SURFACE BRIGHTNESS FLUCTUATIONS IN THE<i>HUBBLE SPACE TELESCOPE</i>ACS/WFC F814W BANDPASS AND AN UPDATE ON GALAXY DISTANCES
Bibliographic record
Abstract
We measure surface brightness fluctuation (SBF) magnitudes in the F814W filter and ( g 475 − I 814 ) colors for nine bright early-type Fornax cluster galaxies imaged with the Hubble Space Telescope Advanced Camera for Surveys (ACS). The goal is to achieve the first systematic SBF calibration for the ACS/F814W bandpass. Because of its much higher throughput, F814W is more efficient for SBF studies of distant galaxies than the ACS/F850LP bandpass that has been used to study nearby systems. Over the color range spanned by the sample galaxies, 1.06 < ( g 475 − I 814 ) < 1.32 (AB mag), the dependence of SBF magnitude on ( g 475 − I 814 ) is linear to a good approximation, with slope ∼2. When the F850LP SBF distance measurements from the ACS Fornax Cluster Survey are used to derive absolute magnitudes, the dependence on ( g 475 − I 814 ) becomes extremely tight, with a slope of 1.8 ± 0.2 and a scatter of 0.03 mag. The small observed scatter indicates both that the estimated random errors are correct and that the intrinsic deviations from the SBF–color relation are strongly correlated between the F814W and F850LP bandpasses, as expected. The agreement with predictions from stellar population models is good, both in slope and zero point, indicating that our mean Fornax distance of 20 Mpc is accurate. The models predict curvature in the relation beyond the color limits of our sample; thus, the linear calibration should not be extrapolated naively. In the appendices, we reconsider the Tonry ground-based and Jensen NICMOS SBF distance catalogs; we provide a correction formula to ameliorate the small apparent bias in the former and the offset needed to make the latter consistent with other SBF studies. We also tabulate two new SBF distances to galaxies observed in the ACS Virgo Cluster Survey.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".