Aperture Photometry Tool Versus SExtractor for Noncrowded Fields
Bibliographic record
Abstract
Outputs from new software program Aperture Photometry Tool (APT) are compared with similar outputs from SExtractor for sources extracted from R -band optical images acquired by the Palomar Transient Factory (PTF), infrared mosaics constructed from Spitzer Space Telescope images, and a processed visible/near-infrared image from the Hubble Legacy Archive (HLA). Two large samples from the PTF images are studied, each containing around 3 × 10 3 sources from noncrowded fields. The median values of source-intensity relative percentage differences between the two software programs, computed separately for two PTF samples, are +0.13% and +0.17%, with corresponding statistical dispersions of 1.43% and 1.84%, respectively. For the Spitzer mosaics, a similar large sample of extracted sources for each of channels 1–4 of Spitzer 's Infrared Array Camera (IRAC) are analyzed with two different sky annulus sizes, and we find that the median and modal values of source-intensity relative percentage differences between the two software programs are between -0.5% and +2.0%, and the corresponding statistical dispersions range from 1.4 to 6.7%, depending on the Spitzer IRAC channel and sky annulus. The results for the HLA image are mixed, as might be expected for a moderately crowded field. The comparisons for the three different kinds of images show that there is generally excellent agreement between APT and SExtractor. Differences in source-intensity uncertainty estimates for the PTF images amount to less than 3% for the PTF sources, and these are potentially caused by SExtractor's omission of the sky background uncertainty term in the formula for source-intensity uncertainty, as well as differing methods of sky background estimation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".