--- license: unknown tags: - physics - particle-physics - high-energy-physics - detector-simulation - cherenkov - eic - drich --- # dRICH simulated hit data (bljul21 baseline) Real, non-synthetic simulated photon-hit data from the EPIC dRICH detector (the dual-radiator RICH — aerogel + gas — of the EIC's EPIC experiment), for 4 charged-particle species. This is the exact data used to train and evaluate a Vision-Transformer particle-ID baseline (**ViT-BL**). **No injected noise, no synthetic mixing, no QE (quantum-efficiency) hit loss, no pixel-gap simulation, no augmentation of any kind, at any stage** (generation, training, or testing). Every hit in every file here is a real simulated photon hit. ## Files in this repo | file | size | contents | |---|---|---| | `dataset.npz` | 18 MB | per-event metadata for all 600,020 events | | `hits_xy.npy` | 1.70 GB | every photon hit's (x, y) sensor-plane position [mm], all events concatenated | | `hits_t.tar.gz` | 1.39 GB | the raw per-event source: 600,020 individual `hits_{PID}_{MOM}_{ETA}_{PHI}.npz` files, each with `cellID` (uint64) + `time` (float32, ns), before cellID was resolved to a physical position | | `cellid_positions.npz` | 6.2 MB | sensor geometry lookup: `cellids` (sorted uint64) → `xyz` (float32, mm) for all 322,560 real dRICH sensor pixels. Needed to resolve raw `cellID` values (from `hits_t.tar.gz`, or from a fresh simulation) into physical hit positions | ### `dataset.npz` fields | key | dtype / shape | meaning | |---|---|---| | `label` | int8, (600020,) | 0=electron, 1=pion, 2=kaon, 3=proton | | `mom` | float32, (600020,) | generated momentum [GeV/c] | | `eta` | float32, (600020,) | generated pseudorapidity | | `phi` | float32, (600020,) | generated azimuth [rad] | | `center` | float32, (600020,2) | per-event median hit (x,y) [mm] (only used for zoomed/crop visualizations) | | `offsets` | int64, (600021,) | `hits_xy[offsets[j]:offsets[j+1]]` = event `j`'s hits | | `split` | int8, (600020,) | 0=train, 1=val, 2=test (80/10/10, split independently per species) | `hits_xy.npy` contains **all** splits together, undivided — train, val, and test hits all live in the same array; only `dataset.npz['split']` tells you which is which. ## Particle ID convention Standard PDG codes: `11`=electron, `211`=pion(+), `321`=kaon(+), `2212`=proton. Filenames (and the raw `hits_t.tar.gz` contents) follow `{PID}_{MOM}_{ETA}_{PHI}` — momentum in GeV/c, eta as generated, phi in radians. ## Generated kinematic ranges - momentum: 0.5–60.5 GeV/c, in 0.1 GeV/c steps (601 grid values) - eta: 1.5–3.5, in 0.1 steps (21 grid values) - phi: 0–2π, in 0.1 rad steps (63 grid values) Each species independently randomly sampled ~150,000 (p, eta, phi) triples from that ~795,000-point grid — this is **not** a shared grid reused across species; any overlap between species' kinematic points is coincidental (matches the ~19% overlap expected from independent random sampling from the same grid). ## Aerogel Cherenkov threshold (n = 1.026) `p_thr = mass / sqrt(n^2 - 1)`. Used to define the "above threshold" (ATH) vs "below threshold" (BTH) splits in the companion ViT-BL baseline. | species | mass [GeV] | p_thr [GeV/c] | |---|---|---| | electron | 0.000511 | 0.0022 | | pion | 0.139570 | 0.6081 | | kaon | 0.493677 | 2.1510 | | proton | 0.938272 | 4.0881 | ## How this data was created 1. **hepmc generation** — one single-event hepmc file per (PID, momentum, eta, phi) combination, covering the full grid above. 