Calibration for astrophysics using an artificial star with NIST-traceable distribution of luminous energy (CANDLE) instrument development
Bibliographic record
Abstract
The CANDLE Engineering Demonstration Unit (EDU) was selected by the 2022 APRA program to develop and demonstrate the ability to reach the flux accuracy and range required for an artificial flux calibration star. A critical issue in producing accurate and reliable flux calibration is systematic effects; this EDU is providing a path to deploying an artificial star calibration payload outside Earth’s atmosphere with SI-traceable calibration that enables accurate throughput characterization of astronomical and earth science observatories in space and on the ground. Such a payload could be carried independently on a dedicated platform such as an orbiting satellite, e.g. the Orbiting Configurable Artificial Star (ORCAS), by a star shade at L2, or some other independent platform to enable accurate end-to-end throughput vs. wavelength calibration that can be measured repeatedly throughout the operational lifetime of an observatory. Once calibrated, the observatory is enabled to carry out astrophysical programs whose science objectives demand high accuracy and/or high precision observations. One specific and immediate application is establishing SI-traceable standard stars beyond the current limited set. We show in this paper the progress made in developing this EDU.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".