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Record W3172082234 · doi:10.1093/mnras/stab1612

The impact of the CMB on the evolution of high-<i>z</i> blazars

2021· article· en· W3172082234 on OpenAlexfundno aff
L. Ighina, A. Caccianiga, A. Moretti, S. Belladitta, R. Della Ceca, A Diana

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersInstitut sur la Nutrition et les Aliments FonctionnelsAmerican Sheep Industry Association
KeywordsBlazarCosmic microwave backgroundPhysicsAstrophysicsRedshiftLuminosityRadio galaxyPopulationAstronomySpitzer Space TelescopeTelescopeGalaxyGamma rayOptics

Abstract

fetched live from OpenAlex

ABSTRACT Different works have recently found an increase of the average X-ray-to-radio luminosity ratio with redshift in the blazar population. We evaluate here whether the inverse Compton interaction between the relativistic electrons within the jet and the photons of the cosmic microwave background (IC/CMB) can explain this trend. Moreover, we test whether the IC/CMB model can also be at the origin of the different space density evolutions found in X-ray and radio-selected blazar samples. By considering the best statistically complete samples of blazars selected in the radio or in the X-ray band and covering a large range of redshift (0.5 ≲ z ≲ 5.5), we evaluate the expected impact of the CMB on the observed X-ray emission on each sample and then we compare these predictions with the observations. We find that this model can satisfactorily explain both the observed trend of the X-ray-to-radio luminosity ratios with redshift and the different cosmological evolutions derived from the radio and X-ray band. Finally, we discuss how currently on-going X-ray missions, like extended ROentgen Survey with an Imaging Telescope Array, could help to further constrain the observed evolution at even higher redshifts (up to z ∼ 6–7).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.196
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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