Modeling quasar proximity zones in a realistic cosmological environment with a self-consistent light curve
Bibliographic record
Abstract
ABSTRACT We study quasar proximity zones in a simulation that includes a self-consistent quasar formation model and realistic intergalactic medium (IGM) environments. The quasar host halo is 1013 M⊙ at z = 6, more massive than typical halos studied in previous work. Between 6 < z < 7.5, the quasar luminosity varies rapidly, with a mean magnitude of MUV, mean = −24.8 and the fluctuation reaching up to two orders of magnitude. Using this light curve to post-process the dense environment around the quasar, we find that the proximity zone size (Rp) ranges between 0.5 and 5 pMpc. We show that the light curve variability causes a similar degree of scatter in Rp as does the density fluctuation, both of which result in a standard deviation of ∼0.3 pMpc. The Rp traces the light curve fluctuations closely but with a time delay of ∼104 yr, breaking the correspondence between the Rp and the contemporaneous MUV. This also indicates that we can only infer quasar activity within the past ∼104 yr instead of the integrated lifetime from Rp in the later part of cosmic reionization. Compared with the variable light curve, a constant light curve underestimates the Rp by 13 per cent at the dim end (MUV ∼ −23.5), and overestimates the Rp by 30 per cent at the bright end (MUV ∼ −26). By calculating the Rp generated by a number of quasars, we show that variable light curves predict a wider Rp distribution than lightbulb models, and readily explain the extremely small Rp values that have been observed.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".