Star formation efficiency in bulges: an observational investigation of eight nearby galaxies with rich molecular gas but low star formation rate
Bibliographic record
Abstract
The physics that regulates star formation (SF) is fundamental to the building of our Universe. In the simplest scenario, stars form out of dense molecular clouds on the free-fall timescale. However, the star formation efficiency (SFE) has been shown to vary across different environments, particularly within galactic starbursts and deep within the bulges of galaxies. Galactic bulges and more massive elliptical galaxies share similar properties. They both have spheroidal distributions of stellar mass with high surface densities. Some bulges and elliptical galaxies have rich molecular gas reservoirs but scarce SF. This thesis aims to solve the mystery of suppressed SF in these gas rich spheroidal systems, using observations of eight galaxies that are either elliptical or have large bulges. The observations offer spatially-resolved measurements (~100 pc resolution) of warm ionised gas emission lines from the imaging Fourier transform spectrograph SITELLE at the Canada-France-Hawaii Telescope, and cold molecular gas from the Atacama Large Millimeter/sub-millimeter Array. I use the ionised gas emission lines to classify ionisation mechanisms and demonstrate the absence of SF regions in the elliptical galaxies and bulges. I then use the surface densities of molecular gas and SF rate to constrain the depletion times, which suggest suppressed SF in the elliptical galaxies and bulges. The radial profile of depletion times indicate an inside-out quenching of SF. I also study the gas dynamics of these galaxies, especially in two detailed case studies: NGC 3169 and NGC 524. I explore a number of SF quenching mechanisms, including bulge dynamics, turbulence and AGN feedback. I find that SF suppression in this sample of galaxies is dominated by galactic rotation induced shear
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".