STRRINGS: STReams in Residual Images of Nearby GalaxieS
Bibliographic record
Abstract
ABSTRACT Tidal features from galaxy mergers, particularly stellar streams, offer valuable insights into galaxy assembly and dark matter halo properties. This paper aims to identify a large sample of nearby stellar streams suitable for detailed modelling and comparison with simulations to enable population-level constraints on halo properties. We visually inspect and compile a tidal feature catalogue for 19 387 galaxies with redshift $z \le 0.02$ from the Siena Galaxy Atlas 2020 using original, model, and residual images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. Residual images, produced by subtracting models of all sources, enhance the detectability of faint asymmetries such as tidal features. We find that $11.9 \pm 0.2{{\ \rm per\ cent}}$ of galaxies host detectable tidal features, more frequently around early- than late-type galaxies. The tidal feature fraction increases with stellar mass, from $2.4 \pm 0.4{{\ \rm per\ cent}}$ at $\sim 10^8\, \mathrm{M}_\odot$ to $36.5 \pm 1.2{{\ \rm per\ cent}}$ at $\sim 5\times 10^{11}\, \mathrm{M}_\odot$. From this, we present the first release of STRRINGS: STReams in Residual Images of Nearby GalaxieS, a subsample of 35 galaxies with long, narrow streams suitable for modelling. We segment these streams and derive their geometry, surface brightness, colours, and stellar masses. The median g-band surface brightness is 26.8 mag arcsec$^{-2}$, reaching 27.5 mag arcsec$^{-2}$ for the faintest stream. Mass ratios are consistent with minor mergers, and we identify five potential dwarf galaxy progenitors. Our streams are typically longer (median 124 kpc) than the literature, with comparable widths. Stream mass correlates with length and colour, and wider streams lie at larger galactocentric radii. STRRINGS will be expanded and used to constrain halo properties in future work.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".