Low surface brightness structures from annotated deep CFHT images: effects of the host galaxy’s properties and environment
Bibliographic record
Abstract
ABSTRACT Hierarchical galactic evolution models predict that mergers drive galaxy growth, producing low surface brightness (LSB) tidal features that trace galaxies’ late assembly. These faint structures encode information about past mergers and are sensitive to the properties and environment of the host galaxy. We investigated the relationships between LSB features and their hosts in a sample of 475 nearby massive galaxies spanning diverse environments (field, groups, Virgo cluster) using deep optical imaging from the Canada–France–Hawaii Telescope (MATLAS, UNIONS/CFIS, VESTIGE, NGVS). Using Jafar, an online annotation tool, we manually annotated tidal features, including 199 tidal tails and 100 streams. Geometric and photometric measurements were extracted to analyse their dependence on galaxy mass, environment, and internal kinematics. At our surface brightness limit of 29 mag arcsec$^{-2}$, tidal features contribute 2 per cent of total galaxy luminosity. They are detected in 36 per cent of galaxies, with none fainter than 27.8 mag arcsec$^{-2}$. The most massive galaxies are twice as likely to host tidal debris. Although small-scale interactions increase the frequency of tidal features, the large-scale environment (Virgo cluster versus field/group) does not influence it. An anticorrelation between this frequency and rotational support is found, but may reflect the mass-driven effect. We release our data base of annotated features for deep learning applications. Our findings confirm that galaxy mass is the dominant factor influencing tidal feature prevalence, consistent with hierarchical formation models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".