The Herschel Fornax Cluster Survey II: FIR properties of optically selected Fornax cluster galaxies
Bibliographic record
Abstract
The Herschel Fornax Cluster Survey is a deep, far-infrared (FIR) survey of the Fornax cluster. The survey is in five Herschel bands (100–500 μm) and covers an area of 16 deg2 centred on NGC 1399. This paper presents photometry, detection rates, dust masses, and temperatures using an optically selected sample from the Fornax Cluster Catalogue. Our results are compared with those previously obtained using data from the Herschel Virgo Cluster Survey (HeViCS). In Fornax, we detect 30 of the 237 (13 per cent) optically selected galaxies in at least one Herschel band. The global detection rates are significantly lower than Virgo, reflecting the morphological make up of each cluster – Fornax has a lower fraction of late-type galaxies. For galaxies detected in at least three bands, we fitted a modified blackbody with a β = 2 emissivity. Detected early-type galaxies (E/S0) have a mean dust mass, temperature, and dust-to-stars ratio of log10(〈Mdust〉/M⊙) = 5.82 ± 0.20, 〈Tdust〉 = 20.82 ± 1.77 K, and |$\log _{10}(M_{\rm dust}/M_{\text{stars}}) = -3.87 \pm 0.28$|, respectively. Late-type galaxies (Sa to Sd) have a mean dust mass, temperature, and dust-to-stars ratio of log10(〈Mdust〉/M⊙) = 6.54 ± 0.19, 〈Tdust〉 = 17.47 ± 0.97 K, and |$\log _{10}(M_{\rm dust}/M_{\text{stars}}) = -2.93 \pm 0.09$|, respectively. The different cluster environments seem to have had little effect on the FIR properties of the galaxies and so we conclude that any environment-dependent evolution has taken place before the cluster was assembled.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".