Deconstructing a galaxy: colour distributions of point sources in Messier 83
Bibliographic record
Abstract
What do we see when we look at a nearby, well-resolved galaxy? Thousands of individual sources are detected in multiband imaging observations of even a fraction of a nearby galaxy, and characterizing those sources is a complex process. This work analyses a ten-band photometric catalogue of nearly 70 000 point sources in a 7.3 square arcmin region of the nearby spiral galaxy Messier 83, made as part of the Early Release Science programme with the Hubble Space Telescope's Wide Field Camera 3. Colour distributions were measured for both broad-band and broad-and-narrow-band colours; colours made from broad-bands with large wavelength differences generally had broader distributions although B − V was an exception. Two- and three-dimensional colour spaces were generated using various combinations of four bands and clustered with the K-Means and Mean Shift algorithms. Neither algorithm was able to consistently segment the colour distributions: while some distinct features in colour space were apparent in visual examinations, these features were not compact or isolated enough to be recognized as clusters in colour space. K-Means clustering of the UBVI colour space was able to identify a group of objects more likely to be star clusters. Mean Shift was successful in identifying outlying groups at the edges of colour distributions. For identifying objects whose emission is dominated by spectral lines, there was no clear benefit from combining narrow-band photometry in multiple bands compared to a simple continuum subtraction. The clustering analysis results are used to inform recommendations for future surveys of nearby galaxies.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".