JADES: The incidence rate and properties of galactic outflows in low-mass galaxies across 3 < z < 9
Bibliographic record
Abstract
We investigate the incidence and properties of ionized gas outflows in a sample of 52 galaxies with stellar mass between $10^7$ M$_{\odot}$ and $10^9$ M$_{\odot}$ observed with ultra-deep JWST/NIRSpec MSA spectroscopy as part of the JWST Advanced Deep Extragalactic Survey (JADES). The high-spectral resolution (R2700) NIRSpec observations allowed us to identify for the first time the signature of outflows in the rest-frame optical nebular lines in low-mass galaxies at $z>3$. The incidence fraction of ionized outflows, traced by broad components, is about 25-40$\%$ depending on the intensity of the emission lines. The low incidence fraction might be due to both the sensitivity limit and the fact that outflows are not isotropic but have a limited opening angle which results in a detection only when this is directed toward our line of sight. Evidence for outflows increases slightly with stellar mass and star-formation rate. The median velocity and mass loading factor (i.e., the ratio between mass outflow rate and star formation rate) of the outflowing ionized gas are 350 km s$^{-1}$ and $η=2.0^{+1.6}_{-1.5}$, respectively. These are 1.5 and 100 times higher, respectively than the typical values observed in local dwarf galaxies. These outflows are able to escape the gravitational potential of the galaxy and enrich the circum-galactic medium and, potentially, the inter-galactic medium. Our results indicate that outflows can significantly impact the star formation activity in low-mass galaxies within the first 2 Gyr of the Universe.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".