The Fornax 3D project: Thick disks in a cluster environment
Bibliographic record
Abstract
We have used deep MUSE observations to perform a stellar-kinematic and population analysis of FCC 153 and FCC 177, two edge-on S0 galaxies in the Fornax cluster. The geometrical definition of the different structural components of these two galaxies allows us to describe the nature of their thick disks. These are both old, relatively metal poor and [Mg/Fe]-enhanced, and their star formation history (SFH) reveals a minor younger component whose chemical properties suggest its later accretion. Moreover, the outer regions of these geometrically defined thick disks show higher values of metallicity and lower values of [Mg/Fe]. These stars probably formed in the thin-disk region and they were dynamically heated to form the flares present in these two galaxies. We propose different formation scenarios for the three populations of these thick disks: in-situ formation, accretion and disk heating. A clear distinction in age is found between the metal poor and [Mg/Fe]-enhanced thick disks (old, ∼12 − 13 Gyr), and the metal rich and less [Mg/Fe]-enhanced thin disks (young, ∼4 − 5 Gyr). These two galaxies show signs of relatively recent star formation in their thin disks and nuclear regions. While the thin disks show more continuous SFHs, the nuclei display a rather bursty SFH. These two galaxies are located outside of the densest region of the Fornax cluster where FCC 170 resides. This other edge-on S0 galaxy has recently been studied, and we have compared and discussed our results with this previous study. The differences between these three galaxies, at different distances from the cluster center, suggest that the environment can have a strong effect on the galaxy evolutionary path.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".