Large-scale outflows from z≃ 0.7 starburst galaxies identified via ultrastrong Mg ii quasar absorption lines
Bibliographic record
Abstract
Star formation driven outflows are a critically important phenomenon in theoretical treatments of galaxy evolution, despite the limited ability of observational studies to trace galactic winds across cosmological time-scales. It has been suggested that the strongest Mg ii absorption-line systems detected in the spectra of background quasars might arise in outflows from foreground galaxies. If confirmed, such ‘ultrastrong’ Mg ii (USMg ii) absorbers would represent a method to identify significant numbers of galactic winds over a huge baseline in cosmic time, in a manner independent of the luminous properties of the galaxy. To this end, we present the first detailed imaging and spectroscopic study of the fields of two USMg ii absorber systems culled from a statistical absorber catalogue, with the goal of understanding the physical processes leading to the large velocity spreads that define such systems. Each field contains two bright emission-line galaxies at similar redshift (Δv≲ 300 km s−1) to that of the absorption. Lower limits on their instantaneous star formation rates (SFRs) from the observed [O ii] and Hβ line fluxes, and stellar masses from spectral template fitting indicate specific SFRs among the highest for their masses at these redshifts. Additionally, their 4000-Å break and Balmer absorption strengths imply they have undergone recent (∼0.01–1 Gyr) starbursts. The concomitant presence of two rare phenomena – starbursts and USMg ii absorbers – strongly implies a causal connection. We consider these data and USMg ii absorbers in general in the context of various popular models, and conclude that galactic outflows are generally necessary to account for the velocity extent of the absorption. We favour starburst-driven outflows over tidally stripped gas from a major interaction, which triggered the starburst as the energy source for the majority of systems. Finally, we discuss the implications of these results and speculate on the overall contribution of such systems to the global SFR density at z≃ 0.7.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".