Investigating merging galaxies by using Pan-STARRS images
Bibliographic record
Abstract
Aims. We studied the r′-, z′-, and y′-band images of merging galaxies from the observations of the Panoramic Survey Telescope & Rapid Response System (Pan-STARRS). The merging galaxies were selected from our merging catalog that was created by checking the images of the Red-Sequence Cluster Survey 2 from the observations of the Canada France Hawaii Telescope. Methods. By using the homomorphic-aperture, we determined the photometric results of these merging systems. To obtain accurate photometry, we calibrated the Pan-STARRS r′-, z′-, and y′-band data to match the results of Sloan Digital Sky Survey Data Release 9. We also investigated the stellar masses of the merging galaxies by comparing the Wide-field Infrared Survey Explorer 3.4 μm emission with the calibrated y′-band data. Results. We present a catalog of the r′-, z′-, and y′-band photometric results for 4698 merging galaxies. For extended sources, our results suggest that the homomorphic-aperture method can obtain more reasonable results than the Desktop Virtual Observatory photometry. We derived new relations between the Pan-STARRS y′-band luminosities and the stellar masses of the merging galaxies. Our results show that the stellar masses of the merging galaxies range from 108 to 1013M⊙; some of the dry mergers could be as massive as 1013M⊙.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".