FIREbox: simulating galaxies at high dynamic range in a cosmological volume
Bibliographic record
Abstract
ABSTRACT We introduce a suite of cosmological volume simulations to study the evolution of galaxies as part of the Feedback in Realistic Environments project. FIREbox, the principal simulation of the present suite, provides a representative sample of galaxies (∼1000 galaxies with $M_{\rm star}\gt 10^8\, M_\odot$ at z = 0) at a resolution ($\Delta {}x\sim {}20\, {\rm pc}$ , $m_{\rm b}\sim {}6\times {}10^4\, M_\odot$ ) comparable to state-of-the-art galaxy zoom-in simulations. FIREbox captures the multiphase nature of the interstellar medium in a fully cosmological setting (L = 22.1 Mpc) thanks to its exceptionally high dynamic range (≳106) and the inclusion of multichannel stellar feedback. Here, we focus on validating the simulation predictions by comparing to observational data. We find that star formation rates, gas masses, and metallicities of simulated galaxies with $M_{\rm star}\lt 10^{10.5-11}\, M_\odot$ broadly agree with observations. These galaxy scaling relations extend to low masses ($M_{\rm star}\sim {}10^7\, M_\odot$ ) and follow a (broken) power-law relationship. Also reproduced are the evolution of the cosmic HI density and the HI column density distribution at z ∼ 0–5. At low z , FIREbox predicts a peak in the stellar-mass–halo-mass relation but also a higher abundance of massive galaxies and a higher cosmic star formation rate density than observed, showing that stellar feedback alone is insufficient to reproduce the properties of massive galaxies at late times. Given its high resolution and sample size, FIREbox offers a baseline prediction of galaxy formation theory in a ΛCDM Universe while also highlighting modelling challenges to be addressed in next-generation galaxy simulations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".