy physics, incl
 
 - **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
+- **Jet tagging**: Identify jets originating from different particle types
 - **Energy reconstruction**: Predict particle energies from calorimeter deposits
-- **Physics analysis**: Event classification (signal vs. background discrimination)
+- **Physics analysis**: Event classification and signal/background discrimination
 - **Representation learning**: Study hierarchical information at different detector levels
 
-### Languages
-
-N/A (Physics data)
-
 ## Dataset Structure
 
-### Data Instances
-
-Each row in the Parquet files represents a single collision event. Variable-length quantities (e.g., lists of particles, hits, tracks) are stored as Parquet list columns.
-
-Example event structure:
-
-```python
-{
-    'event_id': 42,
-    'particle_id': [0, 1, 2, 3, ...],  # List of particle IDs
-    'pdg_id': [11, -11, 211, ...],     # Particle type codes
-    'px': [1.2, -0.5, 3.4, ...],       # Momentum components (GeV)
-    '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
-
-The dataset contains four data types organized by detector hierarchy:
-
-#### 1. `particles` (Truth-level)
-
-Truth information about generated particles before detector simulation.
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `event_id` | int64 | Unique event identifier |
-| `particle_id` | list\<int64\> | Unique particle ID within event |
-| `pdg_id` | list\<int64\> | PDG particle code (e.g., 11=electron, 13=muon, 211=pion) |
-| `mass` | list\<float64\> | Particle rest mass (GeV/c²) |
-| `energy` | list\<float64\> | Particle total energy (GeV) |
-| `charge` | list\<float64\> | Electric charge (in units of e) |
-| `px`, `py`, `pz` | list\<float64\> | Momentum components (GeV/c) |
-| `vx`, `vy`, `vz` | list\<float64\> | Vertex position (mm) |
-| `time` | list\<float64\> | Production time (ns) |
-| `num_tracker_hits` | list\<int64\> | Number of hits in tracker |
-| `num_calo_hits` | list\<int64\> | Number of hits in calorimeter |
-| `vertex_primary` | list\<int64\> | Primary vertex flag (1=primary, 0=secondary) |
-| `parent_id` | list\<float64\> | ID of parent particle |
-
-**Typical event**: ~200-300 particles per event
-
-#### 2. `tracker_hits` (Detector-level)
-
-Digitized spatial measurements from the tracking detector (silicon sensors).
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `event_id` | int64 | Unique event identifier |
-| `x`, `y`, `z` | list\<float64\> | Measured hit position (mm) |
-| `true_x`, `true_y`, `true_z` | list\<float64\> | True (simulated) hit position before digitization (mm) |
-| `time` | list\<float64\> | Hit time (ns) |
-| `particle_id` | list\<int64\> | Truth particle that created this hit |
-| `volume_id` | list\<int64\> | Detector volume identifier |
-| `layer_id` | list\<int64\> | Detector layer number |
-| `surface_id` | list\<int64\> | Sensor surface identifier |
-| `cell_id` | list\<int64\> | Cell/pixel identifier |
-| `detector` | list\<int64\> | Detector subsystem code |
-
-**Typical event**: ~2,000-3,000 hits per event
-
-#### 3. `calo_hits` (Calorimeter-level)
-
-Energy deposits in the calorimeter system (electromagnetic + hadronic).
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `event_id` | int64 | Unique event identifier |
-| `detector` | list\<string\> | Calorimeter subsystem name |
-| `cell_id` | list\<string\> | Calorimeter cell identifier |
-| `total_energy` | list\<float64\> | Total energy deposited in cell (GeV) |
-| `x`, `y`, `z` | list\<float64\> | Cell center position (mm) |
-| `contrib_particle_ids` | list\<list\<int64\>\> | IDs of particles contributing to this cell |
-| `contrib_energies` | list\<list\<float64\>\> | Energy contribution from each particle (GeV) |
-| `contrib_times` | list\<list\<float64\>\> | Time of each contribution (ns) |
-
-**Note**: Nested lists for contributions (one cell can have multiple particle deposits).
-
-**Typical event**: ~500-1,000 calorimeter cells with deposits
