Dwarf galaxy discoveries from the KMTNet supernova programme – III. The Milky-Way analogue NGC 2997 group
Bibliographic record
Abstract
ABSTRACT We present the discovery of 48 new and the analysis of 55, including seven previously discovered dwarf galaxy candidates (DGCs) around the giant spiral galaxy NGC 2997 using deep BVI images from the Korea Microlensing Telescope Network Supernova Programme. Their V-band central surface brightness and total absolute magnitudes are in the range of 20.3–26.7 mag arcsec−2 and −(8.02–17.69) mag, respectively, while the I-band effective radii are 0.14–2.97 kpc. We obtain $\alpha \, \simeq$ −1.43 ± 0.02 for the faint-end slope of their luminosity function, comparable to previously measured values but shallower than theoretical predictions based on Λ cold dark matter models. The distance-independent distributions of their mass and colour suggest that the group could have recently accreted new massive members from the surrounding fields. The systematically bluer colours of the brighter members indicate younger stellar population and higher star formation activities in them, which appears to be consistent with similar findings from the SAGA or ELVES survey. We suggest that the massive and bluer dwarf galaxies in the group have experienced less environmental quenching due to their recent accretion, while environmental quenching has been more effective for the low-mass members. The interpretation of NGC 2997 being populationally young with recent accretion of massive members is also consistent with the overall morphological distribution of the dwarf galaxies showing a lack of morphologically evolved candidates but a plethora of irregularly shaped ones. Our detection rate of DGCs in the NGC 2997 group and their inferred star formation activities are comparable to those found in Milky Way analogue systems from recent surveys within the magnitude limit M$_{V}\, \lesssim$ −13 mag.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".