--- license: cc-by-4.0 task_categories: - other tags: - physics - high-energy-physics - particle-physics - tracking - calorimetry - machine-learning - simulation pretty_name: ColliderML Top-Quark Pair Production (No Pileup) size_categories: - 10K | Unique particle ID within event | | `pdg_id` | list | PDG particle code (e.g., 11=electron, 13=muon, 211=pion) | | `mass` | list | Particle rest mass (GeV/c²) | | `energy` | list | Particle total energy (GeV) | | `charge` | list | Electric charge (in units of e) | | `px`, `py`, `pz` | list | Momentum components (GeV/c) | | `vx`, `vy`, `vz` | list | Vertex position (mm) | | `time` | list | Production time (ns) | | `num_tracker_hits` | list | Number of hits in tracker | | `num_calo_hits` | list | Number of hits in calorimeter | | `vertex_primary` | list | Primary vertex flag (1 = hard scatter, 2,...,N = pileup) | | `parent_id` | list | ID of parent particle | **Typical event**: ~200-500 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 | Measured hit position (mm) | | `true_x`, `true_y`, `true_z` | list | True (simulated) hit position before digitization (mm) | | `time` | list | Hit time (ns) | | `particle_id` | list | Truth particle that created this hit | | `volume_id` | list | Detector volume identifier | | `layer_id` | list | Detector layer number | | `surface_id` | list | Sensor surface identifier | | `cell_id` | list | Cell/pixel identifier | | `detector` | list | Detector subsystem code | **Typical event**: ~2,000-5,000 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 | Calorimeter subsystem name | | `cell_id` | list | Calorimeter cell identifier | | `total_energy` | list | Total energy deposited in cell (GeV) | | `x`, `y`, `z` | list | Cell center position (mm) | | `contrib_particle_ids` | list> | IDs of particles contributing to this cell | | `contrib_energies` | list> | Energy contribution from each particle (GeV) | | `contrib_times` | list> | 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 | Unique track identifier within event | | `majority_particle_id` | list | Truth particle with most hits on this track | | `d0` | list | Transverse impact parameter (mm) | | `z0` | list | Longitudinal impact parameter (mm) | | `phi` | list | Azimuthal angle (radians) | | `theta` | list | Polar angle (radians) | | `qop` | list | Charge divided by momentum (e/GeV) | | `hit_ids` | list> | 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**: ~50-150 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(100000)) 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 ``` ### Support For questions, issues, or feature requests: - Email: daniel.thomas.murnane@cern.ch - You can also open a discussion in the HuggingFace community panel for this dataset. ### Acknowledgments This work was supported by: - NERSC computing resources - U.S. Department of Energy, Office of Science - Danish Data Science Academy (DDSA) --- **Last updated**: October 2025 **Dataset version**: v1