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Record W4390105224 · doi:10.1093/mnrasl/slad199

On the anisotropic distribution of clusters in the local Universe

2023· article· en· W4390105224 on OpenAlexaboutno aff
Matthieu Schaller

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society Letters · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersScience and Technology Facilities CouncilDurham University
KeywordsPhysicsSupercluster (genetic)AstrophysicsCold dark matterUniverseRedshiftAnisotropyDark matterMeasure (data warehouse)AstronomyGalaxyTheoretical physicsQuantum mechanicsDatabase

Abstract

fetched live from OpenAlex

ABSTRACT In his 2021 lecture to the Canadian Association of Physicists Congress, P.J.E. Peebles pointed out that the brightest extragalactic radio sources tend to be aligned with the plane of the de Vaucouleur Local Supercluster up to redshifts of z = 0.02 ($d_{\rm MW}\approx 85~\rm {Mpc}$). He then asked whether such an alignment of clusters is anomalous in the standard Lambda cold dark matter (ΛCDM) framework. In this letter, we employ an alternative, absolute orientation agnostic, measure of the anisotropy based on the inertia tensor axial ratio of these brightest sources and use a large cosmological simulation from the flamingo suite to measure how common such an alignment of structures is. We find that only 3.5 per cent of randomly selected regions display an anisotropy of their clusters more extreme than the one found in the local Universe’s radio data. This sets the region around the Milky Way as a 1.85σ outlier. Varying the selection parameters of the objects in the catalogue, we find that the clusters in the local Universe are never more than 2σ away from the simulations’ prediction for the same selection. We thus conclude that the reported anisotropy, whilst noteworthy, is not in tension with the ΛCDM paradigm.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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