Identifying Dwarfs of MC Analog GalaxiEs (ID-MAGE): The Search for Satellites around Low-mass Hosts
Bibliographic record
Abstract
Abstract We present results from Identifying Dwarfs of MC Analog GalaxiEs (ID-MAGE), a survey aimed at identifying and characterizing unresolved satellite galaxies around 35 nearby LMC- and SMC-mass hosts ( D = 4−10 Mpc). We use archival DESI Legacy Survey imaging data and perform an extensive search for dwarf satellites, extending out to a radius of 150 kpc (∼ R vir ). We identify 355 candidate satellite galaxies, including 264 new discoveries. Extensive tests with injected galaxies demonstrate that the survey is complete down to M V ∼ −9.0 (assuming the distance of the host) and μ 0, V ∼ 26 mag arcsec −2 (assuming an n = 1 Sérsic profile). We perform consistent photometry, via Sérsic profile fitting, on all candidates and have initiated a comprehensive follow-up campaign to confirm and characterize candidates. Through a systematic visual inspection campaign, we classify the top candidates as high-likelihood satellites. On average, we find 4.0 ± 1.4 high-likelihood candidate satellites per LMC-mass host and 2.1 ± 0.6 per SMC-mass host, which is within the range predicted by cosmological models. We use this sample to establish upper and lower estimates on the satellite luminosity function of LMC-/SMC-mass galaxies. ID-MAGE nearly triples the number of low-mass galaxies surveyed for satellites with well-characterized completeness limits, providing a unique data set to explore small-scale structure and dwarf galaxy evolution around low-mass hosts in diverse environments.
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 | 0.001 |
| 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) | 0.001 | 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".