-
-#### 4. `tracks` (Reconstruction-level)
-
-Reconstructed particle tracks from pattern recognition and track fitting algorithms.
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `event_id` | int64 | Unique event identifier |
-| `track_id` | list\<int64\> | Unique track identifier within event |
-| `majority_particle_id` | list\<int64\> | Truth particle with most hits on this track |
-| `d0` | list\<float64\> | Transverse impact parameter (mm) |
-| `z0` | list\<float64\> | Longitudinal impact parameter (mm) |
-| `phi` | list\<float64\> | Azimuthal angle (radians) |
-| `theta` | list\<float64\> | Polar angle (radians) |
-| `qop` | list\<float64\> | Charge divided by momentum (e/GeV) |
-| `hit_ids` | list\<list\<int32\>\> | List of tracker hit IDs assigned to this track |
-
-**Track parameters**: Standard ACTS track representation (perigee parameters at origin).
-
-**Derived quantities**:
-- Transverse momentum: `pt = abs(1/qop) * sin(theta)`
-- Pseudorapidity: `eta = -ln(tan(theta/2))`
-- Total momentum: `p = abs(1/qop)`
-
-**Typical event**: ~100-150 reconstructed tracks per event
-
-### Data Splits
-
-Currently, the dataset does not have predefined train/validation/test splits. Users should implement their own splitting strategy based on their use case. Recommended approach:
-
-```python
-from sklearn.model_selection import train_test_split
-
-# Example: 70% train, 15% validation, 15% test
-all_events = list(range(29000))
-train_val, test = train_test_split(all_events, test_size=0.15, random_state=42)
-train, val = train_test_split(train_val, test_size=0.176, random_state=42)  # 0.176 * 0.85 ≈ 0.15
-```
-
-## Dataset Creation
-
-### Curation Rationale
-
-This dataset was created to support machine learning research in high-energy physics, specifically for:
-
-1. **Benchmarking tracking algorithms**: Compare traditional and ML-based track reconstruction methods
-2. **Hierarchical representation learning**: Study information flow from detector hits → tracks → particles
-3. **Physics analysis**: Develop ML models for event classification and particle identification
-4. **Open science**: Provide publicly accessible, realistic detector simulation data
-
-The ttbar process is chosen because:
-- It produces complex final states with many particles
-- It's a key signature at hadron colliders (LHC)
-- Top quarks decay to b-quarks, W bosons, and ultimately jets and leptons
-- Relevant for searches for new physics beyond the Standard Model
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-The data is generated through the following simulation chain:
-
-1. **Event Generation**: ttbar events generated using a Monte Carlo event generator
-2. **Detector Simulation**: Particle propagation through the Open Data Detector using ACTS
-3. **Digitization**: Conversion of energy deposits to realistic detector signals
-4. **Reconstruction**: Track finding and fitting using ACTS tracking algorithms
-5. **Format Conversion**: EDM4HEP → Parquet using the ColliderML data pipeline
-
-#### Who are the source data producers?
-
-The data is produced by the **ColliderML collaboration** as part of the **ATLAS ITk ML Reconstruction** project at NERSC (National Energy Research Scientific Computing Center).
-
-### Annotations
-
-#### Annotation process
-
-The dataset includes truth-level annotations automatically generated during the simulation:
-
-- **Particle-level truth**: Generator-level particle information
-- **Hit-to-particle associations**: Which particle created each detector hit
-- **Track-to-particle matching**: `majority_particle_id` links reconstructed tracks to truth particles
+### Data Configurations
 
-These annotations enable supervised learning for tasks like:
-- Track efficiency (did we reconstruct this particle?)
-- Track purity (how many hits belong to the correct particle?)
-- Fake rate (how many tracks are not matched to real particles?)
+The dataset contains 4 data types:
 
-#### Who are the annotators?
 
