The combined and respective roles of imaging and stellar kinematics in identifying galaxy merger remnants
Bibliographic record
Abstract
ABSTRACT One of the central challenges to establishing the role of mergers in galaxy evolution is the selection of pure and complete merger samples in observations. In particular, while large and reasonably pure interacting galaxy pair samples can be obtained with relative ease via spectroscopic criteria, automated selection of post-coalescence merger remnants is restricted to the physical characteristics of remnants alone. Furthermore, such selection has predominantly focused on imaging data – whereas kinematic data may offer a complimentary basis for identifying merger remnants. Therefore, we examine the theoretical utility of both the morphological and kinematic features of merger remnants in distinguishing galaxy merger remnants from other galaxies. Deep classification models are calibrated and evaluated using idealized synthetic images and line-of-sight stellar velocity maps of a heterogeneous population of galaxies and merger remnants from the TNG100 cosmological hydrodynamical simulation. We show that even idealized stellar kinematic data have limited utility compared to imaging and underperforms by $2.1 \pm 0.5 {{\ \rm per\ cent}}$ in completeness and $4.7 \pm 0.4 {{\ \rm per\ cent}}$ in purity for our fiducial model architecture. Combining imaging and stellar kinematics offers a small boost in completeness (by $1.8 \pm 0.4 {{\ \rm per\ cent}}$, compared to $92.7 \pm 0.2 {{\ \rm per\ cent}}$ from imaging alone) but no change in purity ($0.1\pm 0.3 {{\ \rm per\ cent}}$ improvement compared to $92.7 \pm 0.2 {{\ \rm per\ cent}}$, evaluated with equal numbers of merger remnant and non-remnant control galaxies). Classification accuracy of all models is particularly sensitive to physical companions at separations ≲ 40 kpc and to time-since-coalescence. Taken together, our results show that the stellar kinematic data have little to offer in compliment to imaging for merger remnant identification in a heterogeneous galaxy population.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".