AstroPRISM

Private research staging data for aligning PRISM with astronomical images and spectra. The package contains 646 object-level candidates with a fixed, source-group-safe split (516 train / 65 validation / 65 test).

Configs and training status

Config	Splits	Rows	Training status
image_caption	train, validation, test	646	Only train rows are allowed; captions are accepted weak supervision.
spectrum_caption	train, validation, test	646	All captions are accepted, reviewed, and dataset-ready; only train rows allow gradient training.

Every spectrum-caption row has caption_status=accepted, review_status=accepted, reviewed=true, and dataset_ready=true. Dataset readiness means the pair is approved for its assigned split; it does not grant gradient-training permission. Only train rows have training_allowed=true. Validation and test rows have role=held_out_evaluation and training_allowed=false so they remain clean evaluation supervision.

The embedded image and spectrum_plot columns are inspection previews. Model inputs are the calibrated arrays in assets/raw/images/ and assets/raw/spectra/. Released AION embeddings, download caches, and encoder weights are intentionally excluded.

Direct raw-array indexing

candidate_index and raw_array_index are identical. They index axis 0 of every monolithic NumPy array. This remains true in all Parquet splits; indices are deliberately not renumbered.

from huggingface_hub import hf_hub_download
import numpy as np

path = hf_hub_download(
    repo_id="Sand33p/AstroPRISM",
    repo_type="dataset",
    filename="assets/raw/images/image_array.npy",
)
images = np.load(path, mmap_mode="r")
image = images[row["candidate_index"]]  # float32 [4, 160, 160]

Load pair metadata with an authenticated Hugging Face session:

from datasets import load_dataset

images = load_dataset("Sand33p/AstroPRISM", "image_caption")
spectra = load_dataset("Sand33p/AstroPRISM", "spectrum_caption")

See metadata/candidates.jsonl for the authoritative cross-modal identity and split mapping, provenance/SOURCES.json for immutable upstream pins, LICENSES.md for source-specific terms, and SHA256SUMS for integrity verification.

Important limitations

• Image descriptions are synthetic weak supervision and can contain unsupported interpretation.
• Spectrum captions were generated from deterministic measured facts and accepted by user instruction.
• Spectrum flux units were not retained explicitly in the extracted local arrays.
• Object-level source groups must stay in their assigned split across every derived task.
• Raw scientific data retain their original survey terms and required acknowledgements.