-N/A (Annotations are from simulation ground truth)
+#### particles
 
-### Personal and Sensitive Information
+- **Number of files**: 28
+- **Fields**: event_id, particle_id, pdg_id, mass, energy, charge, vx, vy, vz, time, px, py, pz, vertex_primary, parent_id
 
-This dataset contains only simulated physics data. No personal or sensitive information is included.
+#### tracker_hits
 
-## Considerations for Using the Data
+- **Number of files**: 28
+- **Fields**: event_id, x, y, z, true_x, true_y, true_z, time, particle_id, cell_id, detector, volume_id, layer_id, surface_id
 
-### Social Impact of Dataset
+#### calo_hits
 
-This dataset supports fundamental physics research and ML algorithm development. It has no direct social impact but contributes to:
+- **Number of files**: 28
+- **Fields**: event_id, detector, total_energy, x, y, z, contrib_particle_ids, contrib_energies, contrib_times
 
-- Open science and reproducible research
-- Education in HEP and ML
-- Development of algorithms that may have broader applications (e.g., pattern recognition, tracking in medical imaging)
+#### tracks
 
-### Discussion of Biases
+- **Number of files**: 28
+- **Fields**: event_id, d0, z0, phi, theta, qop, majority_particle_id, hit_ids, track_id
 
-As a simulated dataset, biases may arise from:
 
-1. **Generator-level biases**: The event generator's modeling of ttbar production
-2. **Detector simulation biases**: Approximations in material interactions, detector response
-3. **Reconstruction biases**: Algorithm choices in track finding and fitting
-4. **No pileup**: Real LHC data has 20-60 simultaneous collisions; this dataset has only 1
-
-Users should be aware that models trained on this data may not generalize to:
-- Real detector data (requires calibration and alignment)
-- Different detector geometries
-- Events with pileup
-
-### Other Known Limitations
-
-- **Limited statistics**: ~29,000 events is moderate for ML training (consider data augmentation)
-- **Single physics process**: Only ttbar; does not include background processes
-- **Idealized detector**: ODD is a generic detector, not an exact replica of ATLAS/CMS
-- **No detector inefficiencies**: Assumes 100% hit efficiency (real detectors have dead regions)
-
-## Additional Information
-
-### Dataset Curators
-
-This dataset is maintained by the ColliderML team:
-
-- Primary contact: [[email protected]](mailto:[email protected])
-- Collaboration: ATLAS ITk ML Reconstruction working group
-- Infrastructure: NERSC (National Energy Research Scientific Computing Center)
-
-### Licensing Information
-
-This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
-
-You are free to:
-- **Share**: Copy and redistribute the material
-- **Adapt**: Remix, transform, and build upon the material
-
-Under the following terms:
-- **Attribution**: You must give appropriate credit and indicate if changes were made
-
-### Citation Information
-
-If you use this dataset in your research, please cite:
-
-```bibtex
-@dataset{colliderml_ttbar_pu0_2024,
-  title={ColliderML: Top-Quark Pair Production Dataset (No Pileup)},
-  author={ColliderML Collaboration},
-  year={2024},
-  publisher={NERSC},
-  howpublished={\url{https://huggingface.co/datasets/OpenDataDetector/ColliderML_ttbar_pu0}},
-  note={Simulation performed using ACTS and the Open Data Detector}
-}
-```
-
-### Contributions
+### Data Location
 
-This dataset was produced using:
+The Parquet files are hosted on NERSC public portal:
 
-- **ACTS (A Common Tracking Software)**: https://acts.readthedocs.io/
-- **Open Data Detector**: https://acts.readthedocs.io/en/latest/examples/open_data_detector.html
-- **EDM4HEP**: https://edm4hep.web.cern.ch/
-- **ColliderML Pipeline**: https://github.com/ATLAS-ITk-ML/colliderml
+Base URL: `https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet`
 
