Investigating the Merger Origin of Early-type Galaxies using Ultra-deep Optical Images
Bibliographic record
Abstract
Abstract The mass assembly of galaxies leaves various imprints on their surroundings, such as shells, streams and tidal tails. The frequency and properties of these fine structures depend on the mechanism driving the mass assembly: e.g. a monolithic collapse, rapid cold-gas accretion followed by violent disk instabilities, minor mergers or major dry/wet mergers. Therefore, by studying the outskirts of galaxies, one can learn about their main formation mechanism. I present here our on-going work to characterize the outskirts of Early-Type Galaxies (ETGs), which are powerful probes at low redshift of the hierarchical mass assembly of galaxies. This work relies on ultra–deep optical images obtained at CFHT with the wide-field of view MegaCam camera of field and cluster ETGs obtained as part of the ATLAS 3D and NGVS projects. State of the art numerical simulations are used to interpret the data. The images reveal a wealth of unknown faint structures at levels as faint as 29 mag arcsec −2 in the g-band. Initial results for two galaxies are presented here.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".