The fates of the circumgalactic medium in the FIRE simulations
Bibliographic record
Abstract
ABSTRACT We analyse the different fates of the circumgalactic medium (CGM) in FIRE-2 cosmological simulations, focusing on the redshifts z = 0.25 and 2 representative of recent surveys. Our analysis includes 21 zoom-in simulations covering the halo mass range $M_{\rm h}(z=0) \sim 10^{10} \!-\! 10^{12} \rm {\,M}_\odot$. We analyse both where the gas ends up after first leaving the CGM (its ‘proximate’ fate) and its location at z = 0 (its ‘ultimate’ fate). Of the CGM at z = 2, about half is found in the ISM or stars of the central galaxy by z = 0 in Mh(z = 2) ∼ 5 × 1011 M⊙ haloes, but most of the CGM in lower mass haloes ends up in the intergalactic medium (IGM). This is so even though most of the CGM in Mh(z = 2) ∼ 5 × 1010 M⊙ haloes first accretes on to the central galaxy before being ejected into the IGM. On the other hand, most of the CGM mass at z = 0.25 remains in the CGM by z = 0 at all halo masses analysed. Of the CGM gas that subsequently accretes on to the central galaxy in the progenitors of Mh(z = 0) ∼ 1012 M⊙ haloes, most of it is cool (T ∼ 104 K) at z = 2 but hot (∼Tvir) at z ∼ 0.25, consistent with the expected transition from cold mode to hot mode accretion. Despite the transition in accretion mode, at both z = 0.25 and $2 \, {\gtrsim} 80{{\ \rm per\ cent}}$ of the cool gas in $M_{\rm h} \gtrsim 10^{11} \rm {M}_\odot$ haloes will accrete on to a galaxy. We find that the metallicity of CGM gas is typically a poor predictor of both its proximate and ultimate fates. This is because there is in general little correlation between the origin of CGM gas and its fate owing to substantial mixing while in the CGM.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".