TONGS: A Treasury of Nearby Galaxy Surveys
Bibliographic record
Abstract
Abstract The beginning of the 21st century marked the “modern era of galaxy surveys” in astronomy. Rapid innovation in observing technology, combined with the base built by galaxy catalogs and atlases dating back centuries, sparked an explosion of new observational programs driven by efforts to understand the different processes driving galaxy evolution. This review aims to answer the following science questions: (1) how have galaxy surveys evolved in the past 20 yr, and how have traditional observational programs been affected by the rise of large panoramic surveys, (2) can the term “nearby” be quantified in the context of galaxy surveys, and (3) how complete is the coverage of the nearby universe and what areas hold the largest opportunity for future work? We define a galaxy survey as a systematically obtained data set which aims to characterize a set of astronomical objects. Galaxy surveys can further be subdivided based on the methods used to select the objects to observe, the properties of the survey samples (e.g., distance or morphology), or the observing strategies used. We focus on pointed nearby galaxy surveys, which we define as surveys which observe a specific sample of target galaxies. Through a study of 43 nearby galaxy surveys, we find no standardized quantitative definition for “nearby” with surveys covering a wide range of distances. We observe that since 2003, traditional targeted galaxy surveys have undergone a dramatic evolution, transitioning from large, statistical surveys to small, ultra-specific projects which compliment the rise of large high resolution panoramic surveys. While wavelength regimes observable from the ground (such as radio or optical wavelengths) host numerous surveys, the largest opportunity for future work is within the less covered space-based wavelength regimes (especially ultraviolet and X-ray).
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".