Photometric Recovery of Crowded Stellar Fields Observed with<i>HST</i>/WFPC2 and the Effects of Confusion Noise on the Extragalactic Distance Scale
Bibliographic record
Abstract
We explore the limits of photometric reductions of crowded stellar fields observed with the Wide Field and Planetary Camera 2 on board the Hubble Space Telescope . Two photometric procedures, based on the DoPHOT and DAOPHOT/ALLFRAME programs, are tested, and the effects of crowding, complex sky background, and cosmic‐ray contamination are discussed using an extensive set of artificial star simulations. As a specific application of the results presented in this paper, we assess the magnitude of photometric biases on programs aimed at finding Cepheids and determining distances. We find that while the photometry in individual images can be biased too bright by up to 0.2 mag in the most crowded fields due to confusion noise, the effects on distance measurements based on Cepheid variables are insignificant, less than 0.02 mag (1% in distance) even in the most problematic cases. This result, which is at odds with claims that have recently surfaced in the literature, is due to the strict criteria applied in the selection of the variable stars, and the photometric cross‐checks made possible by the availability of multiple exposures in different filters which characterize Cepheid observations.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".