The first measurement of the quasar lifetime distribution
Bibliographic record
Abstract
ABSTRACT Understanding the growth of the supermassive black holes (SMBH) powering luminous quasars, their co-evolution with host galaxies, and impact on the surrounding intergalactic medium (IGM) depends sensitively on the duration of quasar accretion episodes. Unfortunately, this time-scale, known as the quasar lifetime, tQ, is still uncertain by orders of magnitude ($t_{\rm Q} \simeq 0.01\, {\rm Myr} - 1\, {\rm Gyr}$). However, the extent of the He ii Ly α proximity zones in the absorption spectra of zqso ∼ 3–4 quasars constitutes a unique probe, providing sensitivity to lifetimes up to ∼30 Myr. Our recent analysis of 22 archival Hubble Space Telescope He ii proximity zone spectra reveals a surprisingly broad range of emission time-scales, indicating that some quasars turned on ≲1 Myr ago, whereas others have been shining for ≳30 Myr. Determining the underlying quasar lifetime distribution (QLD) from proximity zone measurements is a challenging task owing to: (1) the limited sensitivity of individual measurements; (2) random sampling of the quasar light curves; (3) density fluctuations in the quasar environment; and (4) the inhomogeneous ionization state of He ii in a reionizing IGM. We combine a seminumerical He ii reionization model, hydrodynamical simulations post-processed with ionizing radiative transfer, and a novel statistical framework to infer the QLD from an ensemble of proximity zone measurements. Assuming a lognormal QLD, we infer a mean $\langle {\rm log}_{10}(t_{\rm Q} / {\rm Myr})\rangle = 0.22^{+0.22}_{-0.25}$ and standard deviation $\sigma _{{\rm log}_{10}t_{\rm Q}} = 0.80^{+0.37}_{-0.27}$. Our results allow us to estimate the probability of detecting very young quasars with tQ ≤ 0.1 Myr from their proximity zone sizes yielding $p ({\le}0.1\, {\rm Myr}) = 0.19^{+0.11}_{-0.09}$, which is broadly consistent with recent determination at z ∼ 6.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".