---
license: apache-2.0
task_categories:
- image-segmentation
- object-detection
tags:
- particle
- physics
- 3D
- simulation
- lartpc
- pointcloud
pretty_name: >-
  Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy
  Physics - Medium
size_categories:
- 1M<n<10M
---

# Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics

We provide the 168 GB **PILArNet-Medium** dataset, a continuation of the [PILArNet](https://arxiv.org/abs/2006.01993) dataset, consisting of ~1.2 million events from liquid argon time projection chambers ([LArTPCs](https://www.symmetrymagazine.org/article/october-2012/time-projection-chambers-a-milestone-in-particle-detector-technology?language_content_entity=und)).

Each event contains 3D ionization trajectories of particles as they traverse the detector. Typical downstream tasks include:

- Semantic segmentation of voxels into particle-like categories  
- Particle-level (instance-level) segmentation and identification  
- Interaction-level grouping of particles that belong to the same interaction  

## Directory structure

The dataset is stored in HDF5 format and organized as:

```plaintext
/path/to/dataset/
    /train/
        /generic_v2_196200_v2.h5
        /generic_v2_153600_v1.h5
        ...
    /val/
        /generic_v2_66800_v2.h5
        ...
    /test/
        /generic_v2_50000_v1.h5
        ...
````

The number preceding the second `v2` indicates the number of events contained in the file.

Dataset split:

* **Train:** 1,082,400 events
* **Validation:** 66,800 events
* **Test:** 50,000 events

## Data format

Each HDF5 file contains three main datasets: `point`, `cluster`, and `cluster_extra`.
Entries are stored as variable length 1D arrays and should be reshaped event by event.

### `point` dataset

Each entry of `point` corresponds to a single event and encodes all spacepoints for that event in a flattened array. After reshaping, each row corresponds to a point:

Shape per event: `(N, 8)`

Columns (per point):

1. `x` coordinate (integer voxel index, 0 to 768)
2. `y` coordinate (integer voxel index, 0 to 768)
3. `z` coordinate (integer voxel index, 0 to 768)
4. Voxel value (in MeV)
5. Energy deposit `dE` (in MeV)
6. Absolute time in nanoseconds
7. Number of electrons
8. `dx` in millimeters

Example:

```python
import h5py

EVENT_IDX = 0

with h5py.File("/path/to/dataset/train/generic_v2_196200_v2.h5", "r") as h5f:
    point_flat = h5f["point"][EVENT_IDX]
    points = point_flat.reshape(-1, 8)  # (N, 8)
```

### `cluster` dataset

Each entry of `cluster` corresponds to the set of clusters for a single event. After reshaping, each row corresponds to a cluster:

Shape per event: `(M, 6)`

Columns (per cluster):

1. Number of points in the cluster
2. Fragment ID
3. Group ID
4. Interaction ID
5. Semantic type (class ID, see below)
6. Particle ID (PID, see below)

Example:

```python
with h5py.File("/path/to/dataset/train/generic_v2_196200_v2.h5", "r") as h5f:
    cluster_flat = h5f["cluster"][EVENT_IDX]
    clusters = cluster_flat.reshape(-1, 6)  # (M, 6)
```

### `cluster_extra` dataset

Each entry of `cluster_extra` provides additional per-cluster information for a single event. After reshaping, each row corresponds to a cluster:

Shape per event: `(M, 5)`

Columns (per cluster):

1. Particle mass (from PDG)
2. Particle momentum (magnitude)
3. Particle vertex `x` coordinate
4. Particle vertex `y` coordinate
5. Particle vertex `z` coordinate

Example:

```python
with h5py.File("/path/to/dataset/train/generic_v2_196200_v2.h5", "r") as h5f:
    cluster_extra_flat = h5f["cluster_extra"][EVENT_IDX]
    cluster_extra = cluster_extra_flat.reshape(-1, 5)  # (M, 5)
```

### Cluster and point ordering

Points in the `point` array are ordered by the cluster they belong to. For a given event:

* Let `clusters[i, 0]` be the number of points in cluster `i`
* Then points for cluster `0` occupy the first `clusters[0, 0]` rows in `points`
* Points for cluster `1` occupy the next `clusters[1, 0]` rows, and so on

This ordering allows you to map cluster-level attributes (`cluster` and `cluster_extra`) back to the underlying points.

