Morphologies of galaxies within voids
Bibliographic record
Abstract
Context. Among the largest structures in which matter is distributed in the Universe, we find cosmic voids, which are large, under-dense regions almost devoid of galaxies. The study of these structures and the galaxies that inhabit them, the void galaxies, provides key information for understanding galaxy evolution. Aims. In this work we investigate the effects of the environment on the evolution of void galaxies. In particular, we study their morphology and explore its dependence on the location within the void where the galaxies reside, as well as on the properties of the void, such as its size and the galaxy number density. Methods. The sample of void galaxies that we use in this study is based on the catalogue of cosmic voids and void galaxies in the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). As we are interested in studying the morphology of void galaxies, we select galaxies in the redshift range of 0.005 ≤ z ≤ 0.080, and use the public galaxy morphologies of the SDSS sample together with deep learning algorithms to divide the sample into early- and late-type void galaxies. We analyse the fractions of galaxies of each morphological type as a function of the void-centric distance, the size of the voids, and the density of galaxies in each void. Results. There is a higher abundance of late-type galaxies with respect to early-type galaxies within voids, which remains nearly constant from the inner to the outer part of the voids. We do not find any dependence of the fraction of early- and late-type galaxies on void size or on the number-density of galaxies in the voids. Conclusions. Galaxies in voids follow the morphology–density relation, in the sense that the majority of the galaxies in voids (the most under-dense large-scale environments) are late-type galaxies. However, we find no difference between voids with lower or higher volume number-density of galaxies: the fractions of early- and late-type galaxies do not depend on the density of the voids. The physical processes responsible for the evolution from late towards earlier types (such as external environmental quenching) are not sufficiently effective in voids or are so slow (internal secular quenching) that their contributions do not appear in the morphology–density relation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".