Galaxy Mergers Moulding The Cgm
Bibliographic record
Abstract
Galaxies are surrounded by sizeable gas reservoirs which host a significant amount of metals: the circum-galactic medium (CGM). The CGM acts as a mediator between the galaxy and the extra-galactic medium. However, our understanding of how galaxy mergers, a major evolutionary transformation, impact the CGM remains deficient. We present a theoretical study of the effect of galaxy mergers on the CGM: We use hydrodynamical cosmological zoom-in simulations of a major merger selected from the Illustris project such that the z=0 descendant is a Milky Way-like galaxy, and then re-simulated at a 40 times higher mass resolution. We include post-processing ionization modelling. This work demonstrates the effect the merger has on the characteristic size of the CGM, its metallicity and the predicted covering fraction of various commonly observed gas-phase species, such as H I, C IV and O VI. We show that merger-induced outflows can increase the CGM metallicity by 0.2-0.3 dex within 0.5 Gyr post-merger. These effects last up to 6 Gyr post-merger. While the merger increases the total metal covering fractions by factors of 2-3, the covering fractions of commonly observed UV ions decrease due to the hard ionizing radiation from the active galactic nucleus. The case study of the single simulated major merger presented in this work demonstrates the significant impact that a galaxy interaction can have on the size, metallicity and observed column densities of the CGM (Hani et al. in prep).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".