Atomic hydrogen in IllustrisTNG galaxies: the impact of environment parallelled with local 21-cm surveys
Bibliographic record
Abstract
We investigate the influence of environment on the cold-gas properties of galaxies at |$z$| = 0 within the TNG100 cosmological, magnetohydrodynamic simulation, part of the IllustrisTNG suite. We extend previous post-processing methods for breaking gas cells into their atomic and molecular phases, and build detailed mocks to comprehensively compare to the latest surveys of atomic hydrogen (H i) in nearby galaxies, namely ALFALFA and xGASS. We use TNG100 to explore the H i content, star formation activity, and angular momentum of satellite galaxies, each as a function of environment, and find that satellites are typically a factor of ≳3 poorer in H i than centrals of the same stellar mass, with the exact offset depending sensitively on parent halo mass. Due to the large physical scales on which H i measurements are made (∼45–245 kpc), contributions from gas not bound to the galaxy of interest but in the same line of sight crucially lead to larger H i mass measurements in the mocks in many cases, ultimately aligning with observations. This effect is mass-dependent and naturally greater for satellites than centrals, as satellites are never isolated by definition. We also show that H i stripping in TNG100 satellites is closely accompanied by quenching, in tension with observational data that instead favour that H i is preferentially stripped before star formation is reduced.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".