Pairing Optical and Atomic Hydrogen Detections to Assess Newly-Discovered Galaxy Candidates
Bibliographic record
Abstract
As optical telescopes become more advanced, their detections begin to unveil very faint, previously unobserved extragalactic objects in the night sky – some of which could be among the smallest and most diffuse galaxies ever discovered. Many of the objects detected appear to be comprised of young, blue stars which seem to exist in isolation from any parent galaxies. In this research, we attempt to connect the very faint optical detections with atomic hydrogen, HI, detections from archival surveys, in order to assess the likelihood that they are newly-discovered galaxies and whether they are actively forming stars. Using the Systematically Measuring Ultra-diffuse Galaxies (SMUDGes) catalog to generate a list of potential candidates, the HI profile corresponding to each candidate’s coordinates was generated and smoothed, ensuring the detection of subtle emissions. Out of the 48 candidates analyzed, 2 very promising candidates emerged and properties like the object’s recessional velocity and HI mass were determined. While the lack of emissions detected may point to radio surveys not being deep enough to detect subtle HI emissions, observing these successful candidates may allow for the formation, origins and evolution of these objects to be studied, thus expanding our knowledge of the universe and galaxy formations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.003 | 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".