Far-ultraviolet morphology of star-forming filaments in cool core brightest cluster galaxies
Bibliographic record
Abstract
We present a multiwavelength morphological analysis of star-forming clouds and filaments in the central ( 50 kpc) regions of 16 low-redshift (z < 0.3) cool core brightest cluster galaxies.New Hubble Space Telescope imaging of far-ultraviolet continuum emission from young ( 10 Myr), massive ( 5 M ) stars reveals filamentary and clumpy morphologies, which we quantify by means of structural indices.The FUV data are compared with X-ray, Lyα, narrowband Hα, broad-band optical/IR, and radio maps, providing a high spatial resolution atlas of star formation locales relative to the ambient hot (∼10 7-8 K) and warm ionized (∼10 4 K) gas phases, as well as the old stellar population and radio-bright active galactic nucleus (AGN) outflows.Nearly half of the sample possesses kpc-scale filaments that, in projection, extend towards and around radio lobes and/or X-ray cavities.These filaments may have been uplifted by the propagating jet or buoyant X-ray bubble, or may have formed in situ by cloud collapse at the interface of a radio lobe or rapid cooling in a cavity's compressed shell.The morphological diversity of nearly the entire FUV sample is reproduced by recent hydrodynamical simulations in which the AGN powers a self-regulating rain of thermally unstable star-forming clouds that precipitate from the hot atmosphere.In this model, precipitation triggers where the coolingto-free-fall time ratio is t cool /t ff ∼ 10.This condition is roughly met at the maximal projected FUV radius for more than half of our sample, and clustering about this ratio is stronger for sources with higher star formation rates.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".