### Removing low energy deposits (LED)

By construction, the first cluster in each event (`cluster[0]`) corresponds to amorphous low energy deposits or blips: these are treated as uncountable "stuff" and labeled as LED.

To remove LED points from an event:

```python
EVENT_IDX = 0

with h5py.File("/path/to/dataset/train/generic_v2_196200_v2.h5", "r") as h5f:
    point_flat = h5f["point"][EVENT_IDX]
    cluster_flat = h5f["cluster"][EVENT_IDX]

points = point_flat.reshape(-1, 8)      # (N, 8)
clusters = cluster_flat.reshape(-1, 6)  # (M, 6)

# Number of points belonging to LED (cluster 0)
n_led_points = clusters[0, 0]

# Drop LED points
points_no_led = points[n_led_points:]  # points belonging to non-LED clusters
```

LED clusters also have special values in the ID fields, described in the label schema below.

## Label schema

This section summarizes the label conventions used in the dataset for semantic segmentation, particle identification, and instance or interaction level grouping.

### Semantic segmentation classes

Semantic labels are given by the field in `cluster[:, 4]`.
The mapping is:

| Semantic ID | Class name |
| ----------- | ---------- |
| 0           | Shower     |
| 1           | Track      |
| 2           | Michel     |
| 3           | Delta      |
| 4           | LED        |

Here, LED denotes low energy deposits or amorphous "stuff" that is not counted as a particle instance.

To perform semantic segmentation at the point level, use the cluster ordering:

1. Expand cluster semantic labels to per-point labels according to the point counts per cluster.
2. Optionally remove LED points (Semantic ID 4) as shown above.

### Particle identification (PID) labels

Particle identification uses the Particle ID field in `cluster[:, 5]`.
The mapping is:

| ID | Particle type                       |
| --- | ---------------------------------- |
| 0   | Photon                             |
| 1   | Electron                           |
| 2   | Muon                               |
| 3   | Pion                               |
| 4   | Proton                             |
| 5   | Kaon (not present in this dataset) |
| 6   | None (LED)                         |

LED clusters that correspond to low energy deposits use `PID = 6`.
These clusters are typically also `Semantic ID = 4` and treated as "stuff".

### Instance and interaction IDs

The `cluster` dataset contains several integer IDs to support different grouping granularities:

* **Fragment ID** (`cluster[:, 1]`):
  Identifies contiguous fragments of a particle. Multiple fragments may belong to the same particle.

* **Group ID** (`cluster[:, 2]`):
  Identifies particle-level instances. All clusters with the same group ID correspond to the same physical particle.

  * Use `Group ID` for particle instance segmentation or particle-level identification tasks.

* **Interaction ID** (`cluster[:, 3]`):
  Identifies interaction-level groups. All particles with the same interaction ID belong to the same interaction (for example a neutrino interaction and its secondaries).

  * Use `Interaction ID` for interaction-level segmentation or classification.

For LED clusters, all three IDs

* Fragment ID
* Group ID
* Interaction ID

are set to `-1`. This differentiates LED clusters from genuine particle or interaction instances.

## Reconstruction Tasks

Typical uses of this dataset include:

* **Semantic segmentation**:
  Predict voxelwise semantic labels (shower, track, Michel, delta, LED) using the `Semantic type` field.

* **Particle-level segmentation and PID**:

  * Use `Group ID` to define particle instances.
  * Use `PID` to assign particle type (photon, electron, muon, pion, proton, None).

* **Interaction-level reconstruction**:

  * Use `Interaction ID` to group particles belonging to the same physics interaction.
  * Use `cluster_extra` for per-particle momentum and vertex information.

## Getting started

A [Colab notebook](https://colab.research.google.com/drive/1x8WatdJa5D7Fxd3sLX5XSJiMkT_sG_im) is provided for a hands-on introduction to loading and inspecting the dataset.


## Citation

```bibtex
@misc{young2025particletrajectoryrepresentationlearning,
      title={Particle Trajectory Representation Learning with Masked Point Modeling}, 
      author={Sam Young and Yeon-jae Jwa and Kazuhiro Terao},
      year={2025},
      eprint={2502.02558},
      archivePrefix={arXiv},
      primaryClass={hep-ex},
      doi={10.48550/arXiv.2502.02558},
      url={https://arxiv.org/abs/2502.02558},
}
```