The Palomar Transient Factory Photometric Calibration
Bibliographic record
Abstract
The Palomar Transient Factory (PTF) provides multiple epoch imaging for a large fraction of the celestial sphere. Here, we describe the photometric calibration of the PTF data products that allows the PTF magnitudes to be related to other magnitude systems. The calibration process utilizes Sloan Digital Sky Survey (SDSS) r ∼ 16 mag point-source objects as photometric standards. During photometric conditions, this allows us to solve for the extinction coefficients and color terms and to estimate the camera illumination correction. This also enables the calibration of fields that are outside the SDSS footprint. We test the precision and repeatability of the PTF photometric calibration. Given that PTF is observing in a single filter each night, we define a PTF calibrated magnitude system for the R band and g band. We show that, in this system, ≈59% (47%) of the photometrically calibrated PTF R -band ( g -band) data achieve a photometric precision of 0.02–0.04 mag and have color terms and extinction coefficients that are close to their average values. Given the objects' color, the PTF magnitude system can be converted to other systems. Moreover, a night-by-night comparison of the calibrated magnitudes of individual stars observed on multiple nights shows that they are consistent to a level of ≈0.02 mag. Most of the data that were taken under nonphotometric conditions can be calibrated relative to other epochs of the same sky footprint obtained during photometric conditions. We provide a concise guide describing how to use the PTF photometric-calibration data products, as well as the transformations between the PTF magnitude system and the SDSS and Johnson-Cousins systems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.032 |
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".