 ## How to Use This Dataset
 
 ### Loading the Dataset
 
-The dataset is hosted on the NERSC public portal and can be streamed directly without downloading:
-
 ```python
 from datasets import load_dataset
 
-# Load particles (truth-level)
-particles_ds = load_dataset(
-    "OpenDataDetector/ColliderML_ttbar_pu0",
-    "particles",
-    split="train",
-    streaming=True
-)
-
-# Load tracker hits
-tracker_hits_ds = load_dataset(
-    "OpenDataDetector/ColliderML_ttbar_pu0",
-    "tracker_hits",
-    split="train",
-    streaming=True
-)
-
-# Load calorimeter hits
-calo_hits_ds = load_dataset(
-    "OpenDataDetector/ColliderML_ttbar_pu0",
-    "calo_hits",
-    split="train",
-    streaming=True
-)
-
-# Load reconstructed tracks
-tracks_ds = load_dataset(
+# Load a specific configuration
+ds = load_dataset(
     "OpenDataDetector/ColliderML_ttbar_pu0",
-    "tracks",
-    split="train",
-    streaming=True
+    "particles",  # Choose: particles, tracker_hits, calo_hits, tracks
+    split="train"
 )
-```
-
-### Example: Iterating Over Events
-
-```python
-import numpy as np
 
-# Iterate over first 10 events
-for i, event in enumerate(particles_ds.take(10)):
+# Iterate over events
+for event in ds:
     event_id = event['event_id']
-    n_particles = len(event['particle_id'])
-
-    print(f"Event {event_id}: {n_particles} particles")
-
-    # Access list columns as numpy arrays
-    px = np.array(event['px'])
-    py = np.array(event['py'])
-    pz = np.array(event['pz'])
-
-    # Compute transverse momentum
-    pt = np.sqrt(px**2 + py**2)
-    print(f"  Mean pt: {pt.mean():.2f} GeV")
+    print(f"Event {event_id}")
 ```
 
-### Example: Computing Track Features
+### Loading Multiple Configurations
 
 ```python
-import numpy as np
-
-for event in tracks_ds.take(5):
-    # Get track parameters
-    qop = np.array(event['qop'])
-    theta = np.array(event['theta'])
-    phi = np.array(event['phi'])
-
-    # Compute derived quantities
-    pt = np.abs(1.0 / qop) * np.sin(theta)
-    eta = -np.log(np.tan(theta / 2.0))
-
-    print(f"Event {event['event_id']}: {len(qop)} tracks")
-    print(f"  pt range: [{pt.min():.2f}, {pt.max():.2f}] GeV")
-    print(f"  eta range: [{eta.min():.2f}, {eta.max():.2f}]")
-```
-
-### Example: Matching Tracks to Particles
+from datasets import load_dataset
 
-```python
-# Load both datasets
-particles = load_dataset("OpenDataDetector/ColliderML_ttbar_pu0", "particles", split="train", streaming=True)
-tracks = load_dataset("OpenDataDetector/ColliderML_ttbar_pu0", "tracks", split="train", streaming=True)
-
-# Process event-by-event
-for particle_event, track_event in zip(particles, tracks):
-    assert particle_event['event_id'] == track_event['event_id']
-
-    # Create particle ID lookup
-    particle_ids = np.array(particle_event['particle_id'])
-    particle_pt = np.sqrt(
-        np.array(particle_event['px'])**2 +
-        np.array(particle_event['py'])**2
-    )
-
-    # Get track associations
-    track_particle_ids = np.array(track_event['majority_particle_id'])
-
-    # Find matched particles
-    for track_idx, pid in enumerate(track_particle_ids):
-        if pid in particle_ids:
-            particle_idx = np.where(particle_ids == pid)[0][0]
-            truth_pt = particle_pt[particle_idx]
-
-            # Compute reconstructed pt
-            qop = track_event['qop'][track_idx]
-            theta = track_event['theta'][track_idx]
-            reco_pt = abs(1.0 / qop) * np.sin(theta)
-
-            print(f"Track {track_idx}: truth pt = {truth_pt:.2f}, reco pt = {reco_pt:.2f} GeV")
+# Load all configurations
+particles = load_dataset("OpenDataDetector/ColliderML_ttbar_pu0", "particles", split="train")
+tracker_hits = load_dataset("OpenDataDetector/ColliderML_ttbar_pu0", "tracker_hits", split="train")
+calo_hits = load_dataset("OpenDataDetector/ColliderML_ttbar_pu0", "calo_hits", split="train")
+tracks = load_dataset("OpenDataDetector/ColliderML_ttbar_pu0", "tracks", split="train")
 ```
 
