Automatic Approach to Morphological Classification of Galaxies with Analysis of Galaxy Populations in Clusters
Bibliographic record
Abstract
The classification of galaxies based on their morphology is a field in astrophysics that aims to understand galaxy formation and evolution based on their physical differences. Whether structural differences are due to internal factors or a result of local environment, the dominate mechanism that determines galaxy type needs to be robustly quantified in order to have a thorough grasp of the origin of the different types of galaxies. The main subject of my Ph.D. dissertation is to explore the use of computers to automatically classify and analyze large numbers of galaxies according to their morphology, and to analyze sub-samples of galaxies selected by type to understand galaxy formation in various environments. I have developed a computer code to classify galaxies by measuring five parameters from their images in FITS format. The code was trained and tested using visually classified SDSS galaxies from Galaxy Zoo and the EFIGI data set. I apply my morphology software to numerous galaxies from diverse data sets. Among the data analyzed are the 15 Abell galaxy clusters (0.03 < z < 0.184) from Rude et al. 2017 (in preparation), which were observed by the Canada-France-Hawaii Telescope. Additionally, I studied 57 galaxy clusters from Barkhouse et al. (2007), 77 clusters from the WINGS survey (Fasano et al. 2006), and the six Hubble Space Telescope (HST) Frontier Field galaxy clusters. The high resolution of HST allows me to compare distant clusters with those nearby to look for evolutionary changes in the galaxy cluster population. I use the results from the software to examine the properties (e.g. luminosity functions, radial dependencies, star formation rates) of selected galaxies. Due to the large amount of data that will be available from wide-area surveys in the future, the use of computer software to classify and analyze the morphology of galaxies will be extremely important in terms of efficiency. This research aims to contribute to the solution of this problem.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".