The Tully—Fisher relation of distant cluster galaxies
Bibliographic record
Abstract
We have measured maximum rotation velocities (Vrot) for a sample of 111 emission-line galaxies with 0.1 ≲ z ≲ 1, observed in the fields of six clusters. From these data we construct ‘matched’ samples of 58 field and 22 cluster galaxies, covering similar ranges in redshift (0.25 ≤ z ≤ 1.0) and luminosity (MB ≤ − 19.5 mag), and selected in a homogeneous manner. We find the distributions of MB, Vrot and scalelength to be very similar for the two samples. However, using the Tully-Fisher relation (TFR) we find that cluster galaxies are systematically offset with respect to the field sample by −0.7 ± 0.2 mag. This offset is significant at 3σ and persists when we account for an evolution of the field TFR with redshift. Extensive tests are performed to investigate potential differences between the measured emission lines and derived rotation curves of the cluster and field samples. However, no such differences that could affect the derived Vrot values and account for the offset are found. The most likely explanation for the TFR offset is that giant spiral galaxies in distant clusters are on average brighter, for a given rotation velocity, than those in the field. This could be due to enhanced star formation caused by an initial interaction with the intracluster medium. As our selection favours galaxies with strong emission lines, this effect may not apply to the entire cluster spiral population, but does imply that strongly star-forming spirals in clusters are more luminous than those in the field, and possibly have higher star formation rates. However, the possibility that this TFR offset is a mass-related effect, e.g. due to the stripping of galaxy dark matter haloes, is not excluded by our data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".