The<i>Herschel</i>Virgo Cluster Survey
Bibliographic record
Abstract
To assess the effects of the cluster environment on the different components of the interstellar medium, we analyse the far-infrared (FIR) and submillimetre (submm) properties of a sample of star-forming dwarf galaxies detected by the Herschel Virgo Cluster Survey (HeViCS). We determine dust masses and dust temperatures by fitting a modified black body function to the spectral energy distributions (SEDs). Stellar and gas masses, star formation rates (SFRs), and metallicities are obtained from the analysis of a set of ancillary data. Dust is detected in 49 out of a total 140 optically identified dwarfs covered by the HeViCS field; considering only dwarfs brighter than mB = 18 mag, this gives a detection rate of 43%. After evaluating different emissivity indices, we find that the FIR-submm SEDs are best-fit by β = 1.5, with a median dust temperature Td = 22.4 K. Assuming β = 1.5, 67% of the 23 galaxies detected in all five Herschel bands show emission at 500 μm in excess of the modified black-body model. The fraction of galaxies with a submillimetre excess decreases for lower values of β, while a similarly high fraction (54%) is found if a β-free SED modelling is applied. The excess is inversely correlated with SFR and stellar masses. To study the variations in the global properties of our sample that come from environmental effects, we compare the Virgo dwarfs to other Herschel surveys,such as the Key Insights into Nearby Galaxies: Far-Infrared Survey with Herschel (KINGFISH), the Dwarf Galaxy Survey (DGS), and the HeViCS Bright Galaxy Catalogue (BGC). We explore the relations between stellar mass and Hi fraction, specific star formation rate, dust fraction, gas-to-dust ratio over a wide range of stellar masses (from 107 to 1011 M⊙) for both dwarfs and spirals. Highly Hi-deficient Virgo dwarf galaxies are mostly characterised by quenched star formation activity and lower dust fractions giving hints for dust stripping in cluster dwarfs. However, to explain the large dust-to-gas mass ratios observed in these systems, we find that the fraction of dust removed has to be less than that of the Hi component. The cluster environment seems to mostly affect the gas component and star formation activity of the dwarfs. Since the Virgo star-forming dwarfs are likely to be crossing the cluster for the first time, a longer timescale might be necessary to strip the more centrally concentrated dust distribution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".