Blame for README.md

---
language:
- en
license: apache-2.0
pretty_name: ProbeBench classical reference selection
size_categories:
- 10K<n<100K
tags:
- optics
- wavefront-sensing
- scientific-computing
configs:
- config_name: default
  data_files:
  - split: public_tuning
    path: data/*.parquet
---

# ProbeBench: classical reference selection

Measured focal-plane exposures from a simulated wavefront-control benchmark. Each record connects a detector image to the applied optical-path command, photon budget, episode identity, and exact signed detector array.

This release contains **30,912 exposures from 4,032 episodes**, covering **14 classical candidates and 12 independent base wavefronts**. It retains every candidate, including configurations that were not selected, and all optimizer diagnostics. The single split, `public_tuning`, supports inspection and reproduction of parameter selection. It does not provide held-out performance estimates.

[Code](https://github.com/noetherlabs/probebench) · [Methods and results](https://github.com/noetherlabs/probebench/blob/main/docs/parameter-selection-results.md) · [Native records](https://github.com/noetherlabs/probebench/releases/tag/classical-selection-v1-20261016) · [Measurement replay](https://github.com/noetherlabs/probebench/releases/tag/classical-selection-replay-v1-20261017)

## Load an exposure

```python
from datasets import load_dataset

frames = load_dataset(
    "noetherlabs/probebench",
    split="public_tuning",
    revision="classical-selection-v1",
    streaming=True,
)
frame = next(iter(frames))
frame["image"]  # PIL image; display rendering, not detector values
```

The images are embedded in Parquet shards. Streaming reads avoid creating a second complete dataset cache. The revision tag identifies this fixed release; record the resolved commit when reporting derived results.

## Read the detector values

The `raw_archive` column identifies a ZIP shard. Its `raw_array` member contains the original NumPy file, including signed float64 values. Loading a member into memory requires no archive extraction.

```python
import hashlib
import io
import zipfile

import numpy as np
from huggingface_hub import hf_hub_download

archive = hf_hub_download(
    "noetherlabs/probebench",
    filename=frame["raw_archive"],
    repo_type="dataset",
    revision="classical-selection-v1",
)
with zipfile.ZipFile(archive) as z:
    payload = z.read(frame["raw_array"])
assert hashlib.sha256(payload).hexdigest() == frame["raw_array_sha256"]
electrons = np.load(io.BytesIO(payload), allow_pickle=False)
```

## Record structure

| Fields | Meaning |
| --- | --- |
| `image`, `image_sha256` | Original PNG bytes and their SHA-256 digest. |
| `episode_id`, `exposure_index`, `file_name` | Identity and original image filename. |
| `applied_opd_nm`, `expected_photons`, `cumulative_expected_photons` | Applied command and acquired-exposure budgets. |
| `candidate_id`, `family`, `selected_reference` | Candidate identity and the global selection flag for each family. |
| `base_wavefront_id`, `member`, `stratum_id`, `replicate` | Dependence structure: base wavefront, conjugate member, RMS stratum, and detector replicate. |
| `raw_archive`, `raw_array`, `raw_array_sha256` | Archive, member path, and digest of the unchanged detector array. |
| `evaluator_wfe_nm`, `evaluator_strehl_peak_ratio` | Latent simulator labels for analysis. |

Evaluator labels, cohort identities, selection flags, and simulator seeds are **not controller observations**. `episodes.jsonl` preserves complete native episode records, including optimizer diagnostics, seeds, latent wavefronts, and final-command observation indices. A final command measured earlier is distinct from the last exposure or an unmeasured submission.

`selected-settings.json` records six globally selected family configurations. `configuration.json` specifies the experiment. `source-manifest.json` binds the original ImageFolder export; `source-metadata.jsonl` and `source-README.md` preserve its metadata and format description. `provenance.json` records the native archive and rendering provenance. The publication manifest describes the Parquet and ZIP layout. These source artifacts contain evaluator information and are intended for reproduction and analysis.

## Model and interpretation

The simulator uses static, monochromatic scalar Fourier optics with six controlled modes, a matched estimator, and Poisson–Gaussian detector noise. Conjugate members, detector replicates, and shared budget prefixes are correlated; the experiment has **12 independent base wavefronts**, not 4,032 independent wavefronts.

PNGs display `log1p(max(electrons, 0)) / log1p(photons_per_exposure)` with fixed limits 0–1, the magma colormap, and lower image origin. The display clips negative values; the raw arrays preserve them. Every image corresponds to a retained exposure. No image is synthesized for an unmeasured command.

These data establish neither telescope performance nor language-model performance. Recorded decision times do not establish comparable algorithm latency or a real-time control deadline. Keep failed and nonselected candidates in analyses, and separate tuning results from any subsequent evaluation.

## Citation

```bibtex
@misc{noetherlabs2026probebenchselection,
  title  = {ProbeBench: Classical Reference Selection},
  author = {{Noether Labs}},
  year   = {2026},
  url    = {https://huggingface.co/datasets/noetherlabs/probebench}
}
```

Released under Apache-2.0. See [LICENSE](LICENSE) and [NOTICE](NOTICE).