Galaxies OBserved as Low-luminosity Identified Nebulae (GOBLIN): Catalog of 43 000 high-probability dwarf galaxy candidates in the UNIONS survey
Bibliographic record
Abstract
The detection of low-surface-brightness galaxies beyond the Local Group poses significant observational challenges, yet these faint systems are fundamental to our understanding of dark matter, hierarchical galaxy formation, and cosmic structure. Their abundance and distribution provide crucial tests for cosmological models, particularly regarding the small-scale predictions of ΛCDM. We present a systematic detection and classification framework for unresolved dwarf galaxy candidates in the large-scale Ultraviolet Near Infrared Optical Northern Survey (UNIONS) imaging data. The main survey region covers 4861 deg2. Our pipeline preprocesses UNIONS data in three (gri) of the five bands (ugriz), including binning, artifact removal, and stellar masking before employing the software MTOBJECTS (MTO) to detect low-surface-brightness objects. Following a set of parameter cuts using known dwarf galaxies from the literature and cross-matching between the three bands, we were left with an average of ∼360 candidates per deg2. With ∼4000 deg2 in g, r and i, this amounts to ∼1.5 million candidates that form our GOBLIN (Galaxies OBserved as Low-luminosity Identified Nebulae) catalog. For the final classification of these candidates, we finetuned the deep learning model ZOOBOT, which was pretrained based on labels from the Galaxy Zoo project. We created our training dataset by visually inspecting dwarf galaxy candidates from existing literature catalogs within our survey area and assigning probability labels based on averaged expert assessments. This approach captures both consensus and uncertainty among experts. When applied to all detected MTO objects, our method identified 42 965 dwarf galaxy candidates with probability scores of >0.8, of which 23 072 have probabilities exceeding 0.9. The spatial distribution of high-probability candidates reveals a correlation with the locations of massive galaxies (log (M∗/M⊙)≥ 10) within 120 Mpc. While some of these objects may have been previously identified in other surveys, we present this extensive catalog of candidates, including their positions, structural parameter estimates, and classification probabilities, as a resource for the community to enable studies of galaxy formation, evolution, and the distribution of dwarf galaxies in different environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".