The spectroscopy and <i>H</i>-band imaging of Virgo cluster galaxies (SHIVir) survey: data catalogue and kinematic profiles
Bibliographic record
Abstract
ABSTRACT The ‘Spectroscopy and H-band Imaging of Virgo cluster galaxies’ (SHIVir) survey is an optical and near-infrared survey which combines SDSS photometry, deep H-band photometry, and long-slit optical spectroscopy for 190 Virgo cluster galaxies covering all morphological types over the stellar mass range log (M*/M⊙) = 7.8–11.5. We present the spectroscopic sample selection, data reduction, and analysis for this SHIVir sample. We have used and optimized the pPXF routine to extract stellar kinematics from our data. Ultimately, resolved kinematic profiles (rotation curves and velocity dispersion profiles) are available for 133 SHIVir galaxies. A comprehensive data base of photometric and kinematic parameters for the SHIVir sample is presented with grizH magnitudes, effective surface brightnesses, effective and isophotal radii, rotational velocities, velocity dispersions, and stellar and dynamical masses. Parameter distributions highlight some bimodal distributions and possible sample biases. A qualitative study of resolved extended velocity dispersion profiles suggests a link between the so-called ‘sigma-drop’ kinematic profile and the presence of rings in lenticular S0 galaxies. Rising dispersion profiles are linked to early-type spirals or dwarf ellipticals for which a rotational component is significant, whereas peaked profiles are tied to featureless giant ellipticals.
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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 | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".