Molecular gas and star formation within 12 strong galactic bars observed with IRAM-30 m
Bibliographic record
Abstract
Context.While some galactic bars show recent massive star formation (SF) along them, some others do not. Whether bars with low level of SF are a consequence of low star formation efficiency, low gas inflow rate, or dynamical effects remains a matter of debate. Aims.In order to study the physical conditions that enable or prevent SF, we perform a multi-wavelength analysis of 12 strongly barred galaxies with total stellar masses log10(M⋆/M⊙)∈[10.2, 11], chosen to host different degrees of SF along the bar major axis without any prior condition on gas content. We observe the CO(1–0) and CO(2–1) emission within bars with the IRAM-30 m telescope (beam sizes of 1.7–3.9 kpc and 0.9–2.0 kpc, respectively; 7–8 pointings per galaxy on average). Methods.We estimated molecular gas masses (Mmol) from the CO(1–0) and CO(2–1) emissions. SF rates (SFRs) were calculated from GALEX near-ultraviolet (UV) and WISE 12 μm images within the beam-pointings, covering the full bar extent (SFRs were also derived from far-UV and 22 μm). Results.We detect molecular gas along the bars of all probed galaxies. Molecular gas and SFR surface densities span the ranges log10(Σmol/[M⊙pc−2]) ∈ [0.4,2.4] and log10(ΣSFR/[M⊙pc−1kpc−2]]) ∈ [−3.25, −0.75], respectively. The star formation efficiency (SFE; i.e., SFR/Mmol) in bars varies between galaxies by up to an order of magnitude (SFE ∈[0.1, 1.8] Gyr−1). On average, SFEs are roughly constant along bars. SFEs are not significantly different from the mean value in spiral galaxies reported in the literature (∼0.43 Gyr−1), regardless of whether we estimateMmolfrom CO(1–0) or CO(2–1). Interestingly, the higher the total stellar mass of the host galaxy, the lower the SFE within their bars. In particular, the two galaxies in our sample with the lowest SFE and ΣSFR(NGC 4548 and NGC 5850, SFE ≲ 0.25 Gyr−1, ΣSFR ≲ 10−2.25 M⊙yr−1kpc−2,M⋆ ≳ 1010.7 M⊙) are also those hosting massive bulges and signs of past interactions with nearby companions. Conclusions.We present a statistical analysis of the SFE in bars for a sample of 12 galaxies. The SFE in strong bars is not systematically inhibited (either in the central, middle, or end parts of the bar). Both environmental and internal quenching are likely responsible for the lowest SFEs reported in this work.
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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 | 0.000 | 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".