-### Data Location
-
-The Parquet files are hosted at:
+## Citation
 
-```
-https://portal.nersc.gov/cfs/m4958/ColliderML/hard_scatter/ttbar/v1/parquet/
-├── truth/
-│   └── particles/
-│       ├── hard_scatter.ttbar.v1.truth.particles.events0-9.parquet
-│       ├── hard_scatter.ttbar.v1.truth.particles.events2000-2999.parquet
-│       └── ... (29 files total, ~29,000 events)
-├── reco/
-│   ├── tracker_hits/
-│   │   ├── hard_scatter.ttbar.v1.reco.tracker_hits.events0-9.parquet
-│   │   └── ... (29 files)
-│   ├── calo_hits/
-│   │   ├── hard_scatter.ttbar.v1.reco.calo_hits.events0-9.parquet
-│   │   └── ... (29 files)
-│   └── tracks/
-│       ├── hard_scatter.ttbar.v1.reco.tracks.events0-9.parquet
-│       └── ... (29 files)
-```
-
-### File Naming Convention
+If you use this dataset in your research, please cite:
 
-Files follow the pattern:
-```
-<campaign>.<dataset>.<version>.<category>.<object>.<event_range>.parquet
+```bibtex
+@dataset{colliderml_ttbar_v1,
+  title={ColliderML: ColliderML Top-Quark Pair Production (No Pileup)},
+  author={ColliderML Collaboration},
+  year={2025},
+  publisher={NERSC},
+  howpublished={\url{https://huggingface.co/datasets/OpenDataDetector/ColliderML_ttbar_pu0}},
+  note={Simulation performed using ACTS and the Open Data Detector}
+}
 ```
 
-Example: `hard_scatter.ttbar.v1.reco.tracks.events0-9.parquet`
-- Campaign: `hard_scatter`
-- Dataset: `ttbar`
-- Version: `v1`
-- Category: `reco` (or `truth`)
-- Object: `tracks`
-- Event range: `events0-9` (inclusive)
-
-### Performance Tips
-
-1. **Streaming**: Use `streaming=True` to avoid downloading the entire dataset
-2. **Batch processing**: Process events in chunks for better memory efficiency
-3. **Parallel loading**: Use `num_proc` parameter for multi-threaded data loading
-4. **Selective loading**: Only load the data types you need (particles, hits, tracks)
-
-### Related Datasets
-
-- **ColliderML_ttbar_pu200** (coming soon): Same process with 200 pileup interactions
-- **ColliderML_higgs_pu0** (coming soon): Higgs boson production without pileup
-
-### Support
+## Contact
 
-For questions, issues, or feature requests:
-- Open an issue on GitHub: https://github.com/ATLAS-ITk-ML/colliderml/issues
-- Email: [email protected]
+- **Primary contact**: [email protected]
+- **Collaboration**: ATLAS ITk ML Reconstruction working group
+- **Infrastructure**: NERSC (National Energy Research Scientific Computing Center)
 
-### Acknowledgments
+## Acknowledgments
 
 This work was supported by:
 - ATLAS ITk ML Reconstruction project