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Record W4221061514 · doi:10.1093/mnras/stac841

Refined modelling of the radio SZ signal: kinematic terms, relativistic temperature corrections, and anisotropies in the radio background

2022· article· en· W4221061514 on OpenAlexfundno aff
Elizabeth Lee, Jens Chluba, G. P. Holder

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsnot available
FundersH2020 European Research CouncilScience and Technology Facilities CouncilRoyal SocietyUniversity of ManchesterCanadian Institute for Advanced Research
KeywordsPhysicsCosmic microwave backgroundRadio frequencyAstrophysicsRedshiftCosmic background radiationRadio galaxyRelativistic quantum chemistryCluster (spacecraft)IsotropyAnisotropyGalaxyQuantum mechanics

Abstract

fetched live from OpenAlex

ABSTRACT A significant cosmological radio background will inevitably lead to a radio Sunyaev–Zeldovich (SZ) effect. In the simplest limit, the combined signal from the scattered radio and cosmic microwave background exhibits a null at around ν ≃ 735 MHz. Here, we show that kinematic and relativistic temperature corrections to this radio SZ signal are easily calculable. We treat both the cluster and observer motion, and the scattering of anisotropies in the radio background, highlighting how the spectrum of the radio SZ effect is affected in each case. Although relativistic temperature corrections only enter at the level of a few per cent, our expressions allow high-precision modelling of these terms. By measuring the SZ signal around the radio null, one is in principle able to place constraints on the properties of a cosmological radio background. A combination with standard SZ measurements from large cluster samples could provide a promising avenue towards breaking degeneracies between different contributions. Stacking analyses can reduce the effect of kinematic corrections and dipolar anisotropies in the radio background, thereby providing a way to constrain the redshift dependence of the average radio background. Our qualitative discussion is meant to give an analytic understanding of the various effects and also motivate further studies with the aim to obtain quantitative forecasts of their observability. At this stage, a detection of the corrections seems rather futuristic, but the advent of large SZ and X-ray cluster samples could drastically improve our ability to disentangle various effects.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.197
Teacher spread0.184 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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