The ATLAS3D project – XXIX. The new look of early-type galaxies and surrounding fields disclosed by extremely deep optical images
Bibliographic record
Abstract
Galactic archaeology based on star counts is instrumental to reconstruct the past mass assembly of Local Group galaxies. The development of new observing techniques and data reduction, coupled with the use of sensitive large field of view cameras, now allows us to pursue this technique in more distant galaxies exploiting their diffuse low surface brightness (LSB) light. As part of the ATLAS3D project, we have obtained with the MegaCam camera at the Canada–France–Hawaii Telescope extremely deep, multiband images of nearby early-type galaxies (ETGs). We present here a catalogue of 92 galaxies from the ATLAS3D sample, which are located in low- to medium-density environments. The observing strategy and data reduction pipeline, which achieve a gain of several magnitudes in the limiting surface brightness with respect to classical imaging surveys, are presented. The size and depth of the survey are compared to other recent deep imaging projects. The paper highlights the capability of LSB-optimized surveys at detecting new prominent structures that change the apparent morphology of galaxies. The intrinsic limitations of deep imaging observations are also discussed, among those, the contamination of the stellar haloes of galaxies by extended ghost reflections, and the cirrus emission from Galactic dust. The detection and systematic census of fine structures that trace the present and past mass assembly of ETGs are one of the prime goals of the project. We provide specific examples of each type of observed structures – tidal tails, stellar streams and shells – and explain how they were identified and classified. We give an overview of the initial results. The detailed statistical analysis will be presented in future papers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.036 |
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".