Submillimetre galaxies in cosmological hydrodynamical simulations – an opportunity for constraining feedback models
Bibliographic record
Abstract
ABSTRACT Submillimetre galaxies (SMGs) have long posed a challenge for theorists, and self-consistently reproducing the properties of the SMG population in a large-volume cosmological hydrodynamical simulation has not yet been achieved. We use a scaling relation derived from previous simulations plus radiative transfer calculations to predict the submm flux densities of simulated SMGs drawn from cosmological simulations from the Illustris and IllustrisTNG projects based on the simulated galaxies’ star formation rates (SFRs) and dust masses, and compare the predicted number counts with observations. We find that the predicted SMG number counts based on IllustrisTNG are significantly less than observed (more than 1 dex at S850 ≳ 4 mJy). The simulation from the original Illustris project yields more SMGs than IllustrisTNG: the predicted counts are consistent with those observed at both S850 ≲ 5 mJy and S850 ≳ 9 mJy and only a factor of ∼2 lower than those observed at intermediate flux densities. The redshift distribution of SMGs with S850 > 3 mJy in IllustrisTNG is consistent with the observed distribution, whereas the Illustris redshift distribution peaks at significantly lower redshift (1.5 versus 2.8). We demonstrate that IllustrisTNG hosts fewer SMGs than Illustris because in the former, high-mass ($M_{\star }\sim 10^{11} \, \text{M}_{\odot }$) z ∼ 2–3 galaxies have lower dust masses and SFRs than in Illustris owing to differences in the subgrid models for stellar and/or active galactic nucleus feedback between the two simulations (we unfortunately cannot isolate the specific cause(s) post hoc). Our results demonstrate that because our method enables predicting SMG number counts in post-processing with a negligible computational expense, SMGs can provide useful constraints for tuning subgrid models in future large-volume cosmological 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".