Constraining blazar heating with the 2 ≲<i>z</i>≲ 3 Lyman-α forest
Bibliographic record
Abstract
ABSTRACT The intergalactic medium (IGM) acts like a calorimeter recording energy injection by cosmic structure formation, shocks and photoheating from stars and active galactic nuclei. It was recently proposed that spatially inhomogeneous TeV-blazars could significantly heat up the underdense IGM, resulting in patches of both cold and warm IGM around z ≃ 2−3. The goal of this study is to compare predictions of different blazar heating models with recent observations of the IGM. We perform a set of cosmological simulations and carefully compute mock observables of the Lyman-α (Lyα) forest. We perform a detailed assessment of different systematic uncertainties which typically impact this type of observables and find that they are smaller than the differences between our models. We find that our inhomogeneous blazar heating model is in good agreement with the Lyα line properties and the rescaled flux probability distribution function at high redshift (2.5 < z < 3) but that our blazar heating models are challenged by lower redshift data (2 < z < 2.5). Our results could be explained by HeII reionization although state-of-the-art models fall short on providing enough heating to the low-density IGM, thus motivating further radiative transfer studies of inhomogeneous HeII reionization. If blazars are indeed hosted by group-mass haloes of $2\times 10^{13}\, \rm {M}_\odot$, a later onset of blazar heating in comparison with previous models would be favoured, which could bring our findings here in agreement with the evidence of blazar heating from local gamma-ray observations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".