HUNTING FOR YOUNG DISPERSING STAR CLUSTERS IN IC 2574
Bibliographic record
Abstract
Dissolving stellar groups are very difficult to detect using traditional surface photometry techniques. We have developed a method to find and characterize non-compact stellar systems in galaxies where the young stellar population can be spatially resolved. By carrying out photometry on individual stars, we are able to separate the luminous blue stellar population from the star field background. The locations of these stars are used to identify groups by applying the HOP algorithm, which are then characterized using color–magnitude and stellar density radial profiles to estimate age, size, density, and shape. We test the method on Hubble Space Telescope Advanced Camera for Surveys archival images of IC 2574 and find 75 dispersed stellar groups. Of these, 20 highly dispersed groups are good candidates for dissolving systems. We find few compact systems with evidence of dissolution, potentially indicating that star formation in this galaxy occurs mostly in unbound clusters or groups. These systems indicate that the dispersion rate of groups and clusters in IC 2574 is at most 0.45 pc Myr −1 . The location of the groups found with HOP correlate well with H i contour map features. However, they do not coincide with H i holes, suggesting that those holes were not created by star-forming regions.
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 | 0.001 | 0.001 |
| 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".