The little Galaxies that could (reionize the universe): predicting faint end slopes & escape fractions at z>4
Bibliographic record
Abstract
The sources that reionized the universe are still unknown, but likely candidates are faint but numerous galaxies. In this paper, we present results from running a high-resolution, uniform volume simulation, the Vulcan, to predict the number densities of undetectable, faint galaxies and their escape fractions of ionizing radiation, fesc, during reionization. Our approach combines a high spatial resolution, a realistic treatment of feedback and hydroprocesses, a strict threshold for minimum number of resolution elements per galaxy, and a converged measurement of fesc. We calibrate our physical model using a novel approach to create realistic galaxies at z = 0, so the simulation is predictive at high redshifts. With this approach, we can (1) robustly predict the evolution of the galaxy UV luminosity function at faint magnitudes down to MUV ∼ −15, two magnitudes fainter than observations, and (2) estimate fesc over a large range of galaxy masses based on the detailed stellar and gas distributions in resolved galaxies. We find steep faint end slopes, implying high number densities of faint galaxies, and the dependence of fesc on the UV magnitude of a galaxy, given by the power law: log fesc = (0.51 ± 0.04)MUV + 7.3 ± 0.8, with the faint population having fesc ∼ 35 per cent. Convolving the UV luminosity function with fesc(MUV), we find an ionizing emissivity that is (1) dominated by the faintest galaxies and (2) reionizes the universe at the appropriate rate, consistent with observational constraints of the ionizing emissivity and the optical depth to the decoupling surface τes, without the need for additional sources of ionizing radiation.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".