HYPERCALIBRATION: A PAN-STARRS1-BASED RECALIBRATION OF THE SLOAN DIGITAL SKY SURVEY PHOTOMETRY
Bibliographic record
Abstract
ABSTRACT We present a recalibration of the Sloan Digital Sky Survey (SDSS) photometry with new flat fields and zero points derived from Pan-STARRS1. Using point-spread function (PSF) photometry of 60 million stars with 16 < r < 20, we derive a model of amplifier gain and flat-field corrections with per-run rms residuals of 3 millimagnitudes (mmag) in griz bands and 15 mmag in u band. The new photometric zero points are adjusted to leave the median in the Galactic north unchanged for compatibility with previous SDSS work. We also identify transient non-photometric periods in SDSS (“contrails”) based on photometric deviations co-temporal in SDSS bands. The recalibrated stellar PSF photometry of SDSS and PS1 has an rms difference of {9, 7, 7, 8} mmag in griz, respectively, when averaged over 15′ regions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".