Update README.md

README.md

@@ -12,4 +12,257 @@ pretty_name: >-
   Physics - Medium
 size_categories:
 - 100K<n<1M
----
+---
+
+# Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics
+
+We provide the 156 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_10880_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 (what the detector records)
+5. Energy deposit `dE`
+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},
+      url={https://arxiv.org/abs/2502.02558},
+}
+```