MAUVE: a 6 kpc bipolar outflow launched from NGC 4383, one of the most H <scp>i</scp>-rich galaxies in the Virgo cluster
Bibliographic record
Abstract
ABSTRACT Stellar feedback-driven outflows are important regulators of the gas–star formation cycle. However, resolving outflow physics requires high-resolution observations that can only be achieved in very nearby galaxies, making suitable targets rare. We present the first results from the new VLT/MUSE large programme MAUVE (MUSE and ALMA Unveiling the Virgo Environment), which aims to understand the gas–star formation cycle within the context of the Virgo cluster environment. Outflows are a key part of this cycle, and we focus on the peculiar galaxy NGC 4383, which hosts a $\sim\!\! 6\,$ kpc bipolar outflow fuelled by one of Virgo’s most H i-rich discs. The spectacular MUSE data reveal the clumpy structure and complex kinematics of the ionized gas in this M82-like outflow at 100 pc resolution. Using the ionized gas geometry and kinematics, we constrain the opening half-angle to θ = 25–35°, while the average outflow velocity is $\sim\!\! 210\ \text{km} \, \text{s}^{-1}$. The emission line ratios reveal an ionization structure where photoionization is the dominant excitation process. The outflowing gas shows a marginally elevated gas-phase oxygen abundance compared to the disc but lower than the central starburst, highlighting the contribution of mixing between the ejected and entrained gas. Making some assumptions about the outflow geometry, we estimate an integrated mass outflow rate of $\sim\!\! 1.8~\mathrm{M}_{\odot } \, \mathrm{yr}^{-1}$ and a corresponding mass-loading factor in the range of 1.7–2.3. NGC 4383 is a useful addition to the few nearby examples of well-resolved outflows, and will provide a useful baseline for quantifying the role of outflows within the Virgo cluster.
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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 | 0.000 | 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) | 0.002 | 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".