The HST Data Archive as a Discovery Tool: First Experiment
Bibliographic record
Abstract
The Space Telescope European Coordinating Facility and the Canadian Astronomy Data Centre have started a projet aimed at augmenting the way an astronomer an query the Hubble Space Telescope science archive. Today the HST archive contains more than 70,000 WFPC2 expo-sures. We are preparing an automatic pipeline that will extract several parameters from those images. A list of all the sources seen by WFPC2, along with photo-metrical, morphological and astrometric measurements of both the detected astronomical sources and of the background will be extracted. Astronomers will make use of such a database to query the archive in a more sophisticated and scientific manner, that is in the way they think, not in the way the archive is built. This new approach will allow queries of the kind: search for high latitude fields having a certain limiting magnitude and where the density of extended sources is greater than a certain threshold. Nowadays, with the advent of huge detectors over big fields of view, such an approach becomes a must. The catalog of extracted objects will be immediately useful to prepare a wide range of scientific programs for the 8 - 10 meter class telescopes, e.g., multi-object spectroscopy of HST sources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".