2. **Geant4 simulation** — each hepmc file run through the EPIC detector simulation (`drich-dev/simulate.py`), one event per output file. 3. **Extraction** — `DRICHHits/cellID` and `DRICHHits/time` pulled from each simulation ROOT file (`uproot` + `awkward`), one `.npz` per event containing the raw `cellID`/`time` arrays — this is `hits_t.tar.gz`. The raw simulation ROOT files were deleted immediately after extraction to save disk space (not recoverable — only the extracted hits remain). 4. **Geometry resolution** — every unique `cellID` resolved to a global (x, y, z) mm position via `dd4hep`'s `CellIDPositionConverter`, run against the real EPIC/dRICH detector geometry. This lookup table is `cellid_positions.npz`, included in this repo. 5. **Consolidation** — all 600,020 events' hits (now resolved to x,y) concatenated into `hits_xy.npy`, with per-event metadata gathered into `dataset.npz`. ## Quick start: load an event's hits ```python import numpy as np from huggingface_hub import hf_hub_download REPO = "deepaksamuel-cuk/simhits" dataset_path = hf_hub_download(REPO, "dataset.npz", repo_type="dataset") hits_path = hf_hub_download(REPO, "hits_xy.npy", repo_type="dataset") d = np.load(dataset_path) label, mom, eta, phi, offsets = d["label"], d["mom"], d["eta"], d["phi"], d["offsets"] hits = np.load(hits_path, mmap_mode="r") # ~1.6 GiB -- mmap avoids loading it all into RAM j = 0 # any event index, 0 <= j < 600020 a, b = offsets[j], offsets[j + 1] pts = np.asarray(hits[a:b], dtype=np.float32) # this event's (x, y) hit positions [mm] print(label[j], mom[j], eta[j], phi[j], len(pts), "hits") ``` ## Rasterizing into a training-style image Pixel value = `log1p(hit count)` — cells with more hits get more weight (brighter), full detector extent (384x384 px over a 3540 mm window, matching the real dRICH sensor plane's physical size). Multiple hits landing in the same sensor cell are **summed**, not overwritten or capped — `np.add.at` is used specifically because it correctly accumulates duplicate indices, unlike plain indexed assignment. ```python FULL_IMG, FULL_WINDOW = 384, 3540.0 def rasterize(pts, img_size=FULL_IMG, window=FULL_WINDOW): img = np.zeros((img_size, img_size), dtype=np.float32) if len(pts) == 0: return img rel = (pts - (-window / 2)) * (img_size / window) ij = np.floor(rel).astype(np.int64) ok = (ij[:, 0] >= 0) & (ij[:, 0] < img_size) & (ij[:, 1] >= 0) & (ij[:, 1] < img_size) ij = ij[ok] np.add.at(img, (ij[:, 1], ij[:, 0]), 1.0) # accumulate, never overwrite return np.log1p(img) img = rasterize(pts) # (384, 384) float32, ready to imshow() or feed to a model ``` ## Ready-to-run code in this repo - **`view_hits.py`** — command-line script. Pick a PID/momentum/eta/phi, it finds the closest real matching event (the grid above means your exact input almost never exists as an event) and saves a PNG of the rasterized hit image: ```bash python view_hits.py --pid 2212 --mom 30 --eta 2.0 --phi 3.14 --out proton.png ``` - **`colab_hit_viewer.ipynb`** — the same lookup, as an interactive Colab notebook (dropdown + text fields, live plotly plot). Upload it to https://colab.research.google.com directly. ## Related model A ViT-BL baseline model (Vision Transformer, trained from scratch, no ImageNet pretraining) trained on this data's above-threshold events is published separately at [deepaksamuel-cuk/drich-vit-baseline](https://huggingface.co/deepaksamuel-cuk/drich-vit-baseline), including a `predict_from_root.py` script that runs the model directly on a raw simulation `.root` file using this repo's `cellid_positions.npz`.