A Physical Model of Lya Emitters
Bibliographic record
Abstract
We present a simple physical model for populating dark matter halos with Lya emitters (LAEs) and predict the properties of LAEs at z 3-7. The central tenet of this model is that the Lya luminosity is proportional to the star formation rate (SFR) which is directly related to the halo mass accretion rate. The only free parameter in our model is then the star formation efficiency (SFE). An efficiency of 2.5% provides the best fit to the Lya luminosity function (LF) at redshift z = 3.1, and we use this SFE to construct Lya LFs at other redshifts. Our model reproduces the Lya LFs, stellar ages, SFR 1-10 M sun yr1, stellar masses ~107to108 M sun, and the clustering properties of LAEs at z 3-7. We find the spatial correlation lengths ro 3-6 h 1 Mpc, in agreement with the observations. Finally, we estimate the field-to-field variation 30% for current volume and flux limited surveys, again consistent with observations. Our results suggest that the star formation, and hence Lya emission in LAEs can be powered by accretion of new material. Relating the accreted mass, rather than the total mass, to the Lya luminosity of LAEs naturally gives rise to their duty cycle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".