The ACS Fornax Cluster Survey. I. Introduction to the Survey and Data Reduction Procedures
Bibliographic record
Abstract
The Fornax Cluster is a conspicuous cluster of galaxies in the southern hemisphere and the second largest collection of early-type galaxies within ≲20 Mpc after the Virgo Cluster. In this paper, we present a brief introduction to the ACS Fornax Cluster Survey—a program to image, in the F475W ( g 475 ) and F850LP ( z 850 ) bandpasses, 43 early-type galaxies in Fornax using the Advanced Camera for Surveys (ACS) on the Hubble Space Telescope . Combined with a companion survey of Virgo, the ACS Virgo Cluster Survey, this represents the most comprehensive imaging survey to date of early-type galaxies in cluster environments in terms of depth, spatial resolution, sample size, and homogeneity. We describe the selection of the program galaxies, their basic properties, and the main science objectives of the survey, which include the measurement of luminosities, colors, and structural parameters for globular clusters associated with these galaxies, an analysis of their isophotal properties and surface brightness profiles, and an accurate calibration of the surface brightness fluctuation distance indicator. Finally, we discuss the data reduction procedures adopted for the 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.146 |
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".