source-README.md

metadata

language:
  - en
license: apache-2.0
pretty_name: ProbeBench classical reference selection v1
tags:
  - optics
  - wavefront-sensing
  - scientific-computing
configs:
  - config_name: default
    data_files:
      - split: public_tuning
        path: images/**

ProbeBench classical reference selection v1

This export retains 4032 episodes and 30912 acquired images across all 14 candidates and 12 independent base wavefronts. The split is public_tuning: these are public parameter-selection data, not training/test splits, held-out performance estimates, telescope measurements or language-model results.

images/metadata.jsonl preserves candidate, family, photon/exposure cell, base, conjugate member, RMS stratum and detector-replicate identity. selected_reference identifies the single global choice per family; non-winning candidates remain included. The command and expected-photon fields describe the actual exposure. Columns prefixed evaluator_ are latent simulator labels and must not be supplied as controller inputs. Cohort identity and selection flags are analysis labels, not additional observations.

raw/ stores the corresponding signed float64 electron arrays. raw_array paths are relative to this export root, while file_name is relative to images/. PNGs use log1p(max(electrons, 0))/log1p(photons per exposure), fixed limits 0–1, the magma map, and image origin lower. Display clipping does not alter the raw arrays. No final image is invented for an unmeasured command; every image corresponds to a retained exposure.

episodes.jsonl preserves complete native case records under record, including latent wavefronts, seeds, all optimizer diagnostics, and final-command observation indices. A final command measured earlier is distinct from the last exposure and from an unmeasured submission. Episode records and configuration.json contain evaluator information and are not observation inputs. selected-settings.json preserves the six globally selected configurations. provenance.json binds the source fingerprints, full replay receipt, generator and rendering dependencies. manifest.json inventories this export separately from the original archive.

Load locally without a second image cache

from pathlib import Path
from datasets import load_dataset
import numpy as np

root = Path("work/selection-imagefolder")
frames = load_dataset("imagefolder",
    data_files={"public_tuning": str(root / "images" / "**")},
    split="public_tuning", streaming=True)
first = next(iter(frames))
electrons = np.load(root / first["raw_array"], allow_pickle=False)

The layout follows the official ImageFolder metadata format and local loading configuration.

The model is static monochromatic scalar Fourier optics with six controlled modes, an exactly matched estimator, and Poisson–Gaussian detector noise. Conjugates, replicates and shared budget prefixes are correlated; they do not add independent wavefronts. Keep all numerical failures and non-winning results in analyses. Recorded decision times do not establish comparable algorithm latency or real-time guarantees. Hashes establish consistency with the pinned release, not authorship. Shared file locking excludes cooperating writers; keep the input tree unchanged during export. An interrupted export has no completed manifest and must not be treated as a dataset.