MATLAS: a deep exploration of the surroundings of massive early-type galaxies
Bibliographic record
Abstract
The MATLAS project explores the surroundings of a complete sample of nearby massive early-type galaxies with multi-colour deep optical images obtained at the Canada-France Hawaii Telescope. The observing and data reduction techniques ensured the detection of extended low-surface-brightness (LSB) structures, while the high image quality allowed us to identify associated compact objects such as star clusters. A number of scientific topics are addressed with this data-set that are briefly presented in this review: the study of foreground Galactic cirrus at high spatial resolution, telling us about the ISM structure; the characterisation of collisional debris around the galaxies (streams, tails, shells, stellar halos), giving hints on their past merger history; the distribution of dwarf galaxy satellites, including the ultra-diffuse ones, together with their globular cluster population, which are additional tracers of the formation and mass assembly of galaxies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".