Radiative stellar feedback in galaxy formation: Methods and physics
Bibliographic record
Abstract
ABSTRACT Radiative feedback (RFB) from stars plays a key role in galaxies, but remains poorly understood. We explore this using high-resolution, multifrequency radiation-hydrodynamics (RHD) simulations from the Feedback In Realistic Environments (FIRE) project. We study ultrafaint dwarf through Milky Way mass scales, including H+He photoionization; photoelectric, Lyman Werner, Compton, and dust heating; and single+multiple scattering radiation pressure (RP). We compare distinct numerical algorithms: ray-based LEBRON (exact when optically thin) and moments-based M1 (exact when optically thick). The most important RFB channels on galaxy scales are photoionization heating and single-scattering RP: in all galaxies, most ionizing/far-UV luminosity (∼1/2 of lifetime-integrated bolometric) is absorbed. In dwarfs, the most important effect is photoionization heating from the UV background suppressing accretion. In MW-mass galaxies, metagalactic backgrounds have negligible effects; but local photoionization and single-scattering RP contribute to regulating the galactic star formation efficiency and lowering central densities. Without some RFB (or other ‘rapid’ FB), resolved GMCs convert too-efficiently into stars, making galaxies dominated by hyperdense, bound star clusters. This makes star formation more violent and ‘bursty’ when SNe explode in these hyperclustered objects: thus, including RFB ‘smoothes’ SFHs. These conclusions are robust to RHD methods, but M1 produces somewhat stronger effects. Like in previous FIRE simulations, IR multiple-scattering is rare (negligible in dwarfs, $\sim 10{{\ \rm per\ cent}}$ of RP in massive galaxies): absorption occurs primarily in ‘normal’ GMCs with AV ∼ 1.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".