Hidden depths in the local Universe: The Stellar Stream Legacy Survey
Bibliographic record
Abstract
Context. Mergers and tidal interactions between massive galaxies and their dwarf satellites are a fundamental prediction of the Lambda-cold dark matter cosmology. These events are thought to provide important observational diagnostics of non-linear structure formation. Stellar streams in the Milky Way and Andromeda are spectacular evidence for ongoing satellite disruption. However, constructing a statistically meaningful sample of tidal streams beyond the Local Group has proven a daunting observational challenge, and the full potential for deepening our understanding of galaxy assembly using stellar streams has yet to be realised. Aims. Here we introduce the Stellar Stream Legacy Survey, a systematic imaging survey of tidal features associated with dwarf galaxy accretion around a sample of ∼3100 nearby galaxies within z ∼ 0.02, including about 940 Milky Way analogues. Methods. Our survey exploits public deep imaging data from the DESI Legacy Imaging Surveys, which reach surface brightness as faint as ∼29 mag arcsec −2 in the r band. As a proof of concept of our survey, we report the detection and broad-band photometry of 24 new stellar streams in the local Universe. Results. We discuss how these observations can yield new constraints on galaxy formation theory through comparison to mock observations from cosmological galaxy simulations. These tests will probe the present-day mass assembly rate of galaxies, the stellar populations and orbits of satellites, the growth of stellar halos, and the resilience of stellar disks to satellite bombardment.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".