Dwarf Galaxy Discoveries from the KMTNet Supernova Program. I. The NGC 2784 Galaxy Group<sup>*</sup>
Bibliographic record
Abstract
Abstract We present BVI surface photometry of 31 dwarf galaxy candidates discovered in a deep image stack from the KMTNet Supernova Program of ∼30 square degrees centered on the nearby NGC 2784 galaxy group. Our final images have a 3σ surface brightness detection limit of mag arcsec−2. The faintest central surface brightness that we measure is mag arcsec−2. If these candidates are at the distance of NGC 2784, then they have absolute magnitudes greater than mag and effective radii larger than 170 pc. Their radial number density decreases exponentially with distance from the center of NGC 2784 until it flattens beyond a radius of 0.5 Mpc. We interpret the baseline density level to represent the background contamination and estimate that 22 of the 31 new candidates are dwarf members of the group. The candidate’s average color, , and Sérsic structural parameters are consistent with those parameters for the dwarf populations of other groups. We find that the central population of dwarfs is redder and brighter than the rest of the population. The measured faint-end slope of the luminosity function, , is steeper than that of the Local Group, but consistent with published results for other groups. Such comparisons are complicated by systematic differences among different studies, but will be simpler when the KMTNet survey, which will provide homogenous data for 15–20 groups, is completed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".