DeepLearnPhysics/PILArNet-M

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 dataset, consisting of ~1.2 million events from liquid argon time projection chambers (LArTPCs).

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:

/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:

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:

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:

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:

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 is provided for a hands-on introduction to loading and inspecting the dataset.

Citation